Artificial intelligence in gastrointestinal surgery: A systematic review

Table 4 Artificial intelligence applications for postoperative monitoring and complication prediction in gastrointestinal care

Ref.	Complication targeted	AI method/model	Timing of prediction	Data source/type	Performance metrics	Clinical utility
van de Sande et al[31], 2022	Need for hospital specific interventions (e.g., reoperation, radiological intervention, IV antibiotics)	Random forest (4 variants tested)	After second postoperative day	EHR data from 3 non-academic hospitals; 18 perioperative variables (e.g., age, BMI, ASA, meds, surgery time)	AUROC: 0.83, sensitivity: 77.9%, specificity: 79.2%, PPV: 61.6%, NPV: 89.3%	Supports early safe discharge and capacity management
Choi et al[32], 2024	Missed small bowel lesions in negative capsule endoscopy	CNN	After initial human reading of SBCE videos	103 negative SBCE videos retrospectively reanalyzed; images from two academic hospitals	CNN detected additional lesions in 61.2% of cases; model had > 96% accuracy in prior study (AUROC = 0.9957)	Reduces diagnostic oversight; changed diagnosis in 10.3%
Blum et al[18], 2024	Choledocholithiasis	Logistic regression, random forest, XGBoost, KNN, ensemble	Before MRCP, using pre intervention data	Retrospective data from 222 patients (clinical, biochemical, imaging variables) from Royal Hobart Hospital	Ensemble & random forest model (accuracy: 0.81, AUROC: 0.83, sensitivity: 0.94, specificity: 0.69, F1: 0.82)	Avoids unnecessary MRCP, triages patients for ERCP
Haak et al[33], 2022	Incomplete response after CRT in rectal cancer	CNNs (EfficientNet B2, Xception, etc.); FFN for clinical data; combined model	After chemoradiation, pre surgery	226 patients; 731 endoscopic images; clinical features from single institute retrospective cohort	EfficientNet B2-AUC: 0.83, accuracy: 0.75, sensitivity: 0.77, specificity: 0.75, PPV: 0.74, NPV: 0.77	Identifies candidates for non-surgical follow up
Noar and Khan[34], 2023	GP	AI derived GMAT threshold using multivariate regression	Pre-treatment (based on GMA from EGG + WLST)	30 patients with GP; GMA via EGG; WLST; gastric emptying tests; symptom scores (GCSI DD, Leeds)	Sensitivity: 96%, specificity: 75%, accuracy: 93%, AUC: 0.95 for GMAT ≥ 0.59	Guides selection for balloon dilation; personalized therapy

AI: Artificial intelligence; IV: Intravenous; EHR: Electronic health record; BMI: Body mass index; ASA: American Society of Anesthesiologists; AUROC: Area under the receiver operating characteristic curve; PPV: Positive predictive value; NPV: Negative predictive value; CNN: Convolutional neural network; SBCE: Small bowel capsule endoscopy; XGBoost: Extreme gradient boosting; KNN: K-nearest neighbors; MRCP: Magnetic resonance cholangiopancreatography; ERCP: Endoscopic retrograde cholangiopancreatography; CRT: Chemoradiotherapy; FFN: Feed forward neural network; AUC: Area under the curve; GP: Gastroparesis; GMAT: Gastric myoelectric activity threshold; EGG: Electrogastrography; WLST: Water loaded stomach test; GCSI DD: Gastroparesis cardinal symptom index-daily diary.