The added effect of artificial intelligence on physicians’ performance in detecting thoracic pathologies on CT and chest X-ray: A systematic review

Publikation: Bidrag til tidsskriftReviewfagfællebedømt

Dokumenter

  • Fulltext

    Forlagets udgivne version, 1,42 MB, PDF-dokument

Our systematic review investigated the additional effect of artificial intelligence-based devices on human observers when diagnosing and/or detecting thoracic pathologies using different diagnostic imaging modalities, such as chest X-ray and CT. Peer-reviewed, original research articles from EMBASE, PubMed, Cochrane library, SCOPUS, and Web of Science were retrieved. Included articles were published within the last 20 years and used a device based on artificial intelligence (AI) technology to detect or diagnose pulmonary findings. The AI-based device had to be used in an observer test where the performance of human observers with and without addition of the device was measured as sensitivity, specificity, accuracy, AUC, or time spent on image reading. A total of 38 studies were included for final assessment. The quality assessment tool for diagnostic accuracy studies (QUADAS-2) was used for bias assessment. The average sensitivity increased from 67.8% to 74.6%; specificity from 82.2% to 85.4%; accuracy from 75.4% to 81.7%; and Area Under the ROC Curve (AUC) from 0.75 to 0.80. Generally, a faster reading time was reported when radiologists were aided by AI-based devices. Our systematic review showed that performance generally improved for the physicians when assisted by AI-based devices compared to unaided interpretation.

OriginalsprogEngelsk
Artikelnummer2206
TidsskriftDiagnostics
Vol/bind11
Udgave nummer12
Antal sider17
ISSN2075-4418
DOI
StatusUdgivet - 2021

Bibliografisk note

Funding Information:
Funding: This research was funded by Innovation Fund Denmark (IFD) with grant no. 0176-00013B for the AI4Xray project.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Antal downloads er baseret på statistik fra Google Scholar og www.ku.dk


Ingen data tilgængelig

ID: 286988989