Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections.
Florian LippenbergerSebastian ZiegelmayerMaximilian BerletHubertus FeussnerMarcus MakowskiPhilipp-Alexander NeumannMarkus GrafGeorgios KaissisDirk WilhelmRickmer BrarenStefan ReischlPublished in: International journal of colorectal disease (2024)
A Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.
Keyphrases
- minimally invasive
- robot assisted
- coronary artery bypass
- climate change
- high resolution
- surgical site infection
- computed tomography
- liver metastases
- deep learning
- electronic health record
- case report
- big data
- contrast enhanced
- machine learning
- percutaneous coronary intervention
- atrial fibrillation
- magnetic resonance
- dual energy
- mass spectrometry
- photodynamic therapy
- data analysis
- pet ct
- laparoscopic surgery