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BACKGROUND: This retrospective study aims to validate the effectiveness of artificial intelligence (AI) to detect and classify non-mass breast lesions (NMLs) on ultrasound (US) images. METHODS: A total of 228 patients with NMLs and 596 volunteers without breast lesions on US images were enrolled in the study from January 2020 to December 2022. The pathological results served as the gold standard for NMLs. Two AI models were developed to accurately detect and classify NMLs on US images, including DenseNet121₄48 and MobileNet₄48. To evaluate and compare the diagnostic performance of AI models, the area under the curve (AUC), accuracy, specificity and sensitivity was employed. RESULTS: A total of 228 NMLs patients confirmed by postoperative pathology with 870 US images and 596 volunteers with 1003 US images were enrolled. In the detection experiment, the MobileNet₄48 achieved the good performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0. 999 (95%CI: 0. 997-1. 000), 96. 5%, 96. 9% and 96. 1%, respectively. It was no statistically significant compared to DenseNet121₄48. In the classification experiment, the MobileNet₄48 model achieved the highest diagnostic performance in the testing set, with the AUC, accuracy, sensitivity, and specificity were 0. 837 (95%CI: 0. 990-1. 000), 70. 5%, 80. 3% and 74. 6%, respectively. CONCLUSIONS: This study suggests that the AI models, particularly MobileNet₄48, can effectively detect and classify NMLs in US images. This technique has the potential to improve early diagnostic accuracy for NMLs.
Li et al. (Mon,) studied this question.