Evidence map›Paper›PMID 40990678›Full record

Trial reportInternational journal of surgery (London, England)2026

Development and clinical validation of a novel deep learning-based mediastinal endoscopic ultrasound navigation system for quality control: a single-center, randomized controlled trial.

Shiqin Huang, Xuejie Chen, Li Tian, Xiong Chen, Yuanyuan Yang, Yingnan Sun, Yuqian Zhou, Weiming Qu, Rui Wang, Xiaoyan Wang

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in International journal of surgery (London, England), 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

10 authors.

Shiqin HuangDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, Hunan Province, China.
Xuejie ChenDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, Hunan Province, China.
Li TianDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, Hunan Province, China.
Xiong ChenDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, Hunan Province, China.
Yuanyuan YangDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, Hunan Province, China.
Yingnan SunDepartment of Endoscopy, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan Province, China.
Yuqian ZhouDepartment of Gastroenterology, The Second Xiangya Hospital of Central South University, Changsha, Hunan Province, China.
Weiming QuDepartment of Gastroenterology, Zhuzhou Central Hospital, Zhuzhou, Hunan Province, China.
Rui WangDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, Hunan Province, China.
Xiaoyan WangDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, Hunan Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEndoscopic ultrasound (EUS) is crucial for diagnosing and managing mediastinal diseases but lacks effective quality control. This study developed and evaluated an artificial intelligence (AI) system to assist in anatomical landmark identification and scanning guidance, aiming to improve quality control of mediastinal EUS examinations in clinical practice.

methodsThe AI system for mediastinal EUS was trained on 11 230 annotated images from 120 patients, validated internally (1972 images) and externally (824 images from three institutions). A single-center randomized controlled trial was designed to evaluate the effect of quality control, which enrolled patients requiring mediastinal EUS, randomized 1:1 to AI-assisted or control groups. Four endoscopists performed EUS, with the AI group receiving real-time AI feedback. The primary outcome was standard station completeness; secondary outcomes included structure completeness, procedure time, and adverse events. Blinded analysis ensured objectivity.

resultsBetween 16 September 2023, and 28 February 2025, a total of 148 patients were randomly assigned and analyzed, with 72 patients in the AI-assisted group and 76 in the control group. The overall station completeness was significantly higher in the AI-assisted group than in the control group (1.00 [IQR, 1.00-1.00] vs. 0.80 [IQR, 0.60-0.80]; P < 0.001), with the AI-assisted group also demonstrating significantly higher anatomical structure completeness (1.00 [IQR, 1.00-1.00] vs. 0.85 [IQR, 0.62-0.92]; P < 0.001). However, no significant differences were found for station 2 (subcarinal area) or average procedural time, and no adverse events were reported.

conclusionsThe AI system significantly improved the scan completeness and shows promise in enhancing EUS quality control.

Indexed as

Deep LearningEndosonographyMediastinal DiseasesMediastinumAdultAgedFemaleHumansMaleMiddle AgedQuality Controlartificial intelligencedeep learningendoscopic ultrasoundmediastinal diseasesquality control

Identifiers

PMID40990678
PMCPMC12825897

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.