Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study.

Abstract

OBJECTIVES:

We aim ed to evaluate a commercial artificial intelligence (AI) solution on a multicenter cohort of chest radiographs and to compare physicians' ability to detect and localize referable thoracic abnormalities with and without AI assistance.

METHODS:

In this retrospective diagnostic cohort study, we investigated 6,006 consecutive patients who underwent both chest radiography and CT. We evaluated a commercially available AI solution intended to facilitate the detection of three chest abnormalities (nodule/masses, consolidation, and pneumothorax) against a reference standard to measure its diagnostic performance. Moreover, twelve physicians, including thoracic radiologists, board-certified radiologists, radiology residents, and pulmonologists, assessed a dataset of 230 randomly sampled chest radiographic images. The images were reviewed twice per physician, with and without AI, with a 4-week washout period. We measured the impact of AI assistance on observer's AUC, sensitivity, specificity, and the area under the alternative free-response ROC (AUAFROC).

RESULTS:

In the entire set (n = 6,006), the AI solution showed average sensitivity, specificity, and AUC of 0.885, 0.723, and 0.867, respectively. In the test dataset (n = 230), the average AUC and AUAFROC across observers significantly increased with AI assistance (from 0.861 to 0.886; p = 0.003 and from 0.797 to 0.822; p = 0.003, respectively).

CONCLUSIONS:

The diagnostic performance of the AI solution was found to be acceptable for the images from respiratory outpatient clinics. The diagnostic performance of physicians marginally improved with the use of AI solutions. Further evaluation of AI assistance for chest radiographs using a prospective design is required to prove the efficacy of AI assistance.

KEY POINTS:

• AI assistance for chest radiographs marginally improved physicians' performance in detecting and localizing referable thoracic abnormalities on chest radiographs. • The detection or localization of referable thoracic abnormalities by pulmonologists and radiology residents improved with the use of AI assistance.

• Authors: Kwang Nam Jin, Eun Young Kim, Young Jae Kim, Gi Pyo Lee, Hyungjin Kim, Sohee Oh, Yong Suk Kim, Ju Hyuck Han, Young Jun Cho
• Journal: European radiology (Eur Radiol) Vol. 32 Issue 5 Pg. 3469-3479 (May 2022) ISSN: 1432-1084 [Electronic] Germany
• PMID: 34973101 (Publication Type: Journal Article, Multicenter Study)
• Copyright: © 2021. The Author(s).

Topics

• Artificial Intelligence
• Cohort Studies
• Humans
• Outpatients
• Prospective Studies
• Radiography
• Radiography, Thoracic (methods)
• Retrospective Studies
• Sensitivity and Specificity