Automated real-time detection of lung sliding using artificial intelligence: a prospective diagnostic accuracy study.
Artificial Intelligence
LIST
Lung Sliding
Lung Ultrasound
Pneumothorax
Journal
Chest
ISSN: 1931-3543
Titre abrégé: Chest
Pays: United States
ID NLM: 0231335
Informations de publication
Date de publication:
14 Feb 2024
14 Feb 2024
Historique:
received:
10
11
2023
revised:
04
02
2024
accepted:
09
02
2024
medline:
17
2
2024
pubmed:
17
2
2024
entrez:
16
2
2024
Statut:
aheadofprint
Résumé
Rapid evaluation for pneumothorax (PTX) is a common clinical priority. Although lung ultrasound (LUS) is often used to assess for PTX, its diagnostic accuracy varies based on patient and provider factors. To enhance the performance of LUS for pulmonary pathology, artificial intelligence (AI) assisted imaging has been adopted, however, the diagnostic accuracy of AI-Assisted LUS (AI-LUS) deployed in real-time to diagnose PTX remains unknown. In patients with suspected PTX, what is the real-time diagnostic accuracy of AI-LUS to recognize the absence of lung sliding? We performed a prospective AI-assisted diagnostic accuracy study of AI-LUS to recognize the absence of lung sliding in a convenience sample of patients with suspected pneumothorax. After calibrating the model parameters and imaging settings for bedside deployment, we prospectively evaluated its diagnostic accuracy for lung sliding compared to a reference standard of expert consensus. 241 lung sliding evaluations were derived from 62 patients. AI-LUS had a sensitivity of 0.921 (95% CI 0.792, 0.973), specificity of 0.802 (95% CI 0.735 - 0.856), area under the curve of the receiver operating characteristic (AUC) of 0.885 (95% CI 0.828, 0.956), and accuracy of 0.824 (95% CI 0.766 - 0.870) for the diagnosis of absent lung sliding. Real-time AI-LUS has high sensitivity and moderate specificity to identify the absence of lung sliding. Further research to improve model performance and optimize the integration of AI-LUS into existing diagnostic pathways is warranted. N/A.
Sections du résumé
BACKGROUND
BACKGROUND
Rapid evaluation for pneumothorax (PTX) is a common clinical priority. Although lung ultrasound (LUS) is often used to assess for PTX, its diagnostic accuracy varies based on patient and provider factors. To enhance the performance of LUS for pulmonary pathology, artificial intelligence (AI) assisted imaging has been adopted, however, the diagnostic accuracy of AI-Assisted LUS (AI-LUS) deployed in real-time to diagnose PTX remains unknown.
RESEARCH QUESTION
OBJECTIVE
In patients with suspected PTX, what is the real-time diagnostic accuracy of AI-LUS to recognize the absence of lung sliding?
STUDY DESIGN AND METHODS
METHODS
We performed a prospective AI-assisted diagnostic accuracy study of AI-LUS to recognize the absence of lung sliding in a convenience sample of patients with suspected pneumothorax. After calibrating the model parameters and imaging settings for bedside deployment, we prospectively evaluated its diagnostic accuracy for lung sliding compared to a reference standard of expert consensus.
RESULTS
RESULTS
241 lung sliding evaluations were derived from 62 patients. AI-LUS had a sensitivity of 0.921 (95% CI 0.792, 0.973), specificity of 0.802 (95% CI 0.735 - 0.856), area under the curve of the receiver operating characteristic (AUC) of 0.885 (95% CI 0.828, 0.956), and accuracy of 0.824 (95% CI 0.766 - 0.870) for the diagnosis of absent lung sliding.
INTERPRETATION
CONCLUSIONS
Real-time AI-LUS has high sensitivity and moderate specificity to identify the absence of lung sliding. Further research to improve model performance and optimize the integration of AI-LUS into existing diagnostic pathways is warranted.
CLINICAL TRIAL REGISTRATION
BACKGROUND
N/A.
Identifiants
pubmed: 38365174
pii: S0012-3692(24)00157-0
doi: 10.1016/j.chest.2024.02.011
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024. Published by Elsevier Inc.