Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: a retrospective study

Abstract

Abstract To explore a new artificial intelligence-assisted method to assist junior ultrasonographers in improving the diagnostic performance of uterine fibroids and further compare it with senior ultrasonographers to confirm the effectiveness and feasibility of artificial intelligence. In this retrospective study, we collected a total of 3870 ultrasound images from 667 patients (mean age: 42.45 years ± 6.23 [SD]) who were pathological diagnosed with uterine fibroids and 570 women (mean age: 39.24 years ± 5.32 [SD]) without uterine lesions from Shunde Hospital between 2015 and 2020. The DCNN model was trained and developed on the training dataset (2706 images) and internal validation dataset (676 images). To evaluate the performance of the model, on the external validation dataset (488 images), we assessed the diagnostic performance of the DCNN with ultrasonographers possessing different levels of seniority. The DCNN model aided the junior ultrasonographers (Averaged) in diagnosing uterine fibroids with higher accuracy (94.72% vs. 86.63%, P < 0.001), sensitivity (92.82% vs. 83.21%, P = 0.001), specificity (97.05% vs. 90.80%, P = 0.009), positive predictive value (97.45% vs. 91.68%, P = 0.007), and negative predictive value (91.73% vs. 81.61%, P = 0.001) than they achieved alone. Their ability was comparable to that of the senior ultrasonographers (Averaged) in terms of accuracy (94.72% vs. 95.24%, P = 0.66), sensitivity (92.82% vs. 93.66%, P = 0.73), specificity (97.05% vs. 97.16%, P = 0.79), positive predictive value (97.45% vs. 97.57%, P = 0.77), and negative predictive value (91.73% vs. 92.63%, P = 0.75). The DCNN-assisted strategy can significantly improve the uterine fibroid diagnosis performance of junior ultrasonographers and is comparable to that of senior ultrasonographers.