Machine-Learning Algorithm
Cross-source consensus on Machine-Learning Algorithm from 1 sources and 5 claims.
1 sources · 5 claims
Uses
How it works
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Highlighted claims
- The AI analysis is lesion-based and uses intraoperative fluorescence images as input. — Indocyanine green fluorescence for intraoperative detection of liver tumours in minimally invasive surgery: protocol for the LIVERGREEN phase IV multicentre clinical trial
- A major trial goal is to develop a machine-learning algorithm that identifies and classifies liver lesions from intraoperative ICG fluorescence images. — Indocyanine green fluorescence for intraoperative detection of liver tumours in minimally invasive surgery: protocol for the LIVERGREEN phase IV multicentre clinical trial
- Algorithm development begins after the first 100 patients are enrolled. — Indocyanine green fluorescence for intraoperative detection of liver tumours in minimally invasive surgery: protocol for the LIVERGREEN phase IV multicentre clinical trial
- Lesions are annotated with intraoperative findings and corresponding histopathological diagnosis as the reference label. — Indocyanine green fluorescence for intraoperative detection of liver tumours in minimally invasive surgery: protocol for the LIVERGREEN phase IV multicentre clinical trial
- Supervised machine-learning methods, including neural networks, are used to identify fluorescence patterns associated with malignant lesions and estimate malignancy probability. — Indocyanine green fluorescence for intraoperative detection of liver tumours in minimally invasive surgery: protocol for the LIVERGREEN phase IV multicentre clinical trial