{"paper_id":"01c2e65c-d93c-4ca5-a722-70f6310d12c7","body_text":"Abstract\nClinical 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.\nSimilar content being viewed by others\nData availability\nNo datasets were generated or analysed during the current study.\nReferences\nNezhat F, Shamshirsaz AA, Yildirim G, Nezhat C, Nezhat C (2009) Pelvic pain, endometriosis, and the role of the gynecologist. In: Altchek A, Deligdisch L (eds) Pediatric, adolescent and young adult gynecology, 1st edn. Wiley-Blackwell, New York, pp 174–194\nWorld Health Organization (2021) Endometriosis. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/endometriosis. Accessed 08 June 2021\nSharma S, Tripathi A (2022) Endometriosis: the enigma that it continues to be. In: The female pelvis—anatomy, function and disorders. https://doi.org/10.5772/intechopen.108774\nNezhat C, Nezhat F, Nezhat C (2008) Nezhat’s operative gynecologic laparoscopy and hysteroscopy. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511547362\nDuffy JM, Arambage K, Correa FJ, Olive D, Farquhar C, Garry R, Barlow DH, Jacobson TZ (2014) Laparoscopic surgery for endometriosis. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD011031.pub2\nNezhat C, Vang N, Tanaka PP, Nezhat CH (2020) Optimal management of endometriosis and pain. In: Nezhat CH (ed) Endometriosis in adolescents, 1st edn. Springer, Cham, pp 195–204. https://doi.org/10.1007/978-3-030-52984-0_7\nTulandi T, Vilos GA (1985) A comparison between laser surgery and electrosurgery for bilateral hydrosalpinx: a 2-year follow-up. Fertil Steril 44:846–848. https://doi.org/10.1016/s0015-0282(16)49052-7\nSemm K (1987) Operative manual for endoscopic abdominal surgery: operative pelviscopy, operative laparoscopy. Year Book Medical, Chicago\nLiboon J, Funkhouser W, Terris DJ (1997) A comparison of mucosal incisions made by scalpel, CO2 laser, electrocautery, and constant-voltage electrocautery. Otolaryngol Head Neck Surg 116:379–385. https://doi.org/10.1016/S0194-59989770277-8\nInternational Working Group of AAGL, ESGE, ESHRE and WES, Tomassetti C, Johnson NP, Petrozza J, Abrao MS, Einarsson JI, Horne AW, Lee TM, Missmer S, Vermeulen N, Zondervan KT, Grimbizis G, Wilde RL (2021) An international terminology for endometriosis. Human Reprod Open 2121:hoab029. https://doi.org/10.1093/hropen/hoab029\nNezhat FR, Kavic M, Nezhat CH, Nezhat C (2023) Forward we go! J Soc Laparosc Robot Surg 27.https://doi.org/10.4293/JSLS.2022.00073\nHiesinger PR (2021) The self-assembling brain: how neural networks grow smarter. Princeton University Press, New Jersey\nKoonce B, Koonce BE (2021) Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization. Apress, New York. https://doi.org/10.1007/978-1-4842-6168-2\nRonneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28\nPedrett R, Mascagni P, Beldi G, Padoy N, Lavanchy JL (2023) Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review. Surg Endosc 37:7412–7424. https://doi.org/10.1007/s00464-023-10335-z\nNaqvi NZ, Kaur K, Khanna S, Singh S (2022) An overview of machine learning techniques focusing on the diagnosis of endometriosis. In: Singh KK, Bajpai MK, Akbari AS (eds) Machine vision and augmented intelligence: select proceedings of MAI 2022, lecture notes in electrical engineering 1007, pp 61–84. https://doi.org/10.1007/978-981-99-0189-0_6\nVisalaxi S, Muthu TS (2021) Automated prediction of endometriosis using deep learning. Int J Nonlinear Anal Appl 12:2403–2416. https://doi.org/10.22075/ijnaa.2021.5383\nRikhari H, Baidya Kayal E, Ganguly S, Sasi A, Sharma S, Dheeksha DS, Saini M, Rangarajan K, Bakhshi S, Kandasamy D, Mehndiratta A (2024) Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans. Int J Comput Assist Radiol Surg 19:261–272. https://doi.org/10.1007/s11548-023-03010-0\nLeibetseder A, Schoeffmann K, Keckstein J, Keckstein S (2022) Endometriosis detection and localization in laparoscopic gynecology. Multimed Tools Appl 81:6191–6215. https://doi.org/10.1007/s11042-021-11730-1\nTajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X (2020) Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal 63:101693. https://doi.org/10.1016/j.media.2020.101693\nVisalaxi S, Sudalaimuthu T (2022) Automated segmentation of endometriosis using transfer learning technique. F1000Research 11: 360. https://doi.org/10.12688/f1000research.110283.2\nMascagni P, Alapatt D, Sestini L, Altieri MS, Madani A, Watanabe Y, Alseidi A, Redan JA, Alfieri S, Costamagna G, Boškoski I (2022) Computer vision in surgery: from potential to clinical value. npj Digit Med 5:163. https://doi.org/10.1038/s41746-022-00707-5\nAlip SL, Kim J, Rha KH, Han WK (2022) Future platforms of robotic surgery. Urol Clin 49:23–38. https://doi.org/10.1016/j.ucl.2021.07.008\nWani NA, Kumar R, Bedi J (2024) DeepXplainer: an interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence. Comput Methods Programs Biomed 243:107879. https://doi.org/10.1016/j.cmpb.2023.107879\ndos Santos KL, dos Santos Silva MP (2024) Deep cross-training: an approach to improve deep neural network classification on mammographic images. Expert Syst Appl 238:122142. https://doi.org/10.1016/j.eswa.2023.122142\nRaaj RS (2023) Breast cancer detection and diagnosis using hybrid deep learning architecture. Biomed Signal Process Control 82:104558. https://doi.org/10.1016/j.bspc.2022.104558\nHuang Q, Ding H, Effatparvar M (2024) Breast cancer diagnosis based on hybrid SqueezeNet and improved chef-based optimizer. Expert Syst Appl 237:121470. https://doi.org/10.1016/j.eswa.2023.121470\nSzeliski R (2022) Computer vision: algorithms and applications. Springer. https://doi.org/10.1007/978-3-030-34372-9\nHan B, Yao Q, Yu X, Niu G, Xu M, Hu W, Tsang I, Sugiyama M (2018) Co-teaching: robust training of deep neural networks with extremely noisy labels. Adv Neural Inf Process Syst 31 (NeurIPS 2018), pp 8536–8546. https://doi.org/10.48550/arXiv.1804.06872\nFunding\nNo external funding was used for this research.\nAuthor information\nAuthors and Affiliations\nContributions\nThe first author (Sina Saadati) wrote the main manuscript. The second author (Maryam Amirmazlaghani) reviewed the manuscript.\nCorresponding author\nEthics declarations\nConflict of interests\nThe 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.\nAdditional information\nPublisher's Note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\nRights and permissions\nSpringer 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.\nAbout this article\nCite this article\nSaadati, 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\nReceived:\nAccepted:\nPublished:\nVersion of record:\nDOI: https://doi.org/10.1007/s11701-024-02139-7","source_license":"CC0","license_restricted":false}