Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels.
Humans
Male
Female
Tomography, X-Ray Computed
/ methods
Artificial Intelligence
Radiologists
Middle Aged
Lung Neoplasms
/ diagnostic imaging
Solitary Pulmonary Nodule
/ diagnostic imaging
Aged
Adult
Software
Radiographic Image Interpretation, Computer-Assisted
/ methods
Multiple Pulmonary Nodules
/ diagnostic imaging
CT
Lung cancer
Radiologists
Software
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 Sep 2024
28 Sep 2024
Historique:
received:
10
05
2024
accepted:
17
09
2024
medline:
29
9
2024
pubmed:
29
9
2024
entrez:
28
9
2024
Statut:
epublish
Résumé
The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improving detection rates and LungRADS classification in chest CT. The study cohort included 198 participants with 221 pulmonary nodules. Residents' mean detection rate increased significantly from 64 to 77% with AI assist, while seniors' detection rate remained largely unchanged (85% vs. 86%). Residents showed significant improvement in segmental nodule localization with AI assistance, seniors did not. Software 2 slightly outperformed software 1 in increasing detection rates (67-77% vs. 80-86%), but neither significantly affected LungRADS classification. The study suggests that clinical experience mitigates the need for additional AI software, with the combination of CAD with residents being the most beneficial approach. Both software systems performed similarly, with software 2 showing a slightly higher but non-significant increase in detection rates.
Identifiants
pubmed: 39341945
doi: 10.1038/s41598-024-73435-3
pii: 10.1038/s41598-024-73435-3
doi:
Types de publication
Journal Article
Comparative Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
22447Informations de copyright
© 2024. The Author(s).
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