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<p>Fine-grained analysis of intraoperative behavior and its impact on patient outcomes remains a longstanding challenge. We present Frame-to-Outcome (F2O), an end-to-end system that translates tissue dissection videos into gesture sequences and uncovers patterns associated with postoperative outcomes. Leveraging transformer-based spatial and temporal modeling and frame-wise classification, F2O robustly detects consecutive short (˜2 s) gestures in the nerve-sparing step of robot-assisted radical prostatectomy (AUC: 0.80 frame-level; 0.81 video-level). F2O-derived features—gesture frequency, duration, and transitions—predicted postoperative outcomes with accuracy comparable to human annotations (0.79 vs. 0.75; overlapping 95% CI). Across 25 shared features, effect size directions were concordant with small differences (<b>∆</b><i>d</i><sub>avg</sub> ≈ <b>0</b>.<b>07</b>), and strong correlation (<i>r</i> = <b>0</b>.<b>96</b>, <i>p</i> &lt; <b>1</b> <i>×</i> <b>10</b><sup><i>−</i><b>14</b></sup>). F2O also captured key patterns linked to erectile function recovery, including prolonged tissue peeling and reduced energy use. By enabling automatic interpretable assessment, F2O establishes a foundation for data-driven surgical feedback and prospective clinical decision support.</p>

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End to end AI system for surgical gesture sequence recognition and clinical outcome prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Xi Li,
  • Nicholas Matsumoto,
  • Ujjwal Pasupulety,
  • Atharva Deo,
  • Cherine Yang,
  • Jay Moran,
  • Miguel E. Hernandez,
  • Peter Wager,
  • Jasmine Lin,
  • Jeanine Kim,
  • Alvin C. Goh,
  • Christian Wagner,
  • Geoffrey A. Sonn,
  • Andrew J. Hung

摘要

Fine-grained analysis of intraoperative behavior and its impact on patient outcomes remains a longstanding challenge. We present Frame-to-Outcome (F2O), an end-to-end system that translates tissue dissection videos into gesture sequences and uncovers patterns associated with postoperative outcomes. Leveraging transformer-based spatial and temporal modeling and frame-wise classification, F2O robustly detects consecutive short (˜2 s) gestures in the nerve-sparing step of robot-assisted radical prostatectomy (AUC: 0.80 frame-level; 0.81 video-level). F2O-derived features—gesture frequency, duration, and transitions—predicted postoperative outcomes with accuracy comparable to human annotations (0.79 vs. 0.75; overlapping 95% CI). Across 25 shared features, effect size directions were concordant with small differences (∆davg ≈ 0.07), and strong correlation (r = 0.96, p < 1 × 10−14). F2O also captured key patterns linked to erectile function recovery, including prolonged tissue peeling and reduced energy use. By enabling automatic interpretable assessment, F2O establishes a foundation for data-driven surgical feedback and prospective clinical decision support.