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<p>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.&#xa0;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.&#xa0;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.</p>

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GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月28日

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  • 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,
  • Surabie Sivanendran,
  • Maryam Naghibzadeh,
  • Amirhossein Azhie,
  • Sareh Keshavarzi,
  • Kai Duan,
  • Leslie Lilly,
  • Nazia Selzner,
  • Cynthia Tsien,
  • Elmar Jaeckel,
  • Wei Xu,
  • Mamatha Bhat

摘要

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.