Background and Aims: Liver transplant recipients (LTRs) are at risk of developing graft injury, leading to cirrhosis and reduced survival. Liver biopsy remains the gold standard method for the diagnosis of graft pathology but is invasive and risky. Our study aimed to develop a novel hybrid multi-class neural network (NN) model GraftIQ integrating clinician expertise for non-invasive diagnosis of graft pathology. Methods: Graft injury diagnosis was based on liver biopsies from LTRs (1992-2020). Demographic, clinical, and laboratory data from the 30 days before biopsy were used to train a multi-class NN model to classify biopsies into six categories. The dataset was split into 70% training and 30% test sets, with external validation on additional biopsies from 2020-2024. To enhance predictive capabilities, clinician expertise was integrated with neural network predictions using Bayesian fusion to combine clinician-provided probabilities with data-driven outcomes. Results: Our dataset comprises 5,217 biopsies categorized into six graft etiology groups. In response to findings from expert versus machine implementation analysis, Bayesian fusion of clinical expertise and NN predictions enhanced predictive performance. GraftIQ (MulticlassNN + clinical insight) achieved an overall AUC of 0.902 (95% CI: 0.884, 0.919), improving from an AUC of 0.885 using the NN alone. Robustness validated through 10-fold internal cross-validation and external validation, showed AUC improvements of 10-16% compared to conventional machine learning approaches. Conclusion: Our multi-class neural network model demonstrates high accuracy in predicting common causes of graft pathology. Through the integration of clinician expertise, we observed an improvement in its performance, affirming the effectiveness of GraftIQ as a valuable clinical decision support tool.

The authors have declared no competing interest.

This study was funded by a Canadian Society of Transplantation grant, American Society of Transplant (AST) grant, Canadian Institutes of Health Research's (CIHR) grant to MB. Grants not specifically for this unfunded study. The content is solely the responsibility of the author. This study was not funded by industry.

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Ethics committee/IRB of University Health Network gave ethical approval for this work. This study was approved by the Research Ethics Board at UHN (REB study # 21-6170). Since data was retrieved from medical records, exemption from informed consent was granted by the REB committee.

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