Application field
Laparoscopic surgery for colorectal cancer
Technology
Artificial Intelligence (AI), Computer Vision, Data Analysis
Goals
Development of artificial intelligence-based analysis method in indocyanine green (ICG) angiography to perform real-time perfusion analysis:
- Identification of the optimal line for cancer resection
- Assessment of the goodness of the vascularization
The AI techniques applied to near-infrared fluorescence angiography with indocyanine green is proposed as objective method to support surgeons in their decision-making processes, improving treatment and post-operative complications prevention.
Activities
- Identification of appropriate tracker algorithms to enable fast and accurate tracking of the
selected region of interest.
- Data pre-processing (e.g., appropriate color space for analysis) to make it suitable for the machine learning system.
- Design and implementation of AI systems for the evaluation of bowel perfusion during colorectal surgery.
Challenges
- Improving the accuracy of the system using temporal aspect and variation of ICG fluorescence intensity over time.
- Developing AI systems for the prediction of the optimal resection line in laparoscopic surgery for colorectal cancer.
- Developing algorithms on portable devices with reduced computational power and memory capacity.
Bibliography
- Arpaia, P., Bracale, U., Corcione, F., De Benedetto, E., Di Bernardo, A., Di Capua, V., Duraccio, L., Peltrini, R. and Prevete, R., 2022. Assessment of blood perfusion quality in laparoscopic colorectal surgery by means of Machine Learning. Scientific Reports, 12(1), pp.1-9.