#AI #Transplantation
#MachineLearning #GraftRejection
Solid organ
transplantation is currently the gold standard treatment for end-stage organ
failure. However, numerous challenges remain, including organ shortages,
complex preoperative assessments, and the risk of chronic rejection. In this
context, artificial intelligence (AI)—and more specifically, machine learning
(ML)—is drawing increasing interest from clinicians and researchers.
This literature review
explores the latest advances in AI applied to transplantation, focusing on
three major areas: pre-transplant assessment, rejection diagnosis, and
personalized postoperative monitoring. The promise? To transform transplant
medicine into a more predictive, objective, and personalized field.
From segmentation
to predictive scoring: AI before transplantation
Pre-transplant
evaluation still often relies on subjective exams and expert judgment. To make
these steps more reliable, AI models have been developed. One example: an
automated kidney segmentation algorithm based on CT scan data showed near-human
accuracy (Dice coefficients of 0.94 for the cortex).
In lung transplants,
another algorithm using “dictionary learning” was able to predict graft
viability with 85% accuracy based on CT images. Other models, such as XGBoost,
can predict immediate graft function (IGF), thereby optimizing recipient
selection.
An AI model based on
electrocardiographic age (ECG age) also proves useful for assessing patients’
cardiovascular frailty. The greater the gap with chronological age, the higher
the mortality risk—hazard ratio of 3.59 per 10-year increment.
Read next: Oncology: When AI Takes Control…
Diagnosing
rejection objectively
Histological diagnosis
remains the cornerstone of graft rejection detection. Yet the complexity of
classification systems—especially Banff—leads to problematic interobserver
variability. To address this, the Banff Automation System offers an automated
analysis tool integrating slides, lesion scores, and molecular data. The
result? A more accurate reclassification of numerous cases and improved graft
survival prediction.
Other models, such as
those from Zhang’s lab or the RejectClass score, use whole-slide image analysis
to anticipate graft loss. Fang et al.’s proteomics-based approach distinguished
T-cell mediated rejection (TCMR) with 80% accuracy. The MMDx platform uses
transcriptomic profiles to reclassify renal and even lung transplant biopsies,
showing a stronger correlation with real graft survival outcomes.
Managing
immunosuppression: AI at the bedside of pharmacokinetics
Adjusting doses of
cyclosporine or tacrolimus is complex and requires frequent monitoring. AI
models—based on evolutionary algorithms or neural networks—can predict blood
levels from clinical or genetic data. XGBoost even outperformed traditional
Bayesian methods in accuracy for tacrolimus exposure prediction.
Toward Predictive
Post-Transplant Medicine
How can we anticipate
complications and survival after transplantation? Models such as Random
Survival Forest (RSF) allow for accurate risk stratification. In lung
transplantation, this model differentiated low- and high-risk groups, with life
expectancies of 52.9 months versus 14.8 months.
The ReSOLT model,
developed from the UNOS database, classifies liver transplant patients into 20
prognostic subgroups, with an AUC of 0.742. Other approaches have targeted the
prediction of postoperative sepsis or hepatic fibrosis using LSTM algorithms. Finally,
an ensemble learning model developed in Ethiopia reached an AUC of 0.88 in
predicting renal graft failure.
A successful data
transplant?
Thanks to artificial
intelligence, organ transplantation is entering the era of precision. From
pre-transplant evaluation to rejection management, immunosuppression, and
prognosis, every step is being redesigned to be more objective, predictive, and
personalized. The tools are powerful but still imperfect: data bias,
heterogeneous clinical practices, and generalization limitations remain
challenges to overcome.
Nevertheless, these
innovations mark the beginning of a profound transformation of transplant
medicine—toward greater equity, safety, and a firm commitment to precision
medicine.
Read next: Porcine liver and human patient: an unprecedented and functional alliance
