End-stage liver disease (ESLD) is one of the leading causes of death worldwide. Currently, the only curative option for patients with ESLD is liver transplantation. However, the demand for donor livers far exceeds the available supply, partly because many potentially viable livers are discarded following biopsy evaluation. While biopsy is the gold standard for assessing liver histological features related to graft quality and transplant suitability, it often leads to high discard rates due to its susceptibility to sampling errors and limited spatial coverage. Besides, biopsy is invasive, time-consuming, and unavailable in clinical facilities with limited resources. Here, we present an AI-assisted photoacoustic/ultrasound (PA/US) imaging framework for quantitative assessment of human donor liver graft quality and transplant suitablity at the whole-organ scale. With multimodal volumetric PA/US images as the input, our deep-learning (DL) model accurately predicted the risk level of fibrosis and steatosis, which indicate the graft quality and transplant suitability, when comparing with true pathological scores. DL also identified the imaging modes (PAI wavelength and B-mode USI) that correlated the most with prediction accuracy, without relying on ill-posed spectral unmixing. Our method was evaluated in six discarded human donor livers comprising sixty spatially matched regions of interest. Our study will pave the way for a new standard of care in organ graft quality and transplant suitability that is fast, noninvasive, and spatially thorough to prevent unnecessary organ discards in liver transplantation.

The authors have declared no competing interest.

This study was supported by grants from the University of Oklahoma Health Sciences Center (P30CA225520), the National Science Foundation (OIA-2132161, 2238648, 2331409), the National Institutes of Health (R01DK133717), the Oklahoma Center for the Advancement of Science and Technology (HR23-071), the Medical Imaging COBRE (P20GM135009), the Prevent Cancer Foundation, the Data Institute for Societal Challenges and the Research Council funded by the Office of the Vice President for Research and Partnerships of the University of Oklahoma Norman Campus, and the Midwest Biomedical Accelerator Consortium (MBArC), an NIH Research Evaluation and Commercialization Hub (REACH). Histology services were provided by the Tissue Pathology Shared Resource, which is supported in part by the National Institute of General Medical Sciences COBRE Grant (P20GM103639) and the National Cancer Institute Grant (P30CA225520) of the National Institutes of Health. Open access publication support was provided by the University of Oklahoma Libraries Open Access Fund.

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The Institutional Review Board of the University of Oklahoma Health Sciences Center gave ethical approval for this work (IRB protocol 12462).

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