The use of computer vision for the mammography preventive research

Abstract

Computer vision algorithms based on artificial intelligence for diagnostic radiology are rapidly developing. For effective implementation in clinical practice, it is necessary to determine the capabilities and limitations of these algorithms. Objective. To evaluate the threshold values of the quality metrics of computer vision artificial intelligence-based algorithms for the analysis of mammographic examinations in comparison with the diagnostic accuracy of the radiologist. Materials and methods. The study involving a group of radiologists was performed on the «Web-based Radiology Evaluation Platform» on a labeled dataset from mammography studies. The same dataset was sent to 5 artificial intelligence (AI)-based algorithms. Metrics of diagnostic accuracy of an average radiologist and each AI service were obtained using ROC analysis. Results. The diagnostic accuracy of radiologists (AUC) significantly exceeded the indices of 2 out of 5 AI services. None of the AI services in the study significantly exceeded the AUC of the «average» radiologist. The diagnostic accuracy metrics of the «average» radiologist were: AUC 0.928 (95% CI 0.883—0.976), sensitivity 0.792 (95% CI 0.677—0.907), specificity 0.940 (95% CI 0.874—1.000). Conclusion. When deciding on the implementation of artificial intelligence-based computer vision algorithms for preventive mammography, the minimum AUC value obtained for the “average” radiologist should be used (>0.88).

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