Evidence map›Paper›PMID 41509928›Full record

ArticleiScience2026

Deep learning-enabled high-performance multiphoton fluorescence vascular imaging using clinically approved fluorescent probes.

Zhourui Xu, Haoran Luo, Ting Chen, Shoulong Yang, Junkang Peng, Yibin Zhang, Yi Gao, Yonghong Shao, Wing-Cheung Law, Ken-Tye Yong and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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

12 authors.

Zhourui XuSchool of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
Haoran LuoSchool of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
Ting ChenDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Shoulong YangSchool of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
Junkang PengSchool of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
Yibin ZhangDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Yi GaoSchool of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.
Yonghong ShaoKey Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Wing-Cheung LawDepartment of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Ken-Tye YongSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2052, Australia.
Ke WangKey Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Gaixia XuSchool of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Among various modalities, multiphoton fluorescence imaging (MPFI) stands out for its exceptionally high spatial resolution in deep tissue imaging. Unfortunately, current clinically approved fluorescent probes are not engineered for MPFI, hindering the entry of MPFI into the clinical stage. Although several high-performance customized multiphoton probes have been developed, their biosafety has yet to be corroborated. To address this concern, we developed a deep learning-based method, trained on previously reported MPFI images enabled by aggregation-induced emissions luminogens nanoparticles, for high-performance MPFI using commercial q800 quantum dots and a clinically approved indocyanine green (ICG) probe. Remarkably, the proposed method demonstrated exceptional effectiveness when handling previously unseen data and showed strong optimization performance for MPFI images of cerebral microvasculature. Especially, blood vessels in the hippocampus region become clear and noise free after deep learning processing. Overall, this work offers a valuable strategy to greatly improve the practicality and applicability of MPFI.

Indexed as

Biocomputational methodNanoparticlesOptical imaging

Identifiers

PMID41509928
PMCPMC12775992

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.