Evaluating AIDriven 3D Surgical Models for Laparoscopic Strategy in Complex Endometriosis

IF 3.3 2区医学 Q1 OBSTETRICS & GYNECOLOGY

Journal of minimally invasive gynecology Pub Date : 2025-10-22 DOI:10.1016/j.jmig.2025.09.014

K Thott , D Godbole

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引用次数: 0

Abstract

Study Objective

Posterior compartment endometriosis carries up to 50% reoperation rates due to missed lesions. We evaluated whether AIgenerated 3D surgical models improve operative planning, anatomical validation, and intraoperative preparedness in complex, bowelinvolving disease.

Design

Prospective pilot study using structured surgeonreported outcomes and intraoperative validation across consecutive cases.

Setting

Multisite minimally invasive gynecology programs adopting the XENA surgical intelligence platform.

Patients or Participants

20 consecutive laparoscopic surgeries for suspected or confirmed moderatetosevere endometriosis.

Interventions

Preoperative review of patientspecific, AIgenerated 3D surgical models derived from standard pelvic MRI datasets.

The XENA platform transforms MRI data into operative planning maps tailored for surgical use—providing spatial insight to complement radiologic interpretation.

Measurements and Primary Results

Operating surgeons revised their laparoscopic approach in 9/20 cases (45%) after reviewing the 3D model, predominantly to adjust bowel or ureteric dissection strategy.

• Intraoperative validation confirmed 18/20 models (90%) accurately reflected surgical anatomy, reducing exploratory maneuvers.

• In 12/20 procedures (60%), surgeons noted that 3D models enabled more precise planning of dissection planes around critical pelvic structures.

Conclusion

AIdriven 3D surgical models demonstrate early clinical impact in highcomplexity endometriosis—particularly with bowel involvement—by enhancing planning precision and intraoperative orientation. These pilot data support integration of surgical intelligence platforms like XENA into preop workflows. A multicenter validation study is in protocol development.

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评估人工智能驱动的三维手术模型在腹腔镜治疗复杂子宫内膜异位症中的应用

研究目的后腔室子宫内膜异位症因病变漏诊再手术率高达50%。我们评估了ai生成的3D手术模型是否改善了复杂的、涉及肠道的疾病的手术计划、解剖验证和术中准备。前瞻性先导研究采用结构化的外科医生报告的结果和对连续病例的术中验证。设置采用XENA手术智能平台的多部位微创妇科项目。患者或参与者20例疑似或确诊中重度子宫内膜异位症的连续腹腔镜手术。干预：术前回顾基于标准骨盆MRI数据集的患者特异性、人工生成的3D手术模型。XENA平台将MRI数据转换为适合外科手术使用的手术计划图，提供空间洞察以补充放射学解释。测量结果和主要结果手术外科医生在复查3D模型后修改了9/20例（45%）的腹腔镜入路，主要是调整肠或输尿管清扫策略。术中验证证实18/20模型（90%）准确反映了手术解剖，减少了探索性操作。•在12/20（60%）的手术中，外科医生注意到3D模型可以更精确地规划关键骨盆结构周围的剥离平面。结论人工智能驱动的3D手术模型通过提高计划精度和术中定位，在高复杂性子宫内膜异位症（特别是肠受累）中具有早期临床影响。这些试点数据支持将手术智能平台（如XENA）集成到术前工作流程中。一项多中心验证研究正在制定方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of minimally invasive gynecology 医学-妇产科学

CiteScore

5.00

自引率

7.30%

发文量

272

审稿时长

37 days

期刊介绍： The Journal of Minimally Invasive Gynecology, formerly titled The Journal of the American Association of Gynecologic Laparoscopists, is an international clinical forum for the exchange and dissemination of ideas, findings and techniques relevant to gynecologic endoscopy and other minimally invasive procedures. The Journal, which presents research, clinical opinions and case reports from the brightest minds in gynecologic surgery, is an authoritative source informing practicing physicians of the latest, cutting-edge developments occurring in this emerging field.