Automatic diagnosis for adenomyosis in ultrasound images by deep neural networks

Objective: To present a new noninvasive technique for automatic diagnosis of adenomyosis, using a novel end-toend unified network framework based on transformer networks. Study design: This is a prospective descriptive study conducted at a university hospital.1654 patients were recruited to the study according to adenomyosis diagnosed by transvaginal ultrasound (TVS). For adenomyosis characteristics and ultrasound images, automatic identification of adenomyosis were performed based on deep learning methods. We called this unique technique A2DNet: Adenomyosis Auto Diagnosis Network. Results: The A2DNet exhibits excellent performance in diagnosis of adenomyosis, achieving an accuracy of 92.33%, a precision of 96.06%, a recall of 91.71% and an F1 score of 93.80% in the test group. The confusion matrix of experimental results show that the A2DNet can achieve a correct diagnosis rate of 92% or more for both normal and adenomyosis samples, which demonstrate the superiority of the A2DNet comparing with the state-ofthe-arts. Conclusion: The A2DNet is a safe and effective technique to aid in automatic diagnosis of adenomyosis. The technique which is nondestructive and non-invasive, is new and unique due to the advantages of artificial intelligence.