Evidence map›Paper›PMID 33828217›Full record

ReviewNPJ digital medicine2021

Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis.

Ravi Aggarwal, Viknesh Sounderajah, Guy Martin, Daniel S W Ting, Alan Karthikesalingam, Dominic King, Hutan Ashrafian, Ara Darzi

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

Review in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 385 papers, 15 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
385citing papers in PubMed, 15 pooled it
34.1field-weighted citation impact, top 1% 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

385 citing papers in PubMed, 15 syntheses or guidelines pooled it, 821 citations in OpenAlex.

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325 more citing papers are in PubMed but not listed here.

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 3 institutions in 2 countries.

Ravi AggarwalInstitute of Global Health Innovation, Imperial College London, London, UK.
Viknesh SounderajahInstitute of Global Health Innovation, Imperial College London, London, UK.
Guy MartinInstitute of Global Health Innovation, Imperial College London, London, UK.ORCID http://orcid.org/0000-0002-5759-962X
Daniel S W TingSingapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore.ORCID http://orcid.org/0000-0003-2264-7174
Alan KarthikesalingamInstitute of Global Health Innovation, Imperial College London, London, UK.
Dominic KingInstitute of Global Health Innovation, Imperial College London, London, UK.
Hutan AshrafianInstitute of Global Health Innovation, Imperial College London, London, UK. h.ashrafian@imperial.ac.uk.ORCID http://orcid.org/0000-0003-1668-0672
Ara DarziInstitute of Global Health Innovation, Imperial College London, London, UK.ORCID http://orcid.org/0000-0001-7815-7989
Imperial College London · GBNIHR Imperial Biomedical Research Centre · GBSingapore National Eye Center · SG

Funding

Medical Research Council MC_PC_18032
6 · The paper itself

Abstract

Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC's ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC's ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC's ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.

Identifiers

PMID33828217
PMCPMC8027892
OpenAlexW3150212014

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.