Research article Special Issues

Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system


  • Received: 09 February 2021 Accepted: 13 April 2021 Published: 27 April 2021
  • Objective

    Traditional breast ultrasound relies too much on the operation skills of diagnostic doctors, and the repeatability in different doctors was low. This study aimed to evaluate the assistant diagnostic value of S-Detect artificial intelligence (AI) system in differentiating benign from malignant breast masses.

    Methods

    The ultrasound images of 40 patients who underwent ultrasound examination in our hospital were collected. The conventional ultrasound images, elastic images, and S-Detect mode of breast lesions were analyzed. The breast imaging reporting and data system recommended by the American Society of Radiology (BI-RADS) classification for each breast mass was evaluated both by the doctor and AI. The receiver operator characteristics (ROC) curves were drawn to compare the diagnostic efficiency.

    Result

    Among the 40 lesions, 16 were benign, and 24 were malignant. The S-Detect AI system had a high diagnostic efficiency for malignant mass, with sensitivity, specificity, and accuracy of 95.8%, 93.8%, and 89.6%. The accuracy of AI was higher than the elastic image and then than the conventional gray-scale image. With the assistance of the S-Detect AI system, the accuracy of BI-RADS classification was improved significantly.

    Conclusion

    The S-Detect AI system will enhance breast cancer diagnostic accuracy and improve ultrasound examination quality.

    Citation: Qun Xia, Yangmei Cheng, Jinhua Hu, Juxia Huang, Yi Yu, Hongjuan Xie, Jun Wang. Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 3680-3689. doi: 10.3934/mbe.2021184

    Related Papers:

  • Objective

    Traditional breast ultrasound relies too much on the operation skills of diagnostic doctors, and the repeatability in different doctors was low. This study aimed to evaluate the assistant diagnostic value of S-Detect artificial intelligence (AI) system in differentiating benign from malignant breast masses.

    Methods

    The ultrasound images of 40 patients who underwent ultrasound examination in our hospital were collected. The conventional ultrasound images, elastic images, and S-Detect mode of breast lesions were analyzed. The breast imaging reporting and data system recommended by the American Society of Radiology (BI-RADS) classification for each breast mass was evaluated both by the doctor and AI. The receiver operator characteristics (ROC) curves were drawn to compare the diagnostic efficiency.

    Result

    Among the 40 lesions, 16 were benign, and 24 were malignant. The S-Detect AI system had a high diagnostic efficiency for malignant mass, with sensitivity, specificity, and accuracy of 95.8%, 93.8%, and 89.6%. The accuracy of AI was higher than the elastic image and then than the conventional gray-scale image. With the assistance of the S-Detect AI system, the accuracy of BI-RADS classification was improved significantly.

    Conclusion

    The S-Detect AI system will enhance breast cancer diagnostic accuracy and improve ultrasound examination quality.



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