Evidence map›Paper›PMID 40596647›Full record

ArticleScientific reports2025

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.

Qing Song, Xuelei He, Yanjie Wang, Hanjing Gao, Li Tan, Jun Ma, Linli Kang, Peng Han, Yukun Luo, Kun Wang

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. 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

10 authors.

Qing SongDepartment of Ultrasound, First Medical Center of General Hospital of Chinese PLA, Beijing, 100853, China.
Xuelei HeSchool of Information Sciences and Technology, Northwest University, Xi'an, 710127, Shaanxi Province, China.
Yanjie WangDepartment of Ultrasound, Shandong Province Maternal and Child Health Care Hospital, Jinan, 250014, China.
Hanjing GaoDepartment of Ultrasound, Second Medical Center, General Hospital of Chinese PLA, Beijing, 100700, China.
Li TanBeijing Da Wang Lu Emergency Hospital, Beijing, China.
Jun MaDepartment of Ultrasound, First Medical Center of General Hospital of Chinese PLA, Beijing, 100853, China.
Linli KangDepartment of Ultrasound, First Medical Center of General Hospital of Chinese PLA, Beijing, 100853, China.
Peng HanDepartment of Ultrasound, First Medical Center of General Hospital of Chinese PLA, Beijing, 100853, China.
Yukun LuoDepartment of Ultrasound, First Medical Center of General Hospital of Chinese PLA, Beijing, 100853, China. lyk301@163.com.
Kun WangCAS Key Laboratory of Molecular Imaging, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. kun.wang@ia.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in Beijing, China. A deep learning model was created and fine-tuned with animal and clinical data, achieving high accuracy metrics. In internal tests, the model outperformed both Junior and Senior sonographers. External tests showed the model's effectiveness, with a Dice Similarity Coefficient of 0.74, True Positive Rate of 0.80, Positive Predictive Value of 0.74, and 95% Hausdorff distance of 14.84. The model's performance was comparable to Junior sonographers and slightly lower than Senior sonographers. This AI model shows promise for liver injury detection, offering a valuable tool with diagnostic capabilities similar to those of less experienced human operators.

Indexed as

Artificial IntelligenceLiverAdultAnimalsDeep LearningDisease Models, AnimalFemaleHumansMaleMiddle AgedSwineSwine, MiniatureUltrasonographyAnimal modelsArtificial intelligenceDeep learningLacerationsLiver contusionsPredictive value of tests

Identifiers

PMID40596647
PMCPMC12215739

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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.