ArticleAnnals of surgical oncology2026
Artificial-Intelligence-based Surgical Phase Recognition in Robot-Assisted Radical Prostatectomy and Cross-Surgeon Validation.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- A bibliometric analysis of the robotic revolution in prostate cancer surgery: evolution, hotspots, and future directions.Journal of robotic surgery · 2026Article
- Automated recognition of surgical phases in robot-assisted radical prostatectomy using a Hiera-based vision transformer.BJU international · 2026Article
- Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026Review
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Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) has shown potential in various fields; however, its practical application in surgery remains limited. We developed an AI system capable of automatically recognizing surgical phases in robot-assisted radical prostatectomy (RARP) and confirmed its accuracy through cross-surgeon validation. MATERIALS AND
methodsWe analyzed clinical data from 102 patients who underwent RARP, including 81 consecutive patients operated on by one surgeon (surgeon A) and 21 operated on by five other surgeons (surgeons B-F). In total, 65 of the 81 patients were used for AI development, while the remaining 16, in addition to the 21 patients operated on by surgeons B-F, were used for AI validation. We classified surgical operations into nine phases. Well-trained surgeons annotated the time corresponding to each surgical phase for each video. We used Temporal Convolutional Networks for the Operating Room (TeCNO) to develop the AI model and evaluated its precision.
resultsIn AI development, 919,231 frames were utilized. Testing involved 216,357 frames from surgeon A and 249,553 frames from surgeons B-F. When the developed AI was used to analyze surgical videos from surgeon A, precision reached 0.94. In contrast, when the AI was applied to videos from surgeons B-F, precision was 0.83.
conclusionsThe AI we developed not only showed high accuracy, but also demonstrated generalizability across different surgeons. By comprehensively evaluating surgical videos, our AI may be used to assess the quality of surgeries, thereby providing valuable feedback to surgeons and enhancing the effectiveness of surgical education.
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