Evidence map›Paper›PMID 34649779›Full record

SynthesisAcademic radiology2021

Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis.

Temitope Emmanuel Komolafe, Yuzhu Cao, Benedictor Alexander Nguchu, Patrice Monkam, Ebenezer Obaloluwa Olaniyi, Haotian Sun, Jian Zheng, Xiaodong Yang

Open access · bronzeAbstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Academic radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 4 pooled it
2.4field-weighted citation impact, top 11% 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

7 citing papers in PubMed, 4 syntheses or guidelines pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Review
  7. 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

8 authors at 4 institutions in 1 country.

Temitope Emmanuel KomolafeSchool of Biomedical Engineering (Suzhou) (T.E.K.,Y.C., H.S.), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Department of Medical Imaging (T.E.K.,Y.C., H.S., J.Z., X.Y.), Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Hefei National Lab for Physical Sciences at the Microscale and Centres for Biomedical Engineering (B.A.N.), University of Science and Technology of China, Hefei, 230026, China; EasySignal Group, Department of Automation (P.M.), Tsinghua University, Beijing 100084, China; Department of Biomedical Engineering (E.O.O.), Shenzhen University, Shenzhen, 518060, China; Jinhua Laboratory (X.Y.), Foshan, 528000, China.
Yuzhu CaoSchool of Biomedical Engineering (Suzhou) (T.E.K.,Y.C., H.S.), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Department of Medical Imaging (T.E.K.,Y.C., H.S., J.Z., X.Y.), Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Hefei National Lab for Physical Sciences at the Microscale and Centres for Biomedical Engineering (B.A.N.), University of Science and Technology of China, Hefei, 230026, China; EasySignal Group, Department of Automation (P.M.), Tsinghua University, Beijing 100084, China; Department of Biomedical Engineering (E.O.O.), Shenzhen University, Shenzhen, 518060, China; Jinhua Laboratory (X.Y.), Foshan, 528000, China.
Benedictor Alexander NguchuSchool of Biomedical Engineering (Suzhou) (T.E.K.,Y.C., H.S.), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Department of Medical Imaging (T.E.K.,Y.C., H.S., J.Z., X.Y.), Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Hefei National Lab for Physical Sciences at the Microscale and Centres for Biomedical Engineering (B.A.N.), University of Science and Technology of China, Hefei, 230026, China; EasySignal Group, Department of Automation (P.M.), Tsinghua University, Beijing 100084, China; Department of Biomedical Engineering (E.O.O.), Shenzhen University, Shenzhen, 518060, China; Jinhua Laboratory (X.Y.), Foshan, 528000, China.
Patrice MonkamSchool of Biomedical Engineering (Suzhou) (T.E.K.,Y.C., H.S.), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Department of Medical Imaging (T.E.K.,Y.C., H.S., J.Z., X.Y.), Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Hefei National Lab for Physical Sciences at the Microscale and Centres for Biomedical Engineering (B.A.N.), University of Science and Technology of China, Hefei, 230026, China; EasySignal Group, Department of Automation (P.M.), Tsinghua University, Beijing 100084, China; Department of Biomedical Engineering (E.O.O.), Shenzhen University, Shenzhen, 518060, China; Jinhua Laboratory (X.Y.), Foshan, 528000, China.
Ebenezer Obaloluwa OlaniyiSchool of Biomedical Engineering (Suzhou) (T.E.K.,Y.C., H.S.), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Department of Medical Imaging (T.E.K.,Y.C., H.S., J.Z., X.Y.), Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Hefei National Lab for Physical Sciences at the Microscale and Centres for Biomedical Engineering (B.A.N.), University of Science and Technology of China, Hefei, 230026, China; EasySignal Group, Department of Automation (P.M.), Tsinghua University, Beijing 100084, China; Department of Biomedical Engineering (E.O.O.), Shenzhen University, Shenzhen, 518060, China; Jinhua Laboratory (X.Y.), Foshan, 528000, China.
Haotian SunSchool of Biomedical Engineering (Suzhou) (T.E.K.,Y.C., H.S.), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Department of Medical Imaging (T.E.K.,Y.C., H.S., J.Z., X.Y.), Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Hefei National Lab for Physical Sciences at the Microscale and Centres for Biomedical Engineering (B.A.N.), University of Science and Technology of China, Hefei, 230026, China; EasySignal Group, Department of Automation (P.M.), Tsinghua University, Beijing 100084, China; Department of Biomedical Engineering (E.O.O.), Shenzhen University, Shenzhen, 518060, China; Jinhua Laboratory (X.Y.), Foshan, 528000, China.
Jian ZhengSchool of Biomedical Engineering (Suzhou) (T.E.K.,Y.C., H.S.), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Department of Medical Imaging (T.E.K.,Y.C., H.S., J.Z., X.Y.), Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Hefei National Lab for Physical Sciences at the Microscale and Centres for Biomedical Engineering (B.A.N.), University of Science and Technology of China, Hefei, 230026, China; EasySignal Group, Department of Automation (P.M.), Tsinghua University, Beijing 100084, China; Department of Biomedical Engineering (E.O.O.), Shenzhen University, Shenzhen, 518060, China; Jinhua Laboratory (X.Y.), Foshan, 528000, China.
Xiaodong YangSchool of Biomedical Engineering (Suzhou) (T.E.K.,Y.C., H.S.), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; Department of Medical Imaging (T.E.K.,Y.C., H.S., J.Z., X.Y.), Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Hefei National Lab for Physical Sciences at the Microscale and Centres for Biomedical Engineering (B.A.N.), University of Science and Technology of China, Hefei, 230026, China; EasySignal Group, Department of Automation (P.M.), Tsinghua University, Beijing 100084, China; Department of Biomedical Engineering (E.O.O.), Shenzhen University, Shenzhen, 518060, China; Jinhua Laboratory (X.Y.), Foshan, 528000, China. Electronic address: xiaodong.yang@sibet.ac.cn.
Chinese Academy of Sciences · CNShenzhen University · CNUniversity of Science and Technology of China · CNTsinghua University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RATIONALE AND

objectiveTo perform a meta-analysis to compare the diagnostic test accuracy (DTA) of deep learning (DL) in detecting coronavirus disease 2019 (COVID-19), and to investigate how network architecture and type of datasets affect DL performance. MATERIALS AND

methodsWe searched PubMed, Web of Science and Inspec from January 1, 2020, to December 3, 2020, for retrospective and prospective studies on deep learning detection with at least reported sensitivity and specificity. Pooled DTA was obtained using random-effect models. Sub-group analysis between studies was also carried out for data source and network architectures.

resultsThe pooled sensitivity and specificity were 91% (95% confidence interval [CI]: 88%, 93%; I

conclusionThe diagnosis of COVID-19 via deep learning has achieved incredible performance, and the source of datasets, as well as network architectures, strongly affect DL performance.

Indexed as

COVID-19Deep LearningDiagnostic Tests, RoutineHumansProspective StudiesRetrospective StudiesSARS-CoV-2Chest computed tomographyCOVID-19Deep learningDiagnostic test accuracyMeta-analysis

Identifiers

PMID34649779
PMCPMC8445811
OpenAlexW3199608350

What OpenQuestion 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 OpenQuestion graph.