Evidence map›Paper›PMID 42675271›Full record

ArticleJournal of imaging informatics in medicine2026

Deep Learning Algorithm-Reconstructed Triple-Rule-Out CT: Image Quality and Performance in Evaluating Coronary Lumen Stenosis.

Yu Du, Xingyan Chen, Jinhua Zhang, Jing Zhang, Yanjie Zhao, Youfa Tang, Suping Chen, Xiangxiang Chen, Lang Chen, Qiuxia Wang and 1 more

Abstract read
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In one paragraph

Article in Journal of imaging informatics in medicine, 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

11 authors.

Yu Du *Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xingyan Chen *Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jinhua ZhangDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jing ZhangDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yanjie ZhaoDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Youfa TangDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Suping ChenComputed Tomography Research Center, GE HealthCare, Beijing, China.
Xiangxiang ChenComputed Tomography Research Center, GE HealthCare, Beijing, China.
Lang Chen *Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. langc731@163.com.
Qiuxia Wang *Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. wangqiuxia@hust.edu.cn.ORCID http://orcid.org/0000-0003-0998-3800
Zhen LiDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

Hubei Provincial Key Research and Development Program 2024BCB008National Key Research and Development Program of China 2024YFC2419300National Natural Science Foundation of China 82471967
6 · The paper itself

Abstract

To assess radiation burden and contrast medium load, image quality, and the performance of AI-reader and junior radiologists in evaluating coronary stenosis using 80-kVp triple-rule-out (TRO) CT with deep learning image reconstruction (DLIR), 159 patients scheduled for TRO were prospectively recruited and randomized into group A (n = 80; 80-kVp with DLIR) or group B (n = 79; 100-kVp with adaptive statistical iterative reconstruction-V [ASIR-V 60%]). Patients were stratified by BMI (< 25 kg/m

Indexed as

Deep learning image reconstructionImage qualityLumen stenosisRadiation doseTriple-rule-out

Identifiers

PMID42675271

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.