The ever-evolving and contagious nature of the Coronavirus (COVID-19) has immobilized the world around us. As the daily number of infected cases increases, the containment of the spread of this virus is proving to be an overwhelming task. Healthcare facilities around the world are overburdened with an ominous responsibility to combat an ever-worsening scenario. To aid the healthcare system, Internet of Things (IoT) technology provides a better solution—tracing, testing of COVID patients efficiently is gaining rapid pace. This study discusses the role of IoT technology in healthcare during the SARS-CoV-2 pandemics. The study overviews different research, platforms, services, products where IoT is used to combat the COVID-19 pandemic. Further, we intelligently integrate IoT and healthcare for COVID-19 related applications. Again, we focus on a wide range of IoT applications in regards to SARS-CoV-2 tracing, testing, and treatment. Finally, we effectively consider further challenges, issues, and some direction regarding IoT in order to uplift the healthcare system during COVID-19 and future pandemics.
Citation: Anichur Rahman, Muaz Rahman, Dipanjali Kundu, Md Razaul Karim, Shahab S. Band, Mehdi Sookhak. Study on IoT for SARS-CoV-2 with healthcare: present and future perspective[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9697-9726. doi: 10.3934/mbe.2021475
| [1] | Elif Basaran, Gulali Aktas . The relationship of vitamin D levels with hemogram indices and metabolic parameters in patients with type 2 diabetes mellitus. AIMS Medical Science, 2024, 11(1): 47-57. doi: 10.3934/medsci.2024004 |
| [2] | Piero Pavone, Xena Giada Pappalardo, Riccardo Lubrano, Salvatore Savasta, Alberto Verrotti, Pasquale Parisi, Raffaele Falsaperla . 4q interstitial and terminal deletion: clinical features comparison in two unrelated children. AIMS Medical Science, 2023, 10(2): 130-140. doi: 10.3934/medsci.2023011 |
| [3] | Cassandra A DeMarshall, Abhirup Sarkar, Robert G Nagele . Serum Autoantibodies as Biomarkers for Parkinsons Disease: Background and Utility. AIMS Medical Science, 2015, 2(4): 316-327. doi: 10.3934/medsci.2015.4.316 |
| [4] | Hicham Rahmi, Ben Yamine Mallouki, Fatiha Chigr, Mohamed Najimi . The effects of smoking Haschich on blood parameters in young people from the Beni Mellal region Morocco. AIMS Medical Science, 2021, 8(4): 276-290. doi: 10.3934/medsci.2021023 |
| [5] | Antonio Conti, Massimo Alessio . Proteomics for Cerebrospinal Fluid Biomarker Identification in Parkinsons Disease: Methods and Critical Aspects. AIMS Medical Science, 2015, 2(1): 1-6. doi: 10.3934/medsci.2015.1.1 |
| [6] | Hong-Yi Chang, Hung-Wen Tsai, Chiao-Fang Teng, Lily Hui-Ching Wang, Wenya Huang, Ih-Jen Su . Ground glass hepatocytes provide targets for therapy or prevention of hepatitis B virus-related hepatocellular carcinoma. AIMS Medical Science, 2018, 5(2): 90-101. doi: 10.3934/medsci.2018.2.90 |
| [7] | Carman K.M. Ip, Jun Yin, Patrick K.S. Ng, Shiaw-Yih Lin, Gordon B. Mills . Genomic-Glycosylation Aberrations in Tumor Initiation, Progression and Management. AIMS Medical Science, 2016, 3(4): 386-416. doi: 10.3934/medsci.2016.4.386 |
| [8] | Ying Chen, Sok Kean Khoo, Richard Leach, Kai Wang . MTA3 Regulates Extravillous Trophoblast Invasion Through NuRD Complex. AIMS Medical Science, 2017, 4(1): 17-27. doi: 10.3934/medsci.2017.1.17 |
| [9] | Vanessa J. White, Ramesh C. Nayak . Re-circulating Phagocytes Loaded with CNS Debris: A Potential Marker of Neurodegeneration in Parkinsons Disease?. AIMS Medical Science, 2015, 2(1): 26-34. doi: 10.3934/medsci.2015.1.26 |
| [10] | K.E. Khalid, Hamdi Nouri Nsairat, Jingwu Z. Zhang . The Presence of Interleukin 18 Binding Protein Isoforms in Chinese Patients with Rheumatoid Arthritis. AIMS Medical Science, 2016, 3(1): 103-113. doi: 10.3934/medsci.2016.1.103 |
The ever-evolving and contagious nature of the Coronavirus (COVID-19) has immobilized the world around us. As the daily number of infected cases increases, the containment of the spread of this virus is proving to be an overwhelming task. Healthcare facilities around the world are overburdened with an ominous responsibility to combat an ever-worsening scenario. To aid the healthcare system, Internet of Things (IoT) technology provides a better solution—tracing, testing of COVID patients efficiently is gaining rapid pace. This study discusses the role of IoT technology in healthcare during the SARS-CoV-2 pandemics. The study overviews different research, platforms, services, products where IoT is used to combat the COVID-19 pandemic. Further, we intelligently integrate IoT and healthcare for COVID-19 related applications. Again, we focus on a wide range of IoT applications in regards to SARS-CoV-2 tracing, testing, and treatment. Finally, we effectively consider further challenges, issues, and some direction regarding IoT in order to uplift the healthcare system during COVID-19 and future pandemics.
Accumulation of fluids in the pleural cavity is classified into transudates and exudates. Transudate is usually composed of ultra filtrates of plasma and poor cellular content and protein concentration. On the other hand, in typical exudates, the cellular content is much greater than that of transudates and a greater variety of cells is usually present. These cells may be inflammatory or neoplastic, depending on the cause of the exudate. The concentration of protein is often greater than that found in transudate, i.e. < 3 g/dL, but not invariably so, as there is considerable overlap between the ranges [1,2]. The traditional classification of pleural effusion into transudates and exudates is still useful because it indicates the physiopathologic mechanism involved. More specifically, pleural exudates emerge due to alteration of hydrostatic and/or colloid osmotic pressures in plural capillaries, or as a result of fluid passing from the peritoneal cavity to the pleural cavity via a defect in the diaphragm or lymph vessels. The criteria proposed by Light et al. in 1972 are currently used to discriminate between transudates and exudates [3]. However, the distinction between transudates and exudates is not presently clear due to a possible overlap between the two. To date, different parameters have been applied to differentiate between the two conditions, such as pleural fluid protein level, pleural fluid cholesterol (CHOL), lactic dehydrogenase (LDH) level, and pleural fluid/serum protein ratio [4,5]. These approaches are unreliable, as they tend to result in misclassification of many effusions [6]. This study aimed to determine the diagnostic utility of CHOL, LDH, and protein ratios in the differentiation between transudate and exudate in pleuritis cases of different etiology and assess the relationship between pleural effusion and serum level of CHO, LDH, and protein.
This is a descriptive study aiming to evaluate the diagnostic role of the pleural effusion/serum (CHOL, LDH, and protein) ratios in the differentiation between exudate and transudate effusion. The study was conducted in Khartoum state hospitals, Sudan, during the period from May 2012 to May 2014.
Pleural effusion and serum samples were collected from 135 patients with definite diagnosis of having pleural effusion, all of whom were aged 18 years or older. All patients gave informed consent for participating in the study. The following evidence was used to include or exclude the cases [7].
1) Congestive heart failure: presence of clinical features (increased JVP, tachycardia, ventricular gallop) with cardiomegaly or echocardiac evidence of cardiac dysfunction.
2) Renal diseases: elevated urea (> 20 mmol/L) or creatinine > 167 μmol/L with or without signs or symptoms of fluid overload.
3) Malignancy: confirmed by cytology or histological proof of malignant tumor and in absence of all other conditions associated with pleural effusion.
4) Liver cirrhosis: positive ultrasonography or CT findings with clinical and lab evidence of hepatic derangements and portal hypertension.
5) Infective effusion: clear evidence of infection (positive microbiologic culture), elevated CRP or leukocytosis or positive sputum stain.
6) Hypoalbuminemia: serum albumin < 20 mg/L.
Pleural effusion and serum samples were collected from all patients. Thoracentesis was performed to collect pleural effusion, while serum was collected from the venous blood using a 5 mL syringe. CHOL, LDH, and protein levels were measured in both samples.
The CHOL level was measured using an enzymatic colorimetric method, whereby all cholesterol is freed from the lipoproteins and is subsequently oxidized. When the oxidized CHOL is formed, H2O2 is produced, which in turn reacts with the reagent 4-aminoantipyrine to form a red compound. The increase in absorbance at the 520 nm wavelength is proportional to the CHOL level (Cobas Integra system; Roche Diagnostic). LDH level was measured by UV spectrophotometry at 36 oC and 340 nm. In this process, LDH catalyses the oxidation of lactate to pyruvate in the presence of Nicotinamide Adenine Dinucleotide (NAD), which is subsequently reduced to NADH. The rate of NADH formation measured at 340 nm is directly proportional to the serum LDH activity (TECO DIAGNOSTICS). Protein level was measured by quantitative calorimetric (Burt) method, whereby protein forms a colored chelate complex with cupric ions (Cu2+) in an alkaline environment containing sodium potassium tartrate. The product yields a light blue to violet complex that absorbs light at 540 nm. One cupric ion forms a colored coordination complex with four to six peptide bonds in its vicinity. The intensity of the color produced is proportional to the amount of peptide bonds participating in the reaction (HiPer® Protein Estimation Teaching Kit (Quantitative) HimediaLaboratories TM).
The data collected as a part of the study was analyzed by using Statistical Package for Social Sciences (SPSS). Descriptive and other statistics test results were expressed in tables and figures.
In this study, the diagnostic utility of CHOL, LDH, and protein ratios in the differentiation between exudate and transudate pleural effusions was assessed. The study sample comprised of 135 patients aged > 20 (mean age 51), referred to different hospitals in the Khartoum State, in whom pleural effusion was clinically diagnosed. As shown in Table 1 and Figure 1, two groups of individuals were classified according to the diagnostic yields of their effusions. In 95 (70.4%) of study participants, exudative effusion was diagnosed, whereas the remaining 40 (29.6%) had transudate effusion.
| Eff. type | Frequency | Percent (%) |
| Exudate | 95 | 70.4% |
| Transudate | 40 | 29.6% |
| Total | 135 | 100.0% |
DownLoad: CSV
Figure 1. Distribution of the study population by effusion type.
Table 2 and Figure 2 show the distribution of the study population by effusion type and gender. As can be seen, both exudative and transudate effusion was more predominant in male patients, at 64 (67.4%) and 23 (56.5%), respectively.
| Male | Female | Total | ||||
| Eff. Type | No | % | No | % | No | % |
| Exudate | 64 | 67.4% | 31 | 32.6% | 95 | 70.4% |
| Transudate | 23 | 56.5% | 17 | 42.5% | 40 | 29.6% |
| Total | 87 | 64.4 | 48 | 45.6 | 135 | 100.0% |
DownLoad: CSV
Figure 2. Distribution of the study population by effusion type and gender.
The distribution of the study population by effusion type and age is shown in Table 3 and Figure 3. In patients with exudative effusion, the majority (48, or 50.5%) of the study population were in the ˃ 60 age group, followed by the 41-60, and 20-40 age groups, which comprised of 28 (29.5%) and 19 (20%) individuals, respectively. On the other hand, transudate effusion was the most prevalent in the 41-60 age group (18 or 45%), followed by the ˃ 60, and 20-40 age groups, which consisted of 12 (30%) and 10 (25%) patients, respectively.
| 20-40 | 41-60 | > 60 | Total | |||||
| Eff. type | No | % | No | % | No | % | No | % |
| Exudate | 19 | 20 % | 28 | 29.5 % | 48 | 50.5 % | 95 | 70.4 % |
| Transudate | 10 | 25 % | 18 | 45 % | 12 | 30 % | 40 | 29.6 % |
| Total | 29 | 21.5 % | 46 | 34 % | 60 | 44.4 % | 135 | 100 % |
DownLoad: CSV
Figure 3. Distribution of the study population by effusion type and age.
Table 4 and Figure 4, presents the study population distribution by effusion type and clinical diagnosis. As can be seen from the data, among patients with exudative effusion, tuberculosis was the most common cause, diagnosed in 52 (54.7%) individuals, followed by pneumonia (18, 18.9%), pulmonary cancer (10, 10.5%), pulmonary embolism (9, 9.5%), and rheumatoid disease (6, 6.3). However, at 22 (55%), hepatic cirrhosis was the most prevalent condition among patients with transudate effusion, followed by congestive heart failure, which affected 18 (45%) individuals.
| T.B | Pneu. | Pulm Ca. | Pulm emboli | Rheum Disease | Hepatic Cirrhosis | CHF | Total | |||||||||
| Eff. | No | % | No | % | No | % | No | % | No | % | No | % | No | % | No | % |
| Exud | 52 | 54.7% | 18 | 18.9% | 10 | 10.5% | 9 | 9.5% | 6 | 6.3% | 0 | 0% | 0 | 0% | 95 | 70.4% |
| Trans | 0 | 0 | 0 | 0% | 0 | 0% | 0 | 0% | 0 | 0% | 22 | 55% | 18 | 45% | 40 | 29.6% |
| Total | 52 | 38.5% | 18 | 13.3% | 10 | 7.4% | 9 | 6.7% | 6 | 4.4% | 22 | 16.3% | 18 | 14.8% | 135 | 100% |
DownLoad: CSV
Figure 4. Distribution of the study population by effusion type clinical diagnosis.
Table 5 shows the levels of CHOL, LDH, and protein in serum and effusion samples among the study population. The level of all studied parameters in both serum and effusion fluid was significantly higher in the patients in the exudative group compared to those affected by transudate effusion. The differences among the mean values of all parameters was statistically significant at p = 0.000.
| Parameter | Type of sample | Type of effusion | Mean ± SEM |
| CHOL | Pleural fluid | Exudate | 1.92 ± 0.75 mmol/L * |
| Transudate | 0.52 ± 0.28 mmol/L * | ||
| Serum | Exudate | 3.56 ± 0.84 mmol/L * | |
| Transudate | 1.63 ± 0.75 mmol/L * | ||
| Protein | Pleural fluid | Exudate | 3.56 ± 095 g/dL * |
| Transudate | 0.93 ± 0.35 g/dL * | ||
| Serum | Exudate | 5.25 ± 0.88 g/dL * | |
| Transudate | 1.55 ± 0.61 g/dL * | ||
| LDH | Pleural fluid | Exudate | 285 ± 25 IU * |
| Transudate | 160 ± 22 IU * | ||
| Serum | Exudate | 235 ± 45 IU * | |
| Transudate | 134 ± 18 IU * |
DownLoad: CSVPearson correlation between the CHOL, LDH, and protein levels in serum and effusion samples is shown in Table 6. As can be seen from the data, high correlation was observed between CHOL level in effusion samples and LDH level in serum samples with r = 0.867. Similarly, CHOL level in serum samples was correlated with LDH level in effusion samples, as was LDH level in serum samples and protein level in effusion samples (with r = 0.855 and r = 0.824, respectively). A relatively good association was observed between protein and CHOL levels in effusion samples, with r = 0.791. Similarly, Pearson correlation values indicated association between the CHOL, LDH, and protein ratios and clinical diagnosis. In particular, high correlation was observed between CHOL ratio and clinical diagnosis, with r = 0.971), as well as LDH and protein ratios (at r = 0.863 and r = 0.597, respectively).
| Parameters | CHOL. Seru. | CHOL. Eff. | LDH Seru. | LDH Eff. | Prot. Seru. | Prot. Eff. | ||||||
| r | sig | r | sig | r | sig | r | sig | r | sig | r | sig | |
| CHOL.Seru. | 1 | 0.777** | 0.000 | 0.673** | 0.000 | 0.855** | 0.000 | 0.529** | 0.000 | 0.532** | 0.000 | |
| CHOL. Eff. | 0.777** | 0.000 | 1 | 0.867** | 0.000 | 0.667** | 0.000 | 0.061 | 0.000 | 0.791** | 0.000 | |
| LDH Seru. | 0.673** | 0.000 | 0.867** | 0.000 | 1 | 0.761** | 0.000 | 0.207** | 0.005 | 0.824** | 0.000 | |
| LDH Eff. | 0.855** | 0.000 | 0.667** | 0.000 | 0.761** | 0.000 | 1 | 0.690** | 0.000 | 0.630** | 0.000 | |
| Prot. Seru. | 0.529** | 0.000 | 0.061** | 0.000 | 0.207** | 0.005 | 0.690** | 0.000 | 1 | 0.699** | 0.000 | |
| Prot. Eff. | 0.532** | 0.000 | 0.791** | 0.000 | .824** | 0.000 | 0.630** | 0.000 | 0.699** | 0.000 | 1 | |
| ** Correlation is significant at the 0.01 level (2-tailed). | ||||||||||||
DownLoad: CSVTable 7 shows the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the CHOL, LDH, and protein ratios in differentiating between exudates and transudates. The highest values of these indicators pertained to the CHOL ratio, which showed 97.7% sensitivity, 100% specificity, 100% positive predictive value, and 95% negative predictive value. This was followed by the LDH ratio, with 86% sensitivity, 97.4% specificity, 97.4% positive predictive value, and 97% negative predictive value, and finally protein ratio, with 81.4% sensitivity, 81.6% specificity, 89.7% positive predictive value, and 70.4 % negative predictive value.
| Parameters | Sensitivity | Specificity | PPV | NPV | p value |
| CHOL ratio | 97.7% | 100.0% | 100.0% | 95.0% | < 0.0001 |
| Protein ratio | 81.4% | 81.6% | 89.7% | 70.4% | < 0.0001 |
| LDH ratio | 86.0% | 97.4% | 97.4% | 79.0% | < 0.0001 |
DownLoad: CSVThis study assessed the diagnostic utility of CHOL, LDH, and protein ratios in differentiating between exudative and transudate pleural effusion of different etiologies. The study sample comprised of 135 patients with accumulated pleural effusion, of whom 93 (68.9%) were diagnosed with exudates and 42 (31.1%) with transudates. Subsequent analyses revealed that exudate effusion was more prevalent in males (64, 67.4%) and among patients aged ˃ 60 years old (48, 50.5%). Tuberculosis was the most frequent cause of pleural effusion, with 52 (38.5%) cases, followed by hepatic cirrhosis (22, 16.3%), which is in line with the findings reported by Liam et al. [8], who suggested that the most common cause of exudative effusion was tuberculosis. The same results were also obtained by Kalaajieh [9], who concurred that tuberculosis is the most frequent cause of exudative pleural effusions.
Cholesterol CHOL ratio identified exudative effusion with a sensitivity of 97.7% and specificity of 100%, whereas LDH ratio had a sensitivity of 86.0% and specificity of 97.4%. Finally, protein ratio achieved sensitivity and specificity of 81.4% and 81.6%, respectively. These results are consistent with those reported by Wilcox et al. [10], who concluded that Light's criteria, cholesterol and pleural fluid LDH levels, and the pleural fluid cholesterol-to-serum ratio are the most accurate diagnostic indicators for pleural exudates. Sánchez et al. [11] reported similar results and concluded that the cholesterol in pleural liquid/cholesterol in serum quotient was the most productive and useful parameter (with 96% sensitivity and 97% specificity).
Pearson correlation test of the CHOL, LDH and protein ratio yielded 0.971, 0.863, and 0.597 values, respectively. This result suggests that CHOL ratio is more highly correlated than LDH and protein ratios with the clinical diagnosis for exudates, which is significant at the 0.01 level. These results are similar to those obtained by Hamal et al. [12], who suggested that CHOL ratio is more highly correlated than protein ratio with the clinical diagnosis for exudate. Jiménez et al. [13] also reported that the highest diagnostic yield was observed with the combination of pleural cholesterol, pleural LDH, and the pleural fluid/serum protein ratio.
Based on the present study findings and a review of extant work in this field, it can be concluded that:
1. Analysis of the CHOL ratio in serum and effusion samples is the best diagnostic tool in the differentiation between exudative and transudate pleural effusions.
2. Tuberculosis is most frequently observed as a causative agent of exudative pleural effusion.
3. CHOL level in serum and effusion samples has a better sensitivity, specificity and positive predictive value in differentiating exudates and transudates than do LDH and protein levels in the same samples.
I am deeply indebted to all who supported me and gave me advice during this study. A great debt of gratitude is owed to all hospital technicians for their assistance in sample collection and preparation. Finally, I would like to thank all my colleagues and friends for their encouragement and support during the preparation of this study.
| [1] |
T. Singhal, A review of coronavirus disease-2019 (COVID-19), Indian J. Pediatr., 87 (2020), 281–286. doi: 10.1007/s12098-020-03263-6
|
| [2] |
N. Chen, M. Zhou, X. Dong, J. Qu, F. Gong, Y. Han, et al., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, Lancet, 395 (2020), 507–513. doi: 10.1016/S0140-6736(20)30211-7
|
| [3] |
I. Valverde, Y. Singh, J. Sanchez-de Toledo, P. Theocharis, A. Chikermane, S. Di Filippo, et al., Acute cardiovascular manifestations in 286 children with multisystem inflammatory syndrome associated with COVID-19 infection in Europe, Circulation, 143 (2021), 21–32. doi: 10.1161/CIRCULATIONAHA.120.050065
|
| [4] | J. Riou, C. L. Althaus, Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020, Eurosurveillance, 25 (2020), 2000058. |
| [5] | L. Yuan, N. Zhi, C. Yu, G. Ming, L. Yingle, G. N. Kumar, et al., Aerodynamic characteristics and RNA concentration of SARS-CoV-2 aerosol in Wuhan hospitals during COVID-19 outbreak, BioRxiv, 2020. |
| [6] |
N. Van Doremalen, T. Bushmaker, D. H. Morris, M. G. Holbrook, A. Gamble, B. N. Williamson, et al., Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1, N. Engl. J Med., 382 (2020), 1564–1567. doi: 10.1056/NEJMc2004973
|
| [7] |
J. Hellewell, S. Abbott, A. Gimma, N. I. Bosse, C. I. Jarvis, T. W. Russell, et al., Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts, Lancet Global Health, 8 (2020), e488–e496. doi: 10.1016/S2214-109X(20)30074-7
|
| [8] | M. Lotfi, M. R. Hamblin, N. Rezaei, COVID-19: transmission, prevention, and potential therapeutic opportunities, Clin. Chim. Acta, 2020. |
| [9] |
R. P. Singh, M. Javaid, A. Haleem, R. Suman, Internet of things (IoT) applications to fight against COVID-19 pandemic, Diabetes Metab. Syndr. Clin. Res. Rev., 14 (2020), 521–524. doi: 10.1016/j.dsx.2020.04.041
|
| [10] | M. Hasan, A. Rahman, M. J. Islam, Distb-cvs: a distributed secure blockchain based online certificate verification system from bangladesh perspective, in 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), (2020), 460–465. |
| [11] | D. Abid Haleem, M. Javaid, I. H. Khan, B. Tech, Internet of things (IoT) applications in orthopaedics, 2019. |
| [12] | R. Jiloha, COVID-19 and mental health, Epidemiol. Int. (E-ISSN: 2455-7048), 5 (2020), 7–9. |
| [13] |
A. Haleem, M. Javaid, R. Vaishya, S. Deshmukh, Areas of academic research with the impact of COVID-19, Am. J. Emerg. Med., 38 (2020), 1524–1526. doi: 10.1016/j.ajem.2020.04.022
|
| [14] | K. M. S. Azad, N. Hossain, M. J. Islam, A. Rahman, S. Kabir, Preventive determination and avoidance of ddos attack with sdn over the iot networks. in International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI 2021), IEEE, 2021. |
| [15] |
A. Rahman, M. K. Nasir, Z. Rahman, A. Mosavi, S. Shahab, B. Minaei-Bidgoli, Distblockbuilding: a distributed blockchain-based sdn-iot network for smart building management, IEEE Access, 8 (2020), 140008–140018. doi: 10.1109/ACCESS.2020.3012435
|
| [16] | M. Mohammed, H. Syamsudin, S. Al-Zubaidi, R. R. AKS, E. Yusuf, Novel COVID-19 detection and diagnosis system using iot based smart helmet, Int. J. Psychosoc. Rehabil., 24 (2020), 2296–2303. |
| [17] | D. Darma, Z. Ilmi, S. Darma, Y. Syaharuddin, COVID-19 and its impact on education: challenges from industry 4.0, 2020. |
| [18] | Z. Ilmi, D. C. Darma, M. Azis, Independence in learning, education management, and industry 4.0: habitat indonesia during COVID-19, J. Anthropol. Sport Phys. Educ., 4 (2020), 63–66. |
| [19] |
K. Kumar, N. Kumar, R. Shah, Role of IoT to avoid spreading of COVID-19, Int. J. Intell. Networks, 1 (2020), 32–35. doi: 10.1016/j.ijin.2020.05.002
|
| [20] |
K. Farsalinos, K. Poulas, D. Kouretas, A. Vantarakis, M. Leotsinidis, D. Kouvelas, et al., Improved strategies to counter the COVID-19 pandemic: lockdowns vs. primary and community healthcare, Toxicol. Rep., 8 (2021), 1–9. doi: 10.1016/j.toxrep.2020.12.001
|
| [21] | Centers for Disease Control and Prevention, Coronavirus Disease 2019: COVID-19, 2020. Available from: https://www.cdc.gov/coronavirus/2019-ncov/index.html. |
| [22] |
L. H. Nguyen, D. A. Drew, M. S. Graham, A. D. Joshi, C. G. Guo, W. Ma, et al., Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study, Lancet Public Health, 5 (2020), e475–e483. doi: 10.1016/S2468-2667(20)30164-X
|
| [23] | A. Haleem, M. Javaid, Medical 4.0 and its role in healthcare during COVID-19 pandemic: a review, J. Ind. Integr. Manage., 5 (2020). |
| [24] |
A. Celesti, M. Fazio, F. Galán Márquez, A. Glikson, H. Mauwa, A. Bagula, How to develop IoT cloud e-health systems based on fiware: a lesson learnt, J. Sensor Actuator Networks, 8 (2019), 7. doi: 10.3390/jsan8010007
|
| [25] | S. Debdas, C. K. Panigrahi, P. Kundu, S. Kundu, R. Jha, IoT application in interconnected hospitals, Mach. Learn. Healthcare Appl., (2021), 227. |
| [26] |
A. Albahri, J. K. Alwan, Z. K. Taha, S. F. Ismail, R. A. Hamid, A. Zaidan, et al., IoT-based telemedicine for disease prevention and health promotion: state-of-the-art, J. Network Comput. Appl., 173 (2021), 102873. doi: 10.1016/j.jnca.2020.102873
|
| [27] | M. Shahroz, F. Ahmad, M. S. Younis, N. Ahmad, M. N. K. Boulos, R. Vinuesa, et al., COVID-19 digital contact tracing applications and techniques: a review post initial deployments, preprint, arXiv: 2103.01766. |
| [28] | S. Mohapatra, S. Mohanty, S. Mohanty, Smart healthcare: an approach for ubiquitous healthcare management using IoT, in Big Data Analytics for Intelligent Healthcare Management, Elsevier, (2019), 175–196. |
| [29] |
A. Rahman, M. J. Islam, Z. Rahman, M. M. Reza, A. Anwar, M. P. Mahmud, et al., Distb-condo: distributed blockchain-based IoT-sdn model for smart condominium, IEEE Access, 8 (2020), 209 594–209 609. doi: 10.1109/ACCESS.2020.3039113
|
| [30] |
K. N. Swaroop, K. Chandu, R. Gorrepotu, S. Deb, A health monitoring system for vital signs using IoT, Internet Things, 5 (2019), 116–129. doi: 10.1016/j.iot.2019.01.004
|
| [31] | A. Zamanifar, Remote patient monitoring: health status detection and prediction in IoT-based health care, in IoT in Healthcare and Ambient Assisted Living, Springer, (2021), 89–102. |
| [32] | A. Rahman, M. J. Islam, M. Saikat Islam Khan, S. Kabir, A. I. Pritom, M. Razaul Karim, Block-sdotcloud: enhancing security of cloud storage through blockchain-based sdn in IoT network, in 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), (2020), 1–6. |
| [33] |
M. R. Valanarasu, Smart and secure IoT and AI integration framework for hospital environment, J. ISMAC, 1 (2019), 172–179. doi: 10.36548/jismac.2019.3.004
|
| [34] |
A. Alamri, Ontology middleware for integration of IoT healthcare information systems in ehr systems, Computers, 7 (2018), 51. doi: 10.3390/computers7040051
|
| [35] |
T. Wu, F. Wu, C. Qiu, J. M. Redouté, M. R. Yuce, A rigid-flex wearable health monitoring sensor patch for IoT-connected healthcare applications, IEEE Internet Things J., 7 (2020), 6932–6945. doi: 10.1109/JIOT.2020.2977164
|
| [36] |
D. Zhang, X. Xia, Y. Yang, P. Yang, C. Xie, M. Cui, et al., A novel word similarity measure method for IoT-enabled healthcare applications, Future Gener. Comput. Syst., 114 (2021), 209–218. doi: 10.1016/j.future.2020.07.053
|
| [37] | S. Selvaraj, S. Sundaravaradhan, Challenges and opportunities in IoT healthcare systems: a systematic review, SN Appl. Sci., 2 (2020), 1–8. |
| [38] |
N. Gupta, S. Gupta, M. Khosravy, N. Dey, N. Joshi, R. G. Crespo, et al., Economic iot strategy: the future technology for health monitoring and diagnostic of agriculture vehicles, J. Int. Manuf., 32 (2021), 1117–1128. doi: 10.1007/s10845-020-01610-0
|
| [39] | P. P. Ray, B. Chowhan, N. Kumar, A. Almogren, Biothr: electronic health record servicing scheme in IoT-blockchain ecosystem, IEEE Internet Things J., 2021. |
| [40] |
M. Javaid, I. H. Khan, Internet of things (IoT) enabled healthcare helps to take the challenges of COVID-19 pandemic, J. Oral Biol. Craniofacial Res., 11 (2021), 209–214. doi: 10.1016/j.jobcr.2021.01.015
|
| [41] |
I. de Morais Barroca Filho, G. Aquino, R. S. Malaquias, G. Girão, S. R. M. Melo, An IoT-based healthcare platform for patients in ICU beds during the COVID-19 outbreak, IEEE Access, 9 (2021), 27262–27277. doi: 10.1109/ACCESS.2021.3058448
|
| [42] |
A. Islam, S.Y. Shin, A blockchain-based secure healthcare scheme with the assistance of unmanned aerial vehicle in Internet of Things, Comput. Electr. Eng., 84 (2020), 106627. doi: 10.1016/j.compeleceng.2020.106627
|
| [43] | A. Islam, T. Rahim, M. D. Masuduzzaman, S. Y. Shin, A blockchain-based artificial intelligence-empowered contagious pandemic situation supervision scheme using internet of drone things. IEEE Wireless Commun., 2021. |
| [44] |
M. Elhoseny, G. Ramírez-González, O. M. Abu-Elnasr, S. A. Shawkat, N. Arunkumar, A. Farouk, Secure medical data transmission model for IoT-based healthcare systems, IEEE Access, 6 (2018), 20 596–20 608. doi: 10.1109/ACCESS.2018.2817615
|
| [45] | S. Pirbhulal, N. Pombo, V. Felizardo, N. Garcia, A. H. Sodhro, S. C. Mukhopadhyay, Towards machine learning enabled security framework for IoT-based healthcare, in 2019 13th International Conference on Sensing Technology (ICST), IEEE, (2019), 1–6. |
| [46] | S. Saha, A. K. Sutrala, A. K. Das, N. Kumar, J. J. Rodrigues, On the design of blockchain-based access control protocol for IoT-enabled healthcare applications, in ICC 2020-2020 IEEE International Conference on Communications (ICC), IEEE, 2020, 1–6. |
| [47] |
S. S. Sahoo, S. Mohanty, B. Majhi, A secure three factor based authentication scheme for health care systems using IoT enabled devices, J. Ambient Intell. Humanized Comput., 12 (2021), 1419–1434. doi: 10.1007/s12652-020-02213-6
|
| [48] |
A. Hussain, T. Ali, F. Althobiani, U. Draz, M. Irfan, S. Yasin, et al., Security framework for IoT based real-time health applications, Electronics, 10 (2021), 719. doi: 10.3390/electronics10060719
|
| [49] | World Health Organization, 2020. Digital technology for COVID-19 response, Available from: https://www.who.int/news/item/03-04-2020-digital-technology-for-covid-19-response. |
| [50] |
A. Gatouillat, Y. Badr, B. Massot, E. Sejdić, Internet of medical things: a review of recent contributions dealing with cyber-physical systems in medicine, IEEE Internet Things J., 5 (2018), 3810–3822. doi: 10.1109/JIOT.2018.2849014
|
| [51] | L. Wynants, B. Van Calster, G. S. Collins, R. D. Riley, G. Heinze, E. Schuit, et al., Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal, BMJ, 369 (2020). |
| [52] | L. Yuan, W. Yeung, L. Celi, Urban intelligence for pandemic response, JMIR Public Health Surveill., 2020. |
| [53] |
V. Chamola, V. Hassija, V. Gupta, M. Guizani, A comprehensive review of the COVID-19 pandemic and the role of IoT, Drones, AI, Blockchain, and 5G in managing its impact, IEEE Access, 8 (2020), 90 225–90 265. doi: 10.1109/ACCESS.2020.2992341
|
| [54] | Q. V. Pham, D. C. Nguyen, T. Huynh-The, W. J. Hwang, P. N. Pathirana, Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: a survey on the state-of-the-arts, 2020. |
| [55] |
A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem, A. Mohammadi, Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: eesults of 10 convolutional neural networks, Comput. Biol. Med., 121 (2020), 103795. doi: 10.1016/j.compbiomed.2020.103795
|
| [56] |
M. A. Elaziz, K. M. Hosny, A. Salah, M. M. Darwish, S. Lu, A. T. Sahlol, New machine learning method for image-based diagnosis of COVID-19, Plos One, 15 (2020), e0235187. doi: 10.1371/journal.pone.0235187
|
| [57] |
A. Imran, I. Posokhova, H. N. Qureshi, U. Masood, M. S. Riaz, K. Ali, et al., AI4covid-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app, Inf. Med. Unlocked, 20 (2020), 100378. doi: 10.1016/j.imu.2020.100378
|
| [58] | I. Ahmed, A. Ahmad, G. Jeon, An IoT based deep learning framework for early assessment of COVID-19, IEEE Internet Things J., 2020. |
| [59] | Autonomous robot performs COVID-19 nasal swab tests, 2020. Available from: https://www.hospimedica.com/health-it/articles/294783922/autonomous-robot-performs-covid-19-nasal-swab-tests.html. |
| [60] | M. Nasajpour, S. Pouriyeh, R. M. Parizi, M. Dorodchi, M. Valero, H. R. Arabnia, Internet of Things for current COVID-19 and future pandemics: an exploratory study, J. Healthcare Inf. Res., (2020), 1–40. |
| [61] | Visionstate ships first IoT buttons for rapid response to cleaning alerts, 2020. Available from: https://www.globenewswire.com/news-release/2020/03/23/2004645/0/en/Visionstate-Ships-First-IoT-Buttons-for-Rapid-Response-to-Cleaning-Alerts.html. |
| [62] | S. Obeidat, How artificial intelligence is helping fight the COVID-19 pandemic, Entrepreneur Middle East, 2020. |
| [63] | M. Schmitt, How to fight COVID-19 with machine learning, 2020. Available from: https://www.datarevenue.com/en-blog/machine-learning-covid-19. |
| [64] | E. Strickland, AI can help hospitals triage COVID-19 patients, IEEE Spectrum, 2020. Available from: https://spectrum.ieee.org/the-human-os/artificial-intelligence/medical-ai/ai-can-help-hospitals-triage-covid19-patients. |
| [65] | Kinsa is an early warning system to detect and respond to contagious illness, 2020. Available from: https://kinsahealth.com/. |
| [66] |
T. Tamura, M. Huang, T. Togawa, Current developments in wearable thermometers, Adv. Biomed. Eng., 7 (2018), 88–99. doi: 10.14326/abe.7.88
|
| [67] | P. Vaishnavi, J. Agnishwar, K. Padmanathan, S. Umashankar, T. Preethika, S. Annapoorani, et al., Artificial intelligence and drones to combat COVID-19, Preprints, 2020. |
| [68] | Media Centre, Working on pandemic drone to detect corona virus, 2020. Available from: https://www.suasnews.com/2020/03/unisa-working-on-pandemic-drone-to-detect-coronavirus/. |
| [69] | M. Mohammed, N. A. Hazairin, S. Al-Zubaidi, S. A. Karim, S. Mustapha, E. Yusuf, Toward a novel design for coronavirus detection and diagnosis system using IoT based drone technology, Int. J. Psychosoc. Rehabil., 24 (2020), 2287–2295. |
| [70] | M. Mohammed, N. A. Hazairin, H. Syamsudin, S. Al-Zubaidi, A. Sairah, S. Mustapha, et al., 2019 novel coronavirus disease (COVID-19): detection and diagnosis system using IoT based smart glasses, Int. J. Adv. Sci. Technol., 29 (2020). |
| [71] |
G. Quer, J. M. Radin, M. Gadaleta, K. Baca-Motes, L. Ariniello, E. Ramos, et al., Wearable sensor data and self-reported symptoms for COVID-19 detection, Nat. Med., 27 (2021), 73–77. doi: 10.1038/s41591-020-1123-x
|
| [72] | Estimote wearables track workers to curb COVID-19 outbreak, 2020. Available from: https://www.slashgear.com/estimote-wearables-track-workers-to-curb-covid-19-outbreak-02615366/. |
| [73] | T. Hornyak, What America can learn from China's use of robots and telemedicine to combat the coronavirus, Tech. Drivers, 2020. |
| [74] | M. Hollister, AI can help with the COVID-19 crisis-but the right human input is key, in World Economic Forum, 30 (2020). |
| [75] | R. K. R. Kummitha, Smart technologies for fighting pandemics: the techno-and human-driven approaches in controlling the virus transmission, Gov. Inf. Q., (2020), 101481. |
| [76] | D. DeCaprio, J. Gartner, T. Burgess, K. Garcia, S. Kothari, S. Sayed, et al., Building a COVID-19 vulnerability index, preprint, arXiv: 2003.07347. |
| [77] | A. Rahman, M. J. Islam, M. R. Karim, D. Kundu, S. Kabir, An intelligent vaccine distribution process in COVID-19 pandemic through blockchain-sdn framework from bangladesh perspective, in International Conference on Electronics, Communications and Information Technology 2021 (ICECIT 2021), 2021. |
| [78] | M. Zastrow, Coronavirus contact-tracing apps: can they slow the spread of COVID-19? Nature, 2020. |
| [79] |
Z. Geng, X. Zhang, Z. Fan, X. Lv, Y. Su, H. Chen, Recent progress in optical biosensors based on smartphone platforms, Sensors, 17 (2017), 2449. doi: 10.3390/s17112449
|
| [80] | T. Wright, Blockchain app used to track COVID-19 cases in Latin America, Coin Telegraph Future Money, 6 (2020). |
| [81] | Ministry of Health, HaMagen 2.0: together we can defeat COVID-19, Available from: https://govextra.gov.il/ministry-of-health/hamagen-app/download-en/. |
| [82] | C. Chong, About 1 million people have downloaded tracetogether app, but more need to do so for it to be effective: lawrence wong, Straits Times, 2020. |
| [83] | L. Kelion, Coronavirus: moscow rolls out patient-tracking app, 2020. |
| [84] | A. Rahman, C. Chakraborty, A. Anwar, M. Karim, M. Islam, D. Kundu, et al., Sdn–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic, Cluster Comput., (2021), 1–18. |
| [85] | Teladochealth, Whole-Person: virtual care for all, Available from: https://www.teladochealth.com/. |
| [86] | A. Chakraborty, Assam: telemedicine, video monitoring for COVID-19 home quarantined people in Dhemaji, 2020. |
| [87] | D. O'Keeffe, A World First as Drone delivers medication to the Aran Islands, 2019. |
| [88] | J. Yang, T. Reuter, Three ways China is using drones to fight coronavirus, in World Economic Forum, 16 (2020). |
| [89] | E. Ackerman, Zipline wants to bring medical drone delivery to us to fight COVID-19, IEEE Spectrum N. Y. NY USA, 2020. |
| [90] | S. Sahasranamam, How coronavirus sparked a wave of innovation in India, in World Economic Forum, 2020. |
| [91] |
M. Javaid, A. Haleem, R. Vaishya, S. Bahl, R. Suman, A. Vaish, Industry 4.0 technologies and their applications in fighting COVID-19 pandemic, Diabetes Metab. Syndr. Clin. Res. Rev., 14 (2020), 419–422. doi: 10.1016/j.dsx.2020.06.065
|
| [92] | A. Ghimire, S. Thapa, A. K. Jha, A. Kumar, A. Kumar, S. Adhikari, AI and IoT solutions for tackling COVID-19 pandemic, in 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, (2020), 1083–1092. |
| [93] |
L. Bai, D. Yang, X. Wang, L. Tong, X. Zhu, N. Zhong, et al., Chinese experts' consensus on the Internet of things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19), Clin. eHealth, 3 (2020), 7–15. doi: 10.1016/j.ceh.2020.03.001
|
| [94] | M. J. Islam, A. Rahman, S. Kabir, M. R. Karim, U. K. Acharjee, M. K. Nasir, et al., Blockchain-sdn based energy-aware and distributed secure architecture for IoTs in smart cities, IEEE Internet Things J., (2021), 1. |
| [95] | World Certification Institute, How next-generation information technologies tackled COVID-19 in China, 2020. Available from: https://www.worldcertification.org/how-next-generation-information-technologies-tackled-covid-19-china/. |
| [96] | H. H. Elmousalami, A. Darwish, A. E. Hassanien, The truth about 5G and COVID-19: basics, analysis, and opportunities, in Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches, (2021), 249–259, . |
| [97] | A. Rahman, U. Sara, D. Kundu, S. Islam, M. J. Islam, M. Hasan, et al., Distb-sdoindustry: enhancing security in industry 4.0 services based on distributed blockchain through software defined networking-IoT enabled architecture, Int. J. Adv. Comput. Sci. Appl., 11 (2020). |
| [98] | S. Jaafari, A. Alhasani, S. M. Almutairi, E. Alghosn, R. Alfahhad, Certain investigations on IoT system for COVID-19, in 2020 International Conference on Computing and Information Technology (ICCIT-1441), IEEE, (2020), 1–4. |
| [99] | R. Stojanović, A. Škraba, B. Lutovac, A headset like wearable device to track COVID-19 symptoms, in 2020 9th Mediterranean Conference on Embedded Computing (MECO), IEEE, (2020), 1–4. |
| [100] | Researchers use open-source software to improve COVID-19 screening with AI, 2020. Available from: https://uwaterloo.ca/news/news/researchers-use-open-source-software-improve-covid-19. |
| [101] | B. Marr, Robots and drones are now used to fight COVID-19, 2020. |
| [102] | Delhi civic body begins thermal screening people on balconies with drones, 2020. Available from: https://www.ndtv.com/delhi-news/coronavirus-delhi-civic-body-using-drones-to-check-temperature-of-people-on-balconies-2209832. |
| [103] |
M. Abdel-Basset, V. Chang, N. A. Nabeeh, An intelligent framework using disruptive technologies for COVID-19 analysis, Technol. Forecast. Soc. Change, 163 (2021), 120431. doi: 10.1016/j.techfore.2020.120431
|
| [104] | S. Gilgore, GWU hospital tackles COVID-19 with new testing site, telemedicine and outreach on D.C.'s east side, 2020. |
| [105] | M. Shah, A. Tosto, Industry voices—how rush university medical center's virtual investments became central to its COVID-19 response, 2020. |
| [106] | S. Simmons, R. Carrion, K. Alfson, H. Staples, C. Jinadatha, W. Jarvis, et al., Disinfection effect of pulsed xenon ultraviolet irradiation on SARS-CoV-2 and implications for environmental risk of COVID-19 transmission, medRxiv, 2020. |
| [107] | B. Spice, COVID-19 should be wake-up call for robotics research, 2020. |
| [108] |
R. Vaishya, M. Javaid, I. H. Khan, A. Haleem, Artificial intelligence (AI) applications for COVID-19 pandemic, Diabetes Metab. Syndr. Clin. Res. Rev., 14 (2020), 337–339. doi: 10.1016/j.dsx.2020.04.012
|
| [109] | B. G. Ahn, Y. H. Noh, D. U. Jeong, Smart chair based on multi heart rate detection system, in 2015 IEEE SENSORS, IEEE, (2015), 1–4. |
| [110] | I. Chiuchisan, H. N. Costin, O. Geman, Adopting the Internet of things technologies in health care systems, in 2014 International Conference and Exposition on Electrical and Power Engineering (EPE), IEEE, (2014), 532–535. |
| [111] |
P. K. Sahoo, S. K. Mohapatra, S. L. Wu, Analyzing healthcare big data with prediction for future health condition, IEEE Access, 4 (2016), 9786–9799. doi: 10.1109/ACCESS.2016.2647619
|
| [112] | G. Sharma, S. Kalra, A lightweight user authentication scheme for cloud-IoT based healthcare services, Iran. J. Sci. Technol. Trans. Electr. Eng., 43 (2019), 619–636. |
| [113] | F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang, Z. Tang, et al., Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19, IEEE Rev. Biomed. Eng., 2020. |
| [114] |
L. Wang, Z. Q. Lin, A. Wong, COVID-net: atailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images, Sci. Rep., 10 (2020), 1–12. doi: 10.1038/s41598-019-56847-4
|
| [115] | M. Farooq, A. Hafeez, COVID-resnet: A deep learning framework for screening of COVID-19 from radiographs, preprint, arXiv: 2003.14395. |
| [116] |
A. Roy, F. H. Kumbhar, H. S. Dhillon, N. Saxena, S. Y. Shin, S. Singh, Efficient monitoring and contact tracing for COVID-19: a smart iot-based framework, IEEE Internet Things Mag., 3 (2020), 17–23. doi: 10.1109/IOTM.0001.2000145
|
| [117] |
L. Wang, Microwave sensors for breast cancer detection, Sensors, 18 (2018), 655. doi: 10.3390/s18020655
|
| [118] | T. C. Chiang, Y. S. Huang, R. T. Chen, C. S. Huang, R. F. Chang, Tumor detection in automated breast ultrasound using 3-d cnn and prioritized candidate aggregation, IEEE Trans. Med. Imaging, 38 (2018), 240–249. |
| [119] |
Y. Lei, X. He, J. Yao, T. Wang, L. Wang, W. Li, et al., Breast tumor segmentation in 3d automatic breast ultrasound using mask scoring r-cnn, Med. Phys., 48 (2021), 204–214. doi: 10.1002/mp.14569
|
| [120] |
M. Veta, Y. J. Heng, N. Stathonikos, B. E. Bejnordi, F. Beca, T. Wollmann, et al., Predicting breast tumor proliferation from whole-slide images: the tupac16 challenge, Med. Image Anal., 54 (2019), 111–121. doi: 10.1016/j.media.2019.02.012
|
| [121] | G. Pradhan, R. Pradhan, B. Khandelwal, A study on various machine learning algorithms used for prediction of diabetes mellitus, in Soft Computing Techniques and Applications, Springer, (2021), 553–561. |
| [122] | A. Shanthini, G. Manogaran, G. Vadivu, K. Kottilingam, P. Nithyakani, C. Fancy, Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network, J. Ambient Intell. Human. Comput., (2021), 1–15. |
| [123] |
V. Bavkar, A. Shinde, Machine learning algorithms for diabetes prediction and neural network method for blood glucose measurement, Indian J. Sci. Technol., 14 (2021), 869–880. doi: 10.17485/IJST/v14i10.2187
|
| [124] |
E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna, M. Z. Parvez, Corodet: a deep learning based classification for COVID-19 detection using chest x-ray images, Chaos Solitons Fractals, 142 (2021), 110495. doi: 10.1016/j.chaos.2020.110495
|
| [125] | P. R. Bassi, R. Attux, A deep convolutional neural network for COVID-19 detection using chest x-rays, Res. Biomed. Eng., (2021), 1–10. |
| [126] |
A. M. Ismael, A. Şengür, Deep learning approaches for COVID-19 detection based on chest x-ray images, Expert Syst. Appl., 164 (2021), 114054. doi: 10.1016/j.eswa.2020.114054
|
| [127] |
A. Tahamtan, A. Ardebili, Real-time rt-pcr in COVID-19 detection: issues affecting the results, Expert Rev. Mol. Diagn., 20 (2020), 453–454. doi: 10.1080/14737159.2020.1757437
|
| [128] | B. Ghoshal, A. Tucker, Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection, preprint, arXiv: 2003.10769. |
| [129] | A. Mangal, S. Kalia, H. Rajgopal, K. Rangarajan, V. Namboodiri, S. Banerjee, et al., Covidaid: COVID-19 detection using chest x-ray, preprint, arXiv: 2004.09803. |
| [130] |
S. Vaid, R. Kalantar, M. Bhandari, Deep learning COVID-19 detection bias: accuracy through artificial intelligence, Int. Orthop., 44 (2020), 1539–1542. doi: 10.1007/s00264-020-04609-7
|
| [131] |
A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, P. R. Pinheiro, Covidgan: data augmentation using auxiliary classifier gan for improved COVID-19 detection, IEEE Access, 8 (2020), 91 916–91 923. doi: 10.1109/ACCESS.2020.2994762
|
| [132] |
W. Liu, Q. Zhang, J. Chen, R. Xiang, H. Song, S. Shu, et al., Detection of COVID-19 in children in early january 2020 in Wuhan, China, N. Engl. J. Med., 382 (2020), 1370–1371. doi: 10.1056/NEJMc2003717
|
| [133] | M. Z. Alom, M. Rahman, M. S. Nasrin, T. M. Taha, V. K. Asari, COVID_mtnet: COVID-19 detection with multi-task deep learning approaches, preprint, arXiv: 2004.03747. |
| [134] |
L. Brunese, F. Mercaldo, A. Reginelli, A. Santone, Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from x-rays, Comput. Methods and Programs in Biomed., 196 (2020), 105608. doi: 10.1016/j.cmpb.2020.105608
|
| [135] |
L. Ni, F. Ye, M. L. Cheng, Y. Feng, Y. Q. Deng, H. Zhao, et al., Detection of SARS-CoV-2-specific humoral and cellular immunity in COVID-19 convalescent individuals, Immunity, 52 (2020), 971–977. doi: 10.1016/j.immuni.2020.04.023
|
| [136] | A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (COVID-19) using x-ray images and deep convolutional neural networks, preprint, arXiv: 2003.10849. |
| [137] |
T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, U. R. Acharya, Automated detection of COVID-19 cases using deep neural networks with x-ray images, Comput. Biol. Med., 121 (2020), 103792. doi: 10.1016/j.compbiomed.2020.103792
|
| [138] | P. K. Sethy, S. K. Behera, P. K. Ratha, P. Biswas, Detection of coronavirus disease (COVID-19) based on deep features and support vector machine, 2020. |
| [139] |
A. Rahman, M. J. Islam, A. Montieri, M. K. Nasir, M. M. Reza, S. S. Band, et al., Smartblock-sdn: an optimized blockchain-sdn framework for resource management in IoT, IEEE Access, 9 (2021), 283 61–283 76. doi: 10.1109/ACCESS.2021.3096125
|
| [140] | M. J. Islam, A. Rahman, S. Kabir, A. Khatun, A. Pritom, M. Chowdhury, Sdot-nfv: a distributed sdn based security system with IoT for smart city environments, GUB J. Sci. Eng., 7 (2021), 27–35. |
| [141] | M. Ndiaye, A. M. Abu-Mahfouz, G. P. Hancke, Sdnmm—a generic sdn-based modular management system for wireless sensor networks, IEEE Syst. J., 14 (2019), 2347–2357. |
| [142] | S. Islam, U. Sara, A. Kawsar, A. Rahman, D. Kundu, D. D. Dipta, et al., Sgbba: an efficient method for prediction system in machine learning using imbalance dataset, Int. J. Adv. Comput. Sci. Appl., 12 (2021). |
| [143] | A. Rahman, M. J. Islam, F. A. Sunny, M. K. Nasir, Distblocksdn: a distributed secure blockchain based sdn-IoT architecture with nfv implementation for smart cities, in 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), (2019), 1–6. |
| [144] | M. J. Hossain, M. A. H. Wadud, A. Rahman, J. Ferdous, M. F. Mridha, A secured patient's online data monitoring through blockchain: an intelligent way to store lifetime medical records, in International Conference on Science and Contemporary Technologies (ICSCT), 2021. |
| [145] | D. Li, 5G and intelligence medicine—how the next generation of wireless technology will reconstruct healthcare? Precis. Clin. Med., 2 (2019), 205–208. |
| [146] | Z. Allam, D. S. Jones, On the coronavirus (COVID-19) outbreak and the smart city network: universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management, in Healthcare, Multidisciplinary Digital Publishing Institute, 8 (2020), 1–9. |
Figures(6) / Tables(4)
Anichur Rahman, Muaz Rahman, Dipanjali Kundu, Md Razaul Karim, Shahab S. Band, Mehdi Sookhak. Study on IoT for SARS-CoV-2 with healthcare: present and future perspective[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9697-9726. doi: 10.3934/mbe.2021475
| Eff. type | Frequency | Percent (%) |
| Exudate | 95 | 70.4% |
| Transudate | 40 | 29.6% |
| Total | 135 | 100.0% |
DownLoad: CSV| Male | Female | Total | ||||
| Eff. Type | No | % | No | % | No | % |
| Exudate | 64 | 67.4% | 31 | 32.6% | 95 | 70.4% |
| Transudate | 23 | 56.5% | 17 | 42.5% | 40 | 29.6% |
| Total | 87 | 64.4 | 48 | 45.6 | 135 | 100.0% |
DownLoad: CSV| 20-40 | 41-60 | > 60 | Total | |||||
| Eff. type | No | % | No | % | No | % | No | % |
| Exudate | 19 | 20 % | 28 | 29.5 % | 48 | 50.5 % | 95 | 70.4 % |
| Transudate | 10 | 25 % | 18 | 45 % | 12 | 30 % | 40 | 29.6 % |
| Total | 29 | 21.5 % | 46 | 34 % | 60 | 44.4 % | 135 | 100 % |
DownLoad: CSV| T.B | Pneu. | Pulm Ca. | Pulm emboli | Rheum Disease | Hepatic Cirrhosis | CHF | Total | |||||||||
| Eff. | No | % | No | % | No | % | No | % | No | % | No | % | No | % | No | % |
| Exud | 52 | 54.7% | 18 | 18.9% | 10 | 10.5% | 9 | 9.5% | 6 | 6.3% | 0 | 0% | 0 | 0% | 95 | 70.4% |
| Trans | 0 | 0 | 0 | 0% | 0 | 0% | 0 | 0% | 0 | 0% | 22 | 55% | 18 | 45% | 40 | 29.6% |
| Total | 52 | 38.5% | 18 | 13.3% | 10 | 7.4% | 9 | 6.7% | 6 | 4.4% | 22 | 16.3% | 18 | 14.8% | 135 | 100% |
DownLoad: CSV| Parameter | Type of sample | Type of effusion | Mean ± SEM |
| CHOL | Pleural fluid | Exudate | 1.92 ± 0.75 mmol/L * |
| Transudate | 0.52 ± 0.28 mmol/L * | ||
| Serum | Exudate | 3.56 ± 0.84 mmol/L * | |
| Transudate | 1.63 ± 0.75 mmol/L * | ||
| Protein | Pleural fluid | Exudate | 3.56 ± 095 g/dL * |
| Transudate | 0.93 ± 0.35 g/dL * | ||
| Serum | Exudate | 5.25 ± 0.88 g/dL * | |
| Transudate | 1.55 ± 0.61 g/dL * | ||
| LDH | Pleural fluid | Exudate | 285 ± 25 IU * |
| Transudate | 160 ± 22 IU * | ||
| Serum | Exudate | 235 ± 45 IU * | |
| Transudate | 134 ± 18 IU * |
DownLoad: CSV| Parameters | CHOL. Seru. | CHOL. Eff. | LDH Seru. | LDH Eff. | Prot. Seru. | Prot. Eff. | ||||||
| r | sig | r | sig | r | sig | r | sig | r | sig | r | sig | |
| CHOL.Seru. | 1 | 0.777** | 0.000 | 0.673** | 0.000 | 0.855** | 0.000 | 0.529** | 0.000 | 0.532** | 0.000 | |
| CHOL. Eff. | 0.777** | 0.000 | 1 | 0.867** | 0.000 | 0.667** | 0.000 | 0.061 | 0.000 | 0.791** | 0.000 | |
| LDH Seru. | 0.673** | 0.000 | 0.867** | 0.000 | 1 | 0.761** | 0.000 | 0.207** | 0.005 | 0.824** | 0.000 | |
| LDH Eff. | 0.855** | 0.000 | 0.667** | 0.000 | 0.761** | 0.000 | 1 | 0.690** | 0.000 | 0.630** | 0.000 | |
| Prot. Seru. | 0.529** | 0.000 | 0.061** | 0.000 | 0.207** | 0.005 | 0.690** | 0.000 | 1 | 0.699** | 0.000 | |
| Prot. Eff. | 0.532** | 0.000 | 0.791** | 0.000 | .824** | 0.000 | 0.630** | 0.000 | 0.699** | 0.000 | 1 | |
| ** Correlation is significant at the 0.01 level (2-tailed). | ||||||||||||
DownLoad: CSV| Parameters | Sensitivity | Specificity | PPV | NPV | p value |
| CHOL ratio | 97.7% | 100.0% | 100.0% | 95.0% | < 0.0001 |
| Protein ratio | 81.4% | 81.6% | 89.7% | 70.4% | < 0.0001 |
| LDH ratio | 86.0% | 97.4% | 97.4% | 79.0% | < 0.0001 |
DownLoad: CSV| Eff. type | Frequency | Percent (%) |
| Exudate | 95 | 70.4% |
| Transudate | 40 | 29.6% |
| Total | 135 | 100.0% |
| Male | Female | Total | ||||
| Eff. Type | No | % | No | % | No | % |
| Exudate | 64 | 67.4% | 31 | 32.6% | 95 | 70.4% |
| Transudate | 23 | 56.5% | 17 | 42.5% | 40 | 29.6% |
| Total | 87 | 64.4 | 48 | 45.6 | 135 | 100.0% |
| 20-40 | 41-60 | > 60 | Total | |||||
| Eff. type | No | % | No | % | No | % | No | % |
| Exudate | 19 | 20 % | 28 | 29.5 % | 48 | 50.5 % | 95 | 70.4 % |
| Transudate | 10 | 25 % | 18 | 45 % | 12 | 30 % | 40 | 29.6 % |
| Total | 29 | 21.5 % | 46 | 34 % | 60 | 44.4 % | 135 | 100 % |
| T.B | Pneu. | Pulm Ca. | Pulm emboli | Rheum Disease | Hepatic Cirrhosis | CHF | Total | |||||||||
| Eff. | No | % | No | % | No | % | No | % | No | % | No | % | No | % | No | % |
| Exud | 52 | 54.7% | 18 | 18.9% | 10 | 10.5% | 9 | 9.5% | 6 | 6.3% | 0 | 0% | 0 | 0% | 95 | 70.4% |
| Trans | 0 | 0 | 0 | 0% | 0 | 0% | 0 | 0% | 0 | 0% | 22 | 55% | 18 | 45% | 40 | 29.6% |
| Total | 52 | 38.5% | 18 | 13.3% | 10 | 7.4% | 9 | 6.7% | 6 | 4.4% | 22 | 16.3% | 18 | 14.8% | 135 | 100% |
| Parameter | Type of sample | Type of effusion | Mean ± SEM |
| CHOL | Pleural fluid | Exudate | 1.92 ± 0.75 mmol/L * |
| Transudate | 0.52 ± 0.28 mmol/L * | ||
| Serum | Exudate | 3.56 ± 0.84 mmol/L * | |
| Transudate | 1.63 ± 0.75 mmol/L * | ||
| Protein | Pleural fluid | Exudate | 3.56 ± 095 g/dL * |
| Transudate | 0.93 ± 0.35 g/dL * | ||
| Serum | Exudate | 5.25 ± 0.88 g/dL * | |
| Transudate | 1.55 ± 0.61 g/dL * | ||
| LDH | Pleural fluid | Exudate | 285 ± 25 IU * |
| Transudate | 160 ± 22 IU * | ||
| Serum | Exudate | 235 ± 45 IU * | |
| Transudate | 134 ± 18 IU * |
| Parameters | CHOL. Seru. | CHOL. Eff. | LDH Seru. | LDH Eff. | Prot. Seru. | Prot. Eff. | ||||||
| r | sig | r | sig | r | sig | r | sig | r | sig | r | sig | |
| CHOL.Seru. | 1 | 0.777** | 0.000 | 0.673** | 0.000 | 0.855** | 0.000 | 0.529** | 0.000 | 0.532** | 0.000 | |
| CHOL. Eff. | 0.777** | 0.000 | 1 | 0.867** | 0.000 | 0.667** | 0.000 | 0.061 | 0.000 | 0.791** | 0.000 | |
| LDH Seru. | 0.673** | 0.000 | 0.867** | 0.000 | 1 | 0.761** | 0.000 | 0.207** | 0.005 | 0.824** | 0.000 | |
| LDH Eff. | 0.855** | 0.000 | 0.667** | 0.000 | 0.761** | 0.000 | 1 | 0.690** | 0.000 | 0.630** | 0.000 | |
| Prot. Seru. | 0.529** | 0.000 | 0.061** | 0.000 | 0.207** | 0.005 | 0.690** | 0.000 | 1 | 0.699** | 0.000 | |
| Prot. Eff. | 0.532** | 0.000 | 0.791** | 0.000 | .824** | 0.000 | 0.630** | 0.000 | 0.699** | 0.000 | 1 | |
| ** Correlation is significant at the 0.01 level (2-tailed). | ||||||||||||
| Parameters | Sensitivity | Specificity | PPV | NPV | p value |
| CHOL ratio | 97.7% | 100.0% | 100.0% | 95.0% | < 0.0001 |
| Protein ratio | 81.4% | 81.6% | 89.7% | 70.4% | < 0.0001 |
| LDH ratio | 86.0% | 97.4% | 97.4% | 79.0% | < 0.0001 |
