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Related Experiment Video

Updated: Apr 21, 2026

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Deep learning for fluorescence confocal microscopy image interpretation in radical prostatectomy.

Lixiang Fang1, Nikhil Mayor2,3, Alexander Light2,3

  • 1Division of Surgery, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.

BJU International
|April 19, 2026
PubMed
Summary

A deep learning model was developed to interpret fluorescence confocal microscopy (FCM) images for surgical margin assessment during radical prostatectomy (RP). This AI tool shows strong performance and generalizability, potentially reducing the need for intraoperative pathology support.

Keywords:
artificial intelligencedeep learningfluorescence confocal microscopyintraoperative margin analysispositive surgical marginsprostate cancerradical prostatectomyrobotic surgery

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Surgical Pathology

Background:

  • Intraoperative surgical margin assessment is critical during radical prostatectomy (RP) to ensure complete tumor removal.
  • Current methods can be time-consuming, potentially delaying treatment decisions.
  • Fluorescence confocal microscopy (FCM) offers real-time imaging capabilities for tissue analysis.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for interpreting FCM images.
  • To enable automated intraoperative surgical margin assessment during RP.
  • To improve the efficiency and accuracy of margin evaluation in prostate cancer surgery.

Main Methods:

  • A convolutional neural network (CNN) model was trained and tested using FCM images from the IP8-FLUORESCE study.
  • The model incorporated advanced techniques like focal loss, dropout regularization, and adaptive class weighting to handle class imbalance.
  • External validation was performed using data from the LaserSAFE feasibility trial, with performance assessed by sensitivity, specificity, and AUC.

Main Results:

  • The DL model achieved high diagnostic performance on internal testing (AUC 0.93) and demonstrated good generalizability in external validation (AUC 0.83).
  • Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed the model's focus on relevant malignant structures.
  • A custom graphical user interface (GUI) allowed for rapid, interpretable predictions in under 2 seconds.

Conclusions:

  • A validated DL model can accurately interpret FCM images for intraoperative margin assessment in RP.
  • This automated approach offers a scalable solution for real-time margin evaluation.
  • The technology has the potential to decrease reliance on traditional intraoperative pathology services.