Evidence map›Paper›PMID 40585612›Full record

ArticleCureus2025

Establishment and Reliability of an Automatic Measurement Method of Pectus Excavatum Indices Using a Deep Learning Model.

Xicheng Deng, Siping He, Jiayi Lin, Chenhan Wang, Songxian Xie, Shanshan Hu, Kefeng Ling, Frank-Martin Haecker, Shuangquan Qu

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Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Xicheng DengDepartment of Cardiothoracic Surgery, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, CHN.
Siping HeDepartment of Radiology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, CHN.
Jiayi LinDepartment of Cardiothoracic Surgery, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, CHN.
Chenhan WangDepartment of Cardiothoracic Surgery, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, CHN.
Songxian XieSchool of Engineering Science, Shandong Xiehe University, Jinan, CHN.
Shanshan HuData and Information Management Center, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, CHN.
Kefeng LingData and Information Management Center, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, CHN.
Frank-Martin HaeckerDepartment of Pediatric Surgery, Children's Hospital of Eastern Switzerland, St. Gallen, CHE.
Shuangquan QuDepartment of Anesthesiology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital, Changsha, CHN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to evaluate the consistency and accuracy of pectus excavatum (PE) indices assessment by comparing U-Net-based automated segmentation with manual measurements, aiming to reduce interobserver variability and standardize clinical workflow in PE severity evaluation.

methodsAn automatic measurement model was developed using U-Net architecture, trained on 550 chest computed tomography (CT) scans from 94 patients and validated on 164 independent scans. The model calculated three key indices (Haller, correction, and asymmetry), compared against measurements by four observers.  Results: Manual measurements showed an initial error rate of 15.9% (first three observers), reduced to 4.1% after consensus correction (p<0.01). The U-Net model exhibited stable error rates (8.7% vs 8.5% pre-/post-correction, p=0.91). Strong agreement was observed between automated and corrected manual measurements: Haller index (intra-class correlation (ICC)=0.83), correction index (ICC=0.86), asymmetry index (ICC=0.92) (all p<0.01). Bland-Altman analysis confirmed minimal bias.

conclusionU-Net-based automation provides reliable measurement of PE severity indices, demonstrating the potential to reduce observer-dependent variability and enhance clinical workflow efficiency. Multi-center validation is warranted to support broader radiologic applications.

Indexed as

asymmetry indexcorrection indexdeep learninghaller indexpectus excavatumu-net

Identifiers

PMID40585612
PMCPMC12204242

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LicenceCC BY
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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.