Evidence map›Paper›PMID 39407181›Full record

ArticleBMC pediatrics2024

Differential diagnosis of congenital ventricular septal defect and atrial septal defect in children using deep learning-based analysis of chest radiographs.

Huihui Jia, Songqiao Tang, Wanliang Guo, Peng Pan, Yufeng Qian, Dongliang Hu, Yakang Dai, Yang Yang, Chen Geng, Haitao Lv

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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.

Huihui JiaDepartment of Radiology, Children ' s Hospital of Soochow University, 215025, Suzhou, P. R. China.
Songqiao TangSchool of Electronic & Information Engineering, Suzhou University of Science and Technology, 215009, Suzhou, China.
Wanliang GuoDepartment of Radiology, Children ' s Hospital of Soochow University, 215025, Suzhou, P. R. China.
Peng PanDepartment of Radiology, Children ' s Hospital of Soochow University, 215025, Suzhou, P. R. China.
Yufeng QianDepartment of Radiology, Children ' s Hospital of Soochow University, 215025, Suzhou, P. R. China.
Dongliang HuDepartment of Radiology, Children ' s Hospital of Soochow University, 215025, Suzhou, P. R. China.
Yakang DaiSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 215163, Suzhou, China.
Yang YangDepartment of Radiology, Children ' s Hospital of Soochow University, 215025, Suzhou, P. R. China.
Chen Geng *Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 215163, Suzhou, China. gengc@sibet.ac.cn.
Haitao Lv *Department of Pediatric Cardiology, Children ' s Hospital of Soochow University, 215025, Suzhou, P. R. China. haitaosz@163.com.

Funding

Scientific Research Project of the Suzhou Health Commission M2021029
6 · The paper itself

Abstract

backgroundChildren with atrial septal defect (ASD) and ventricular septal defect (VSD) are frequently examined for respiratory symptoms, even when the underlying disease is not found. Chest radiographs often serve as the primary imaging modality. It is crucial to differentiate between ASD and VSD due to their distinct treatment. PURPOSE: To assess whether deep learning analysis of chest radiographs can more effectively differentiate between ASD and VSD in children.

methodsIn this retrospective study, chest radiographs and corresponding radiology reports from 1,194 patients were analyzed. The cases were categorized into a training set and a validation set, comprising 480 cases of ASD and 480 cases of VSD, and a test set with 115 cases of ASD and 119 cases of VSD. Four deep learning network models-ResNet-CBAM, InceptionV3, EfficientNet, and ViT-were developed for training, and a fivefold cross-validation method was employed to optimize the models. Receiver operating characteristic (ROC) curve analyses were conducted to assess the performance of each model. The most effective algorithm was compared with the interpretations provided by two radiologists on 234 images from the test group.

resultsThe average accuracy, sensitivity, and specificity of the four deep learning models in the differential diagnosis of VSD and ASD were higher than 70%. The AUC values of ResNet-CBAM, IncepetionV3, EfficientNet, and ViT were 0.87, 0.91, 0.90, and 0.66, respectively. Statistical analysis showed that the differential diagnosis efficiency of InceptionV3 was the highest, reaching 87% classification accuracy. The accuracy of InceptionV3 in the differential diagnosis of VSD and ASD was higher than that of the radiologists.

conclusionsDeep learning methods such as IncepetionV3 based on chest radiographs in the study showed good performance for differential diagnosis of congenital VSD and ASD, which may be able to assist radiologists in diagnosis, education, and training, and reduce missed diagnosis and misdiagnosis.

Indexed as

Deep LearningHeart Septal Defects, AtrialHeart Septal Defects, VentricularRadiography, ThoracicAdolescentChildChild, PreschoolDiagnosis, DifferentialFemaleHumansInfantMaleRetrospective StudiesROC CurveSensitivity and SpecificityChest radiographyCongenital heart diseaseDeep learningDifferential diagnosis

Identifiers

PMID39407181
PMCPMC11476512

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.