Evaluation of an AI-Driven Deep Learning Model in Predicting Mammographic Breast Density

Abstract Background Increased mammographic breast density is an established risk factor for breast cancer. Given the high workload of mammography screening programs, Mounting attention is being directed at establishing and fine- tunning AI algorithms capable of improving the performance of human readers. Aim of the Work To emphasize the role of AI-driven deep learning algorithms in accurate prediction of breast density on digital mammograms. Patients and Methods The study was performed on 62 digital mammography films of women undergoing breast cancer screening. Mammograms were assessed by three human readers of different levels of experience in comparison to a commercially available AI algorithm (Lunit INSIGHT MMG, version 1.1.7.2; Lunit Inc). Results This clinical investigation demonstrated that AI softwares (namely LUNIT INSIGHT in our study) can predict mammographic breast density with about 67.7%, 69.4 % and 75.81 % accuracy for HR1(least experienced), HR2 and HR3 (most experienced) respectively, and reliability (Cronbach's Alpha) 0.887, 0.892 and 0.906 for HR1(least experienced), HR2 and HR3 (most experienced) respectively. This result offers a robust way to overcome the variability of human visual assessment and improve the overall performance of less experienced radiologists allowing them to reach expert like levels. Conclusion Application of AI softwares on screening mammograms can overcome the variability of human visual assessment and improve the overall performance of less experienced radiologists allowing them to reach expert like levels.