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Comparative Study
. 2024 Sep 28;14(1):22447.
doi: 10.1038/s41598-024-73435-3.

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

Alan Arthur Peters  1 , Nina Wiescholek  2 , Martin Müller  3 , Jeremias Klaus  2 , Felix Strodka  2 , Ana Macek  2   4 , Elias Primetis  2 , Dionysios Drakopulos  2 , Adrian Thomas Huber  2   5 , Verena Carola Obmann  2 , Thomas Daniel Ruder  2 , Justus Erasmus Roos  5 , Johannes Thomas Heverhagen  2   6   7 , Andreas Christe  2 , Lukas Ebner  2   5
Affiliations
Comparative Study

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

Alan Arthur Peters et al. Sci Rep. .

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.

Keywords: CT; Lung cancer; Radiologists; Software.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Patient flowchart.
Fig. 2
Fig. 2
Example case depicting a small ground glass nodule in the right upper lobe in a 69-year-old male patient: (a) shows the nodule on the initial CT scan, (b) shows the results of software 2 and (c) the results of software 1 in the vessel suppression image. The nodule was identified and correctly segmented by both CAD systems.
Fig. 3
Fig. 3
Example case depicting a part-solid nodule in the right upper lobe in a 72-year-old male patient: (a) shows the nodule on the initial CT scan, (b) shows the results of software 2 and (c) the results of software 1 in the vessel suppression image. The nodule was identified and the solid component correctly segmented by both CAD systems.
Fig. 4
Fig. 4
Detection rates by reader. The white columns depict the detection rate without and the black columns with AI assist. The asterisk (*) indicates statistical significance.
See this image and copyright information in PMC

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