Evidence map›Paper›PMID 42783164›Full record

ReviewBiosensors2026

Photoacoustic Imaging for Liver Disease: The Systems and the Molecules.

Bowen Jiang, Zhixian Lin, Xiaoquan Yang

Abstract readReview
In one paragraph

Review in Biosensors, 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

3 authors.

Bowen JiangSchool of Sino-German Robotics, Shenzhen University of Information Technology, Shenzhen 518172, China.ORCID 0009-0008-0486-4902
Zhixian LinState Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya 572025, China.
Xiaoquan YangMOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.

Funding

Shenzhen University of Information Technology SZIIT2024KJ036
6 · The paper itself

Abstract

Liver disease represents a significant global health burden, and its effective management relies on early, accurate diagnosis. Established assessments for liver diseases, ranging from invasive biopsies to conventional noninvasive imaging (e.g., ultrasound, MRI, and CT), are often limited by inadequate specificity, potential safety risks, or unsuitability for dynamic tracking. Photoacoustic imaging (PAI), a hybrid modality that combines optical absorption contrast with deep ultrasonic detection, offers an attractive solution for noninvasive, high-sensitivity, and high-specificity imaging in deep organs such as the liver. This review summarizes recent advances in photoacoustic imaging for liver pathophysiology, beginning with the evolution of imaging systems and extending to the diverse molecules employed for preclinical studies and early clinical trials. Specifically, novel reconstruction algorithms improved the spatial resolution and acquisition speed by up to threefold, Monte Carlo-based fluence compensation increased the deep-tissue signal-to-background ratio by approximately 50%, and a 7-azaindole-modified probe exhibited one-magnitude-higher superoxide-triggered activation than conventional hemicyanine dyes. Furthermore, we discuss key challenges and future perspectives, highlighting the translational potential of PAI as an emerging liver imaging modality.

Indexed as

Liver DiseasesPhotoacoustic TechniquesAnimalsHumansLiveractivatable probesimage reconstructionliver diseasephotoacoustic computed tomographyphotoacoustic imaging

Identifiers

PMID42783164
PMCPMC13604271

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.