Augmenting endometriosis analysis from ultrasound data using deep learning

Adrian Balica; Jennifer Dai; Kayla Piiwaa; Xiao Qi; Ashlee N. Green; Nancy Phillips; Susan Egan; Ilker Hacihaliloglu

doi:10.1117/12.2653940

10 April 2023 Augmenting endometriosis analysis from ultrasound data using deep learning

Adrian Balica, Jennifer Dai, Kayla Piiwaa, Xiao Qi, Ashlee N. Green, Nancy Phillips, Susan Egan, Ilker Hacihaliloglu

Proceedings Volume 12470, Medical Imaging 2023: Ultrasonic Imaging and Tomography; 124700O (2023) https://doi.org/10.1117/12.2653940
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Endometriosis is a non-malignant disorder that affects 176 million women globally. Diagnostic delays result in severe dysmenorrhea, dyspareunia, chronic pelvic pain, and infertility. Therefore, there is a significant need to diagnose patients at an early stage. Our objective in this work is to investigate the potential of deep learning methods to classify endometriosis from ultrasound data. Retrospective data from 100 subjects were collected at the Rutgers Robert Wood Johnson University Hospital (New Brunswick, NJ, USA). Endometriosis was diagnosed via laparoscopy or laparotomy. We designed and trained five different deep learning methods (Xception, Inception-V4, ResNet50, DenseNet, and EfficientNetB2) for the classification of endometriosis from ultrasound data. Using 5-fold cross-validation study we achieved an average area under the receiver operator curve (AUC) of 0.85 and 0.90 respectively for the two evaluation studies.

Conference Presentation

Citation Download Citation

Adrian Balica, Jennifer Dai, Kayla Piiwaa, Xiao Qi, Ashlee N. Green, Nancy Phillips, Susan Egan, and Ilker Hacihaliloglu "Augmenting endometriosis analysis from ultrasound data using deep learning", Proc. SPIE 12470, Medical Imaging 2023: Ultrasonic Imaging and Tomography, 124700O (10 April 2023); https://doi.org/10.1117/12.2653940

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