Evidence map›Paper›PMID 37649977›Full record

ArticleFrontiers in medicine2023

A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm.

Yang Yang, Lu Xia, Ping Liu, Fuping Yang, Yuqing Wu, Hongqiu Pan, Dailun Hou, Ning Liu, Shuihua Lu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
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

8 citing papers in PubMed, 8 citations in OpenAlex.

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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 at 6 institutions in 1 country.

Yang Yang *Department of Tuberculosis, Shanghai Public Health Clinical Center Affiliated to Fudan University, Shanghai, China.
Lu Xia *Department of Pulmonary Medicine, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital/The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Ping LiuDepartment of Tuberculosis, Shanghai Public Health Clinical Center Affiliated to Fudan University, Shanghai, China.
Fuping YangDepartment of Tuberculosis, Chongqing Public Health Medical Center, Southwest University, Chongqing, China.
Yuqing WuDepartment of Tuberculosis, Jiangxi Chest Hospital, Nanchang, Jiangxi, China.
Hongqiu PanDepartment of Tuberculosis, The Third Hospital of Zhenjiang, Zhenjiang, Jiangsu, China.
Dailun HouDepartment of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China.
Ning LiuDepartment of Tuberculosis, Hebei Chest Hospital, Shijiangzhuang, Hebei, China.
Shuihua LuDepartment of Pulmonary Medicine, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital/The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China.
Shanghai Public Health Clinical Center · CNSouthern University of Science and Technology · CNCapital Medical University · CNHospital of Hebei Province · CNJiangxi Chest Hospital · CNSouthwest University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chest radiography (chest X-ray or CXR) plays an important role in the early detection of active pulmonary tuberculosis (TB). In areas with a high TB burden that require urgent screening, there is often a shortage of radiologists available to interpret the X-ray results. Computer-aided detection (CAD) software employed with artificial intelligence (AI) systems may have the potential to solve this problem. Objective: We validated the effectiveness and safety of pulmonary tuberculosis imaging screening software that is based on a convolutional neural network algorithm. Methods: We conducted prospective multicenter clinical research to validate the performance of pulmonary tuberculosis imaging screening software (JF CXR-1). Volunteers under the age of 15 years, both with or without suspicion of pulmonary tuberculosis, were recruited for CXR photography. The software reported a probability score of TB for each participant. The results were compared with those reported by radiologists. We measured sensitivity, specificity, consistency rate, and the area under the receiver operating characteristic curves (AUC) for the diagnosis of tuberculosis. Besides, adverse events (AE) and severe adverse events (SAE) were also evaluated. Results: The clinical research was conducted in six general infectious disease hospitals across China. A total of 1,165 participants were enrolled, and 1,161 were enrolled in the full analysis set (FAS). Men accounted for 60.0% (697/1,161). Compared to the results from radiologists on the board, the software showed a sensitivity of 94.2% (95% CI: 92.0-95.8%) and a specificity of 91.2% (95% CI: 88.5-93.2%). The consistency rate was 92.7% (91.1-94.1%), with a Kappa value of 0.854 ( Conclusion: The software for tuberculosis screening based on a convolutional neural network algorithm is effective and safe. It is a potential candidate for solving tuberculosis screening problems in areas lacking radiologists with a high TB burden.

Indexed as

computer-aided detection (CAD)convolutional neural network algorithmdeep learningdiagnosisradiographyscreeningtuberculosis

Identifiers

PMID37649977
PMCPMC10463041
OpenAlexW4385875004

What OpenQuestion holds

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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.