Evidence map›Paper›PMID 34541861›Full record

SynthesisThe British journal of radiology2021

Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: a systematic literature review.

Conor J Hardacre, Joseph A Robertshaw, Shaney L Barratt, Hannah L Adams, Robert V MacKenzie Ross, Graham Re Robinson, Jay Suntharalingam, John D Pauling, Jonathan Carl Luis Rodrigues

Open access · bronzeAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 3 pooled it
0.6field-weighted citation impact, top 30% 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, 3 syntheses or guidelines pooled it, 9 citations in OpenAlex.

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

9 authors at 4 institutions in 1 country.

Conor J HardacreMedical School, University of Bristol, Bristol, UK.ORCID 0000-0001-9800-0479
Joseph A RobertshawMedical School, University of Bristol, Bristol, UK.
Shaney L BarrattDepartment of Respiratory Medicine, North Bristol NHS Trust, Bristol, UK.
Hannah L AdamsBrighton and Sussex Medical School, Brighton, UK.
Robert V MacKenzie RossRoyal United Hospitals Bath NHS Foundation Trust, Bath, UK.
Graham Re RobinsonRoyal United Hospitals Bath NHS Foundation Trust, Bath, UK.
Jay SuntharalingamRoyal United Hospitals Bath NHS Foundation Trust, Bath, UK.
John D PaulingRoyal United Hospitals Bath NHS Foundation Trust, Bath, UK.
Jonathan Carl Luis RodriguesRoyal United Hospitals Bath NHS Foundation Trust, Bath, UK.ORCID 0000-0003-2744-3509
Royal United Hospital Bath NHS Trust · GBUniversity of Bristol · GBBrighton and Sussex Medical School · GBNorth Bristol NHS Trust · GB

Funding

Medical Research Council MR/M008894/1Wellcome Trust 205188/Z/16/Z
6 · The paper itself

Abstract

objectivesTo undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for the diagnosis of acquired pulmonary arterial hypertension (PAH).

methodsSearches of Medline, Embase and Web of Science were undertaken on 1 July 2020. Original publications studying AI applied to cross-sectional imaging for the diagnosis of acquired PAH in adults were identified through two-staged double-blinded review. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies and Checklist for Artificial Intelligence in Medicine frameworks. Narrative synthesis was undertaken following Synthesis Without Meta-Analysis guidelines. This review received no funding and was registered in the International Prospective Register of Systematic Reviews (ID:CRD42020196295).

resultsSearches returned 476 citations. Three retrospective observational studies, published between 2016 and 2020, were selected for data-extraction. Two methods applied to cardiac-MRI demonstrated high diagnostic accuracy, with the best model achieving AUC=0.90 (95% CI: 0.85-0.93), 89% sensitivity and 81% specificity. Stronger results were achieved using cardiac-MRI for classification of idiopathic PAH, achieving AUC=0.97 (95% CI: 0.89-1.0), 96% sensitivity and 87% specificity. One study reporting CT-based AI demonstrated lower accuracy, with 64.6% sensitivity and 97.0% specificity.

conclusionsAutomated methods for identifying PAH on cardiac-MRI are emerging with high diagnostic accuracy. AI applied to cross-sectional imaging may provide non-invasive support to reduce diagnostic delay in PAH. This would be helped by stronger solutions in other modalities. ADVANCES IN KNOWLEDGE: There is a significant shortage of research in this important area. Early detection of PAH would be supported by further research advances on the promising emerging technologies identified.

Indexed as

Artificial IntelligenceHumansHypertension, PulmonaryImage Interpretation, Computer-AssistedLungMagnetic Resonance ImagingReproducibility of ResultsSensitivity and SpecificityTomography, X-Ray Computed

Identifiers

PMID34541861
PMCPMC8631018
OpenAlexW3201358074

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.