Revolutionizing endometriosis treatment: automated surgical operation through artificial intelligence and robotic vision

Clinical limitations due to poverty significantly impact the lives and health of many individuals globally. Nevertheless, this challenge can be addressed with modern technologies, particularly through robotics and artificial intelligence. This study aims to address these challenges using advanced technologies in robotic surgery and artificial intelligence, proposing a method to fully automate endometriosis robotic surgery with a focus on interpretability, accuracy, and reliability. A methodology for fully automatic endometriosis surgery is introduced. Given the complexity of endometriosis lesions detection, they are categorized by their anatomical location to improve system interpretability. Then, three ensemble U-Net frameworks are designed to detect and localize common types of endometriosis lesions intraoperatively. A cross-training approach is employed, exploring U-Net models with diverse neural architectures—such as ResNet50, ResNet101, VGG19, InceptionV3, MobileNet, and EfficientNetB7—to develop U-Net ensemble models for precise endometriosis lesions segmentation. A novel image augmentation technique is also introduced, enhancing the segmentation models’ accuracy and reliability. Furthermore, two U-Net models are developed to localize the ovaries and uterus, mitigating unexpected noise and bolstering the method’s accuracy and reliability. The image segmentation models, assessed using the Intersection over Union (IoU) metric, achieved outstanding results: 97.57% for ovarian, 96.35% for uterine, and 92.58% for peritoneal endometriosis. This study proposes a fully automatic method for some common types of endometriosis surgery, including ovarian endometriomas and superficial endometriosis. This method is centered around three ensemble U-Net frameworks and a noise reduction technique using two additional U-Nets for localizing the ovaries and uterus. This approach has the potential to significantly improve the accuracy and reliability of robotic surgeries, potentially reducing healthcare costs and improving outcomes for patients worldwide. Similar content being viewed by others Data availability No datasets were generated or analysed during the current study.

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The second author (Maryam Amirmazlaghani) reviewed the manuscript. Corresponding author Ethics declarations Conflict of interests The authors declare no conflict of interest is sufficient. This study was conducted in accordance with ethical standards. As this research utilized only open-source datasets and did not involve any human or animal subjects, no ethical approval was required. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Saadati, S., Amirmazlaghani, M. Revolutionizing endometriosis treatment: automated surgical operation through artificial intelligence and robotic vision. J Robotic Surg 18, 383 (2024). https://doi.org/10.1007/s11701-024-02139-7 Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1007/s11701-024-02139-7