Recent advancements of artificial intelligence in minimally invasive surgery for endometriosis

Ting Zhao, Yuhong Liu, Zhiyun Wei, Xiaofang Yi
In: Intelligent Surgery · 2025 · vol. 8 , pp. 118–124 · doi:10.1016/j.isurg.2025.10.001 · W4415218856
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Abstract

The integration of artificial intelligence (AI) into minimally invasive surgery (MIS) for endometriosis represents a transformative advancement in clinical practice, with the potential to improve preoperative diagnosis, intraoperative guidance, and postoperative outcomes. This review synthesizes recent literature to evaluate the applications of AI across various domains including imaging modalities, biomarker interpretation, and surgical navigation etc. The performance of machine learning (ML) and deep learning (DL) algorithms has been assessed. Notably, AI-augmented imaging techniques, in particular, significantly improved the detection of ovarian endometriomas, achieving an area under the curve (AUC) of up to 0.997, while deep endometriosis detection yielded AUC values of 0.800–0.878. Beyond diagnosis, AI demonstrates promise in enhancing surgical precision through real-time lesion recognition and identification of critical anatomical structures. Nevertheless, the development of fully or semi-automated surgical procedures is still in the conceptual validation stage and remains a long-term goal for future investigations. Current findings suggest that AI could substantially enhance both diagnostic and therapeutic outcomes in MIS for endometriosis, although most studies remain at proof-of-concept stages and challenges exist. Future research should prioritize multicenter prospective trials with external validation to ensure reproducibility and generalizability across diverse populations and clinical settings.

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endometriosis

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Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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