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Development of Bleeding Artificial Intelligence Detector (BLAIR) System for Robotic Radical Prostatectomy
Enrico Checcucci1, Pietro Piazzolla2, Giorgia Marullo3
1Department of Surgery, Candiolo Cancer Institute, FPO-IRCCS, 10060 Turin, Italy.
Journal of Clinical Medicine
|December 9, 2023
Summary
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.
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.
