Nomographic predictive models for complications after minimally invasive esophagectomy: Current status and future perspectives

Table 6 Currently available risk prediction models developed using artificial intelligence and machine learning for predicting esophagectomy related complications

Ref.	Study size	Study design	Objective	Results
Bolourani et al[97], 2021	2037	Retrospective	ML-based prediction model for early readmissions within 30 days following esophagectomy	ML model for clinical decision AUC: 0.72
				ML model for quality review of esophagectomy AUC: 0.74
van Kooten et al[98], 2022	4288	Retrospective	ML methods for predicting postoperative complications following esophagectomy and development of a predictive model for anastomotic leakage and cardiopulmonary complications	The AUC of 0.619 for anastomotic leakage and 0.644 for pulmonary complications
Jung et al[99], 2023	604	Retrospective	ML-based methods for predicting Clavien–Dindo grade IIIa or greater complications following esophagectomy	The AUC of neural network was 0.672 for overall Clavien-Dindo grade IIIa or higher morbidity, 0.695 for medical complications, and 0.653 for surgical complications
Klontzas et al[55], 2024	471	Retrospective	ML model combining CT and clinical variables for predicting anastomotic leakage following esophagectomy	The model achieved an AUC of 0.792

ML: Machine learning; AUC: Area under the curve for receiver operating characteristic curve; CT: Computed tomography.