Artificial intelligence and machine learning in transplantation surgery care pathway

Table 1 Summary of artificial intelligence and machine learning studies mapped to the transplantation surgery care pathway, organised by operative phase

Ref.	Application/focus	Key findings	Limitations & future directions
Preoperative imaging & planning
Oh et al[1]	DL model for automated 3D biliary segmentation (MRCP) in LDLT donors	DSC = 0.80 ± 0.20; reduced manual workload; inferred missing duct segments improving clarity for CHD/hilum	Trained on homogeneous cohort (young, ideal BMI); needs validation in diverse populations and clinical integration
Park et al[2]	DL-assisted CT volumetry for graft weight estimation	CCC = 0.834 vs actual weights; mean processing time 18 minutes; nearly perfect inter-reader agreement (CCC = 0.998)	Minor underestimation in male/high-BMI donors; focused only on right-lobe grafts; external validation required
Machry et al[3]	Review of AI volumetric tools in LDLT	AI platforms (e.g., “Dr. Liver”) reduced volumetry time (38 minutes → 7 minutes); improved accuracy (error 3.1% vs > 10% manual)	Limited evidence for left lobe/paediatric grafts; requires broader adoption and workflow integration
Risk stratification & functional status
Brennan et al[4]	MySurgeryRisk ML algorithm vs physician judgment	Improved AUC; reduced under/overestimation of complications; transparent UI improved trust	Single-centre study; prospective validation ongoing
Chu et al[5]; Thongprayoon et al[6]	ML analysis of KPS in > 224000 KT recipients	Post-KT functional improvement approximately 0.89%/year; low KPS strongly predicted mortality and graft loss	KPS predictive validity (C-statistic approximately 0.60) < frailty/performance measures (approximately 0.75); annual pre-KT screening advised
	Unsupervised ML clustering of low KPS (≤ 40%)	Cluster 2 (younger, more living donors, fewer HLA mismatches) showed superior 5-year outcomes vs cluster 1	Highlights systemic inequities; challenges exclusion of low-functioning candidates; needs socio-clinical integration
Lyden et al[7]	Statistical best practices for ML urgency scoring	Advocates time-dependent covariates, cause-specific hazard models, proper handling of waitlist mortality	Requires rigorous adherence to statistical standards to avoid immortal time bias and misclassification
Prehabilitation & surgical eligibility
Krivov et al[8]	¹H NMR metabolomic tracking for kidney transplant recovery	Differentiated primary vs delayed graft function by POD 2; diffusion framework robust against overfitting	Limited resolution for acute rejection; requires enhanced temporal resolution
Jung et al[9]	ML model for 90-day graft failure in paediatric LT	AUROC = 0.898; key predictors: Encephalopathy, sodium, bilirubin, vascular thrombosis; enabled perioperative stratification	Retrospective, single-centre; requires multicentre validation
Law and Kow[10]	Risk factors for SFSS in LDLT	Donor age > 45, macrosteatosis > 10%, high portal venous pressure = higher risk; GRWR ≥ 0.8%, GV/SLV ≥ 40% protective	BMI not predictive unless with steatosis; AI volumetry needed for accurate planning
Prediction of intraoperative complications
Chen et al[11]	CatBoost ML model for massive transfusion in LT	Outperformed logistic regression/XGBoost; key predictors: Hb, Hct, fibrinogen, platelets	Lower specificity in prospective data; excluded intraoperative variables; multicentre validation needed
Eyth et al[12]	Manually calculable risk score for RBC transfusions	Incorporated Hb ≤ 7.5 g/dL, ASA, urgency, hypoalbuminaemia, thrombocytopenia; aids planning & resource allocation	Not AI-driven; performance may vary across surgeries; requires validation in broader LT cohorts
Park et al[13]	ML risk score for intraoperative haemorrhage	Predictors: MELD, aPTT, creatinine, MAP, pulse pressure, temperature; PT/INR not predictive	Retrospective study; prospective validation required
Zhang et al[14]	GBM + SHAP for intraoperative AKI risk	Strong predictors: Urine output (approximately 2.2 mL/kg/hour), anaesthesia time, platelet count; SHAP confirmed validity	Single-centre; external validation needed
Liu et al[15]	GBM + SHAP for AKI risk in DCD grafts	Predictors: Indirect bilirubin, urine output, anaesthesia time, platelets, steatosis; superior to other ML models	No external validation; lacked AKI staging
Lee et al[16]	GBM model for AKI in LT	Key predictors: Cold ischaemic time, SvO2; online calculator developed	Prospective testing pending
Bredt et al[17]	ANN vs logistic regression for post-LT AKI	ANN outperformed LR; predictors: MELD, marginal grafts, hypotension, transfusion, perfusion	Retrospective, single-centre; limited interpretability/generalisation
Robotic & ML-enhanced surgical techniques
Martucci et al[18]	Da Vinci robotic hepatectomy in LT	Enabled precision, 3D vision, EndoWrist dexterity; moderate blood loss; feasible anaesthetic optimisation	High cost, training requirements, physiological challenges (e.g., pneumoperitoneum, hypothermia)
Territo et al[19]	RAKT vs OKT	Lower blood loss, pain, wound complications; equivalent renal outcomes despite longer ischaemia times	Higher operative time; requires technical expertise
Slagter et al[20]	RAKT in obese recipients	Lower SSI, fewer lymphoceles, shorter hospital stay; learning curve approximately 21-35 cases	High cost; contraindicated in calcified iliac arteries; limited to selected patients
Karadag et al[21]	RAKT vs OKT	Less bleeding, fewer complications, shorter hospital stays; longer ischaemia/anastomosis times	Requires experienced centres; standardisation needed
Khajeh et al[22]	RADN vs LDN	Better outcomes in operation time, complications, and recovery after learning curve; slightly more blood loss	Surgeon experience crucial; need standardised definition of ‘experience’
Giulianotti et al[23]	Robotic surgery in obese transplant patients	Reduced incision size, trauma, wound complications; expanded surgical feasibility	High cost, warm ischaemia risks; requires long-term data
Mulloy et al[24]	Robotic transplant nephrectomy	Lower blood loss, infections, hospital stays; minimally invasive alternative	No FDA-approved robotic staplers; higher costs, longer operative times
Predicting postoperative complications & graft function
Chen et al[27]	ML (random forest) for sepsis after LT	Identified 8 risk factors; RF outperformed SOFA (AUC 0.731); online calculator developed	Retrospective, single-centre; moderate sensitivity; excluded pre-operative sepsis
Luo et al[28]	Random forest for pneumonia after KT	AUROC 0.91; predictors: Pulmonary lesions, reoperation, rATG dose, albumin, Ig, DGF; guided escalation protocol	Small, single-centre; retrospective; class imbalance
Tan et al[29]	Nomogram for paediatric LDLT graft recovery	DRHF + 4 predictors (donor liver insufficiency, ischaemia time, creatinine, bilirubin); AUROC 0.898	Single-centre, small sample; requires multicentre validation
Longitudinal monitoring of recovery
Wan et al[30]	Recipient-to-donor eGFR ratio	RFR ≥ 1 Linked to 45% lower 10-year graft failure; optimal 1-2; outside range = inadequate recovery/hyperfiltration	Single-centre; small sample; multicentre validation needed
Campbell et al[31]	Predictors of renal recovery post-OLT	Duration of renal dysfunction > 3.6 weeks predicted poor 12-month renal function (AUC 0.71); CKLT showed better recovery	Creatinine-based GFR limits; retrospective
Levitsky et al[32]	Native vs graft GFR in SLK	Radionuclide scans improved discrimination; abnormal renal imaging predicted poor recovery; exploratory biomarkers proposed	UNOS criteria insufficient; biomarkers unvalidated; small sample
Functionality & rehabilitation
Park et al[33]	Analgesic impact (ITMB) in living kidney donors	ITMB reduced delayed kidney recovery by 74.3%; protective via haemodynamic stabilisation; fluid balance also critical	Mechanism indirect; limited validation; demographic disparities noted
Predictive models of outcomes & survival
Molinari et al[34]	Mortality risk model in cadaveric LT	C-statistic up to 0.95 for 90-day mortality; excludes sex/diagnosis to reduce bias	Retrospective; lacks frailty/nutrition data; needs validation
Sliwinski et al[35]	Prehab App risk calculator	Simple metrics predicted 90-day survival; < 5 minutes use; supports personalised prehabilitation strategies	Single-centre; excluded cardiac/orthopaedic cases; AI-enhancements planned
Imaging-based AI applications
Felli et al[36]	AI-enhanced hyperspectral imaging for liver viability during HAO	Accurate tissue oxygenation mapping; strong correlation with lactates & Suzuki score; CNN: Sensitivity 0.993, specificity 0.997	Shallow penetration (3-5 mm); no real-time video; limited ischaemia discrimination; future HYPER-reality integration proposed
Marsh et al[37]	Deep learning for glomerulosclerosis quantification in kidney biopsies	Fully convolutional model reduced processing time 22-fold; improved pixel-wise accuracy; reduced inter-observer variability	Misclassification near vessels/cysts; requires larger datasets and refined post-processing
Multiorgan & multidisciplinary applications
Rashidi and Bihorac[38]	RNNs for AKI prediction using EHR and intraoperative data	Predicted AKI up to 48 hours pre-onset; dynamic recalibration with evolving physiology; integrated intra-operative signals for continuous risk	Needs broader validation; integration into perioperative workflows still limited
	CNNs for CKD stage classification via ultrasound	Non-invasive eGFR and CKD staging; reduced reliance on serum creatinine; radiation-free monitoring	Requires validation across diverse populations; long-term performance unknown
	CNNs for renal histopathology segmentation	Automated quantification of glomeruli, tubules, interstitium, vasculature; supports rejection/fibrosis grading	Risk of variability with rare morphologies; dataset standardisation required

AKI: Acute kidney injury; ANN: Artificial neural network; AUC: Area under the curve; AUROC: Area under the receiver-operating characteristic curve; CCC: Concordance correlation coefficient; CKLT: Combined kidney-liver transplant; DCD: Donation after circulatory death; DGF: Delayed graft function; DSC: Dice similarity coefficient; GBM: Gradient boosting machine; GFR: Glomerular filtration rate; GRWR: Graft-to-recipient weight ratio; GV: Graft volume; SLV: Standard liver volume; HAO: Hepatic artery occlusion; KPS: Karnofsky Performance Score; KT: Kidney transplantation; LDLT: Living-donor liver transplantation; LT: Liver transplantation; ML: Machine learning; NMR: Nuclear magnetic resonance; OKT: Open kidney transplantation; POD: Postoperative day; RAKT: Robot-assisted kidney transplantation; RADN: Robot-assisted donor nephrectomy; RFR: Recipient-to-donor functional ratio; RF: Random forest; RNN: Recurrent neural network; SFSS: Small-for-size syndrome; SHAP: Shapley additive explanations; SLK: Simultaneous liver-kidney transplant; SSI: Surgical site infection; MRCP: Magnetic resonance cholangiopancreatography; DL: Deep learning; CHD: Common hepatic duct; BMI: Body mass index; CT: Computed tomography; AI: Artificial intelligence; UI: User interface; XGBoost: Extreme gradient boosting; Hb: Haemoglobin; ASA: American Society of Anesthesiologists; RBC: Red blood cells; MELD: Model for end-stage liver disease; aPTT: Activated partial thromboplastin time; MAP: Mean arterial pressure; PT: Prothrombin time; INR: International normalized ratio; FDA: Food and Drug Administration; SOFA: Sequential organ failure assessment; rATG: Rabbit anti-thymocyte globulin; DRHF: Dynamic ratio of hepatic function; OLT: Orthotopic liver transplantation; ITMB: Intrathecal morphine block; CNN: Convolutional neural network; EHR: Electronic health record.

Table 2 Consolidated future directions for artificial intelligence and machine learning research across the transplantation surgery care pathway, organised by domain

Domain	Future directions
Preoperative imaging & planning	Multicentre validation, inclusion of diverse donor/recipient populations, integration of AI tools into clinical workflows, expansion to paediatric and left-lobe graft planning
Risk stratification & functional status	Prospective multicentre validation, integration of objective frailty and performance measures, incorporation of socio-clinical context into models
Prehabilitation & surgical eligibility	Broader external validation, development of precision prehabilitation programmes, incorporation of metabolic biomarkers, and standardised eligibility frameworks
Perioperative risk prediction	Incorporation of intraoperative real-time signals, multicentre prospective trials, explainability-focused models for clinical adoption
Robotic & ML-enhanced surgery	Cost-effectiveness studies, technological refinement, definition of training standards, long-term comparative outcome studies
Postoperative monitoring	Large-scale validation, integration with real-time EHR systems, multimodal models combining clinical, imaging, and biomarker data
Longitudinal recovery & rehabilitation	Validation of composite recovery biomarkers, mechanistic studies on perioperative interventions, personalised rehabilitation protocols
Cross-cutting imaging & pathology	Development of real-time video-capable imaging, dataset expansion, integration of AI histopathology into diagnostic standards
Multiorgan & multidisciplinary models	Multimodal, cross-organ models; integration into perioperative and critical care decision-support systems; dataset standardisation across institutions
Ethical & regulatory frameworks	Development of ethical governance frameworks, bias audits, and explainable AI standards to guide deployment in transplantation

EHR: Electronic health record; ML: Machine learning; AI: Artificial intelligence.