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Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy.

Enrico Checcucci1, Pietro Piazzolla2, Giorgia Marullo3

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This study introduces BLAIR, an AI system using convolutional neural networks to predict intraoperative bleeding during robotic prostatectomy. BLAIR achieved over 90% accuracy, assisting surgeons by identifying bleeding risks.

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Area of Science:

  • Robotic Surgery
  • Artificial Intelligence in Medicine
  • Surgical Technology

Background:

  • Intraoperative bleeding is a significant challenge in robotic surgery.
  • Developing AI solutions for real-time bleeding detection is crucial.
  • This study focuses on robot-assisted radical prostatectomy (RARP).

Purpose of the Study:

  • To develop an AI system for forecasting intraoperative bleeding during RARP.
  • To create a system that alerts surgeons to bleeding risks.
  • To leverage convolutional neural networks (CNNs) for bleeding detection.

Main Methods:

  • A multi-task learning (MTL) CNN based on U-Net architecture was employed.
  • Bleeding Artificial Intelligence-based Detector (BLAIR) software was developed using Python Keras and PyQT.
  • Clinical assessment compared BLAIR's performance against a urologist, using Multiple Correspondence Analysis (MCA).

Main Results:

  • The MTL-CNN achieved 90.63% accuracy in event recognition.
  • BLAIR demonstrated similar bleeding identification performance to human experts.
  • BLAIR provided faster response times in predicting bleeding incidents.

Conclusions:

  • BLAIR software accurately predicts bleeding events during RARP with over 90% accuracy.
  • AI shows significant potential to aid surgeons during robotic interventions.
  • This study highlights the positive impact of AI in enhancing surgical procedures.