Evidence map›Paper›PMID 42064957›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Deep-learning-Assisted Photoacoustic and Ultrasound Evaluation for Pre-transplant Human Liver Graft Quality and Transplant Suitability.

Qinghao Zhang, Kaustubh Pandit, Yan Cui, Feng Yan, Nanchao Wang, Jingting Li, Alan Yao, Luca Menozzi, Kar-Ming Fung, Zhongxin Yu and 15 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Qinghao ZhangStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.ORCID 0009-0005-2906-6315
Kaustubh PanditStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Yan CuiStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Feng YanStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Nanchao WangDepartment of Biomedical Engineering, Duke University, Durham, NC, 27705, USA.
Jingting LiDepartment of Biomedical Engineering, Duke University, Durham, NC, 27705, USA.
Alan YaoDepartment of Biomedical Engineering, Duke University, Durham, NC, 27705, USA.
Luca MenozziDepartment of Biomedical Engineering, Duke University, Durham, NC, 27705, USA.
Kar-Ming FungDepartment of Pathology, University of Oklahoma Health Campus, Oklahoma City, OK 73104, USA.
Zhongxin YuDepartment of Pathology, University of Oklahoma Health Campus, Oklahoma City, OK 73104, USA.
Paige ParrackDepartment of Pathology, University of Oklahoma Health Campus, Oklahoma City, OK 73104, USA.
Walid AliDepartment of Pathology, University of Oklahoma Health Campus, Oklahoma City, OK 73104, USA.
Ronghao LiuStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Chen WangStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Junyuan LiuStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Clint A HostetlerDepartment of Pathology, University of Oklahoma Health Campus, Oklahoma City, OK 73104, USA.
Ashley N MilamLifeShare of Oklahoma, Oklahoma City, OK 73132, USA.
Bradon NaveLifeShare of Oklahoma, Oklahoma City, OK 73132, USA.
Ronald A SquiresLifeShare of Oklahoma, Oklahoma City, OK 73132, USA.
Narendra R BattulaDepartment of Surgery, University of Oklahoma Health Campus, Oklahoma City, OK 73104, USA.
Chongle PanStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Paulo N MartinsDepartment of Surgery, University of Oklahoma Health Campus, Oklahoma City, OK 73104, USA.
Junjie YaoDepartment of Biomedical Engineering, Duke University, Durham, NC, 27705, USA.
Tri VuStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Qinggong TangStephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73019, USA.ORCID 0000-0001-9499-5384

Funding

Tissue Pathology Shared ResourceP30CA225520 · NCI · UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR · PI ROBERT S. MANNEL · 2018 to 2026
$27.1M
TUMOR RESISTANCE MECHANISMS TO ANTI-VEGF THERAPY IN PROSTATE CANCER (Sukyung Woo)P20GM103639 · NIGMS · UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR · PI DHANASEKARAN, DANNY N. · 2012 to 2022
$21.3M
Use of 3D Quantitative Optical Methods to Optimize Mebendazole Treatment of Ovarian CancerP20GM135009 · NIGMS · UNIVERSITY OF OKLAHOMA · PI Javier Antonio Jo · 2022 to 2026
$13.6M
Automatic Wide-Field Optical Coherence Tomography for Assessment of Transplant Kidney ViabilityR01DK133717 · NIDDK · UNIVERSITY OF MASSACHUSETTS AMHERST · PI POTTER, STEVEN, TANG, QINGGONG · 2022 to 2025
$2.5M
NCI NIH HHS P30 CA225520NIDDK NIH HHS R01 DK133717NIGMS NIH HHS P20 GM103639NIGMS NIH HHS P20 GM135009
6 · The paper itself

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.

Identifiers

PMID42064957
PMCPMC13127501

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.