Evidence map›Paper›PMID 35996746›Full record

ReviewEuropean journal of radiology open2022

Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonias: A systematic review and meta-analysis.

Lu-Lu Jia, Jian-Xin Zhao, Ni-Ni Pan, Liu-Yan Shi, Lian-Ping Zhao, Jin-Hui Tian, Gang Huang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in European journal of radiology open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 4 pooled it
–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

24 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

7 authors.

Lu-Lu JiaFirst Clinical School of Medicine, Gansu University of Chinese Medicine, Lanzhou 73000, China.
Jian-Xin ZhaoFirst Clinical School of Medicine, Gansu University of Chinese Medicine, Lanzhou 73000, China.
Ni-Ni PanFirst Clinical School of Medicine, Gansu University of Chinese Medicine, Lanzhou 73000, China.
Liu-Yan ShiFirst Clinical School of Medicine, Gansu University of Chinese Medicine, Lanzhou 73000, China.
Lian-Ping ZhaoDepartment of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China.
Jin-Hui TianEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China.
Gang HuangDepartment of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

Indexed as

2D, two-dimensional3D, three-dimensionalAI, artificial intelligenceArtificial IntelligenceAUC, area under the curveCNN, Convolutional neural networkCOVID-19COVID-19, Coronavirus disease 2019CRP, C-reactive proteinCT, Computed tomographyCXR, Chest X-RayDiagnostic ImagingGGO, ground-glass opacitiesKNN, K-nearest neighborLASSO, least absolute shrinkage and selection operatorMachine learningMEERS-COV, Middle East respiratory syndrome coronavirusML, machine learningPLR, negative likelihood ratioPLR, positive likelihood ratioPneumoniaROI, regions of interestRT-PCR, Reverse transcriptase polymerase chain reactionSARS-CoV-2, severe acute respiratory syndrome coronavirus 2SARS, severe acute respiratory syndromeSROC, summary receiver operating characteristicSVM, Support vector machine

Identifiers

PMID35996746
PMCPMC9385733

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.