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Purpose <p>Fluorescence imaging is critical for intraoperative intestinal perfusion assessment in colorectal surgery, yet its clinical adoption remains limited by subjective interpretation and lack of quantitative standards. This study introduces an integrated approach combining fluorescence curve analysis, biophysical modeling, and machine learning to improve intraoperative perfusion assessment.</p> Methods <p>Laparoscopic fluorescence videos from 68 low rectal cancer patients were analyzed, with 1,263 measurement points (15–20 per case) selected along colonic bands. Fluorescence intensity dynamics were extracted via color space transformation, video stabilization and image registration, then modeled using the Random Sample Consensus (RANSAC) algorithm and nonlinear least squares fitting to derive biophysical parameters. Three clinicians independently scored perfusion quality (0–100 scale) using morphological features and biophysical metrics. An XGBoost model was trained on these parameters to automate scoring.</p> Results <p>The model achieved superior test performance, with a root mean square error (RMSE) of 2.47, a mean absolute error (MAE) of 1.99, and an R<sup>2</sup> of 97.2%, outperforming conventional time-factor analyses. It demonstrated robust generalizability, showing no statistically significant prediction differences across age, diabetes, or smoking subgroups (P &gt; 0.05). Clinically, low perfusion scores in distal anastomotic regions were significantly associated with postoperative complications (P &lt; 0.001), validating the scoring system’s clinical relevance and discriminative capacity. The automated software we developed completed analyses within 2&#xa0;min, enabling rapid intraoperative assessment.</p> Conclusion <p>This framework synergistically enhances surgical evaluation through three innovations: (1) Biophysical modeling quantifies perfusion dynamics beyond time-based parameters; (2) Machine learning integrates multimodal data for surgeon-level accuracy; (3) Automated workflow enables practical clinical translation. By addressing limitations of visual assessment through quantitative, rapid, and generalizable analysis, this method advances intraoperative perfusion monitoring and decision-making in colorectal surgery.</p>

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Biophysical modeling and artificial intelligence for quantitative assessment of anastomotic blood supply in laparoscopic low anterior rectal resection

  • 期刊论文

  • 发表日期：

    2025年4月14日

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  • Weizhen He,
  • Haoran Zhu,
  • Xionghui Rao,
  • Qinzhu Yang,
  • Huixing Luo,
  • Xiaobin Wu,
  • Yi Gao

摘要

Purpose

Fluorescence imaging is critical for intraoperative intestinal perfusion assessment in colorectal surgery, yet its clinical adoption remains limited by subjective interpretation and lack of quantitative standards. This study introduces an integrated approach combining fluorescence curve analysis, biophysical modeling, and machine learning to improve intraoperative perfusion assessment.

Methods

Laparoscopic fluorescence videos from 68 low rectal cancer patients were analyzed, with 1,263 measurement points (15–20 per case) selected along colonic bands. Fluorescence intensity dynamics were extracted via color space transformation, video stabilization and image registration, then modeled using the Random Sample Consensus (RANSAC) algorithm and nonlinear least squares fitting to derive biophysical parameters. Three clinicians independently scored perfusion quality (0–100 scale) using morphological features and biophysical metrics. An XGBoost model was trained on these parameters to automate scoring.

Results

The model achieved superior test performance, with a root mean square error (RMSE) of 2.47, a mean absolute error (MAE) of 1.99, and an R2 of 97.2%, outperforming conventional time-factor analyses. It demonstrated robust generalizability, showing no statistically significant prediction differences across age, diabetes, or smoking subgroups (P > 0.05). Clinically, low perfusion scores in distal anastomotic regions were significantly associated with postoperative complications (P < 0.001), validating the scoring system’s clinical relevance and discriminative capacity. The automated software we developed completed analyses within 2 min, enabling rapid intraoperative assessment.

Conclusion

This framework synergistically enhances surgical evaluation through three innovations: (1) Biophysical modeling quantifies perfusion dynamics beyond time-based parameters; (2) Machine learning integrates multimodal data for surgeon-level accuracy; (3) Automated workflow enables practical clinical translation. By addressing limitations of visual assessment through quantitative, rapid, and generalizable analysis, this method advances intraoperative perfusion monitoring and decision-making in colorectal surgery.