[3H4-OS-10b] OS-10

[3H4-OS-10b-03] Development of an AI-based Diagnostic Support System for Endometriosis Using MRI

〇Rie Shiokawa¹, Miho Li¹, Kimio Terao¹, Junichiro Iwasawa², Yuta Tokuoka², Kazue Mizuno², Yohei Sugawara², Keita Oda² (1. Chugai Pharmaceutical Co., Ltd., 2. Preferred Networks, Inc.)

Keywords:Machine Learning, Endometriosis, Medical Image, MRI, Diagnostic Support

Endometriosis affects approximately 10% of women, causing infertility and chronic pain. Definitive diagnosis requires invasive laparoscopy, leading to delayed treatment intervention. While comprehensive diagnostic methods including non-invasive imaging are gaining attention for early intervention, the global shortage of expertized radiologists remains challenging. This study aimed to develop a machine learning-based diagnostic support system to improve diagnostic accuracy and address the shortage of interpreting radiologists. We designed an integrated model system called AI-based MR imaging Support Program (AMP). This system enables the detection of deep endometriosis (DE) nodular lesions in the posterior uterus and ovarian endometriomas using nnU-Net, as well as the prediction of pelvic inter-organ adhesions using radiomics features. AMP was established through collaboration between expertized radiologists and AI experts, based on the limited number of medical images. MRI is suitable for evaluating DE, but there is a need for advanced analytical expertise and reduction of interpretation burden. AMP addresses these challenges, supporting early diagnosis of DE and appropriate treatment strategy planning through preoperative assessment. This presentation introduces the development process and data collection for establishing AMP using medical images.