Author
Listed:
- Divya Sharma
(University Health Network
York University)
- Neta Gotlieb
(University of Ottawa)
- Daljeet Chahal
(University of British Columbia)
- Joseph C. Ahn
(Division of Gastroenterology and Hepatology at Mayo Clinic)
- Bastian Engel
(Hannover Medical School)
- Richard Taubert
(Hannover Medical School)
- Eunice Tan
(National University Hospital)
- Lau Kai Yun
(National University Hospital)
- Sara Naimimohasses
(University Health Network
University Health Network)
- Ankit Ray
(University Health Network)
- Yoojin Han
(University Health Network)
- Sara Gehlaut
(University Health Network)
- Maryam Shojaee
(University Health Network)
- Surabie Sivanendran
(University Health Network)
- Maryam Naghibzadeh
(University Health Network)
- Amirhossein Azhie
(University Health Network)
- Sareh Keshavarzi
(University Health Network)
- Kai Duan
(University Health Network)
- Leslie Lilly
(University Health Network
University Health Network)
- Nazia Selzner
(University Health Network
University Health Network)
- Cynthia Tsien
(University Health Network
University Health Network)
- Elmar Jaeckel
(University Health Network
University Health Network)
- Wei Xu
(University Health Network)
- Mamatha Bhat
(University Health Network
University of Toronto)
Abstract
Liver transplant recipients (LTRs) are at risk of graft injury, leading to cirrhosis and reduced survival. Liver biopsy, the diagnostic gold standard, is invasive and risky. We developed a hybrid multi-class neural network (NN) model, ‘GraftIQ,’ integrating clinician expertise for non-invasive graft pathology diagnosis. Biopsies from LTRs (1992–2020) were classified into six categories using demographic, clinical, and lab data from 30 days pre-biopsy. The dataset (5217 biopsies) was split 70/30 for training/testing, with external validation at Mayo Clinic, Hannover Medical School, and NUHS Singapore. Bayesian fusion was used to combine clinician-derived probabilities with NN predictions, improving performance. Here we show that GraftIQ (MulticlassNN+clinical insight) achieved an AUC of 0.902 (95% CI:0.884–0.919), up from 0.885 with NN alone. Internal and external validation demonstrated 10–16% higher AUC than conventional ML models. GraftIQ demonstrates high accuracy in identifying graft etiologies and offers a valuable clinical decision support tool for LTRs.
Suggested Citation
Divya Sharma & Neta Gotlieb & Daljeet Chahal & Joseph C. Ahn & Bastian Engel & Richard Taubert & Eunice Tan & Lau Kai Yun & Sara Naimimohasses & Ankit Ray & Yoojin Han & Sara Gehlaut & Maryam Shojaee , 2025.
"GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients,"
Nature Communications, Nature, vol. 16(1), pages 1-11, December.
Handle:
RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59610-8
DOI: 10.1038/s41467-025-59610-8
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