Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels

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.

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Publication Details

Journal
Scientific Reports
Published
2024-09-28
DOI
https://doi.org/10.1038/s41598-024-73435-3
Citations
12
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
5.03
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article

Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels

Thomas D. Ruder, Martín Müller, Justus E. Roos, Andreas Christe et al.
12 citations
Scientific Reports
Lung Cancer Diagnosis and Treatment
5.03
article

Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels

Thomas D. Ruder, Martín Müller, Justus E. Roos, Andreas Christe, Alan A. Peters, Adrian Thomas Huber, Jeremias Klaus, Lukas Ebner, Johannes T. Heverhagen, Verena Carola Obmann, Ana Macek, Felix Strodka, Dionysios Drakopulos, Nina Wiescholek, Elias Primetis
article en
12 citations

Abstract

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.

Scientific ReportsVol. 14(1)
University of Bern (CH), University Hospital of Bern (CH), Luzerner Kantonsspital (CH), Kantonsspital Münsterlingen (CH), The Ohio State University (US)
Openalex Percentile: Top 4%
Lung Cancer Diagnosis and Treatment
5.03
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