Deep-learning-Assisted Photoacoustic and Ultrasound Evaluation for Pre-transplant Human Liver Graft Quality and Transplant Suitability
Year: 2026
Type: preprint
Abstract: 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.
Source: medRxiv
Authors: Qinghao Zhang, Kaustubh Pandit, Yan Cui, Feng Yan, Nanchao Wang, Jingting Li, Alan Yao, Luca Menozzi, Kar-Ming Fung, Zhongxin Yu, Paige Parrack, Walid Ali, Ronghao Liu, Chen Wang, Junyuan Liu, Clint A. Hostetler, Ashley N. Milam, Bradon Nave, Ronald A. Squires, Narendra R. Battula, Chongle Pan, Paulo N. Martins, Junjie Yao, Tri Vu, Qinggong Tang
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7154473239