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
- surgical site infection
- computed tomography
- case report
- deep learning
- electronic health record
- image quality
- insulin resistance
- magnetic resonance imaging
- coronary artery disease
- big data
- skeletal muscle
- metabolic syndrome
- adipose tissue
- positron emission tomography
- dual energy
- magnetic resonance
- fluorescence imaging
- type diabetes
- neural network
- atrial fibrillation