Application of deep learning model in the sonographic diagnosis of uterine adenomyosis

Abstract

Purpose: To evaluate the diagnostic performance of Deep Learning (DL) machine for the detection of adenomyosis on uterine ultrasonographic images and compare it to intermediate ultrasound skilled trainees. Methods: Prospective observational study conducted between 1st and 30th April 2022. Transvaginal ultrasound (TVUS) diagnosis of adenomyosis was investigated by an experienced sonographer on 100 fertile-age patients. Videoclips of the uterine corpus were recorded and sequential ultrasound images were extracted. Intermediate ultrasound skilled trainees and DL machine were asked to make a diagnosis reviewing uterine images. We evaluated and compared the accuracy, sensitivity, positive predictive value, F1- score, specificity and negative predictive value of the DL model and the trainees for adenomyosis diagnosis. Results: Accuracy of DL and intermediate ultrasound skilled trainees for the diagnosis of adenomyosis were 0.51 (95% CI, 0.48-0.54) and 0.70 (95% CI, 0.60-0.79), respectively. Sensitivity, specificity and F1-score of DL were 0.43 (95% CI, 0.38-0.48), 0.82 (95% CI, 0.79-0.85) and 0.46 (0.42-0.50), whereas intermediate ultrasound skilled trainees had sensitivity of 0.72 (95% CI, 0.52-0.86), specificity of 0.69 (95% CI, 0.58-0.79) and F1-score of 0.55 (95% CI, 0.43-0.66). Conclusion: In this preliminary study DL model showed a lower accuracy but a higher specificity in diagnosing adenomyosis on ultrasonographic images compared to intermediate skilled trainees.