Double-reading mammograms using artificial intelligence technologies: A new model of mass preventive examination organization

BACKGROUND: In recent years, the availability of medical datasets and technologies for software development based on artificial intelligence technology has resulted in a growth in the number of solutions for medical diagnostics, particularly mammography. Registered as a medical device, this program can interpret digital mammography, significantly saving time, material, and human resources in healthcare while ensuring the quality of mammary gland preventive studies.

AIM: This study aims to justify the possibility and effectiveness of artificial intelligence-based software for the first interpretation of digital mammograms while maintaining the practice of a radiologist’s second description of X-ray images.

MATERIALS AND METHODS: A dataset of 100 digital mammography studies (50 — “absence of target pathology” and 50 ― “presence of target pathology,” with signs of malignant neoplasms) was processed by software based on artificial intelligence technology that was registered as a medical device in the Russian Federation. Receiver operating characteristic analysis was performed. Limitations of the study include the values of diagnostic accuracy metrics obtained for software based on artificial intelligence technology versions, relevant at the end of 2022.

RESULTS: When set to 80.0% sensitivity, artificial intelligence specificity was 90.0% (95% CI, 81.7–98.3), and accuracy was 85.0% (95% CI, 78.0–92.0). When set to 100% specificity, artificial intelligence demonstrated 56.0% sensitivity (95% CI, 42.2–69.8) and 78.0% accuracy (95% CI, 69.9–86.1). When the sensitivity was set to 100%, the artificial intelligence specificity was 54.0% (95% CI, 40.2–67.8), and the accuracy was 77.0% (95% CI, 68.8–85.2). Two approaches have been proposed, providing an autonomous first interpretation of digital mammography using artificial intelligence. The first approach is to evaluate the X-ray image using artificial intelligence with a higher sensitivity than that of the double-reading mammogram by radiologists, with a comparable level of specificity. The second approach implies that artificial intelligence-based software will determine the mammogram category (“absence of target pathology” or “presence of target pathology”), indicating the degree of “confidence” in the obtained result, depending on the corridor into which the predicted value falls.

CONCLUSIONS: Both proposed approaches for using artificial intelligence-based software for the autonomous first interpretation of digital mammograms can provide diagnostic quality comparable to, if not superior to, double-image reading by radiologists. The economic benefit from the practical implementation of this approach nationwide can range from 0.6 to 5.5 billion rubles annually.