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Effect of Artificial Intelligence as a Second Reader on the Lung Nodule Detection and Localization Accuracy of Radiologists and Non-radiology Physicians in Chest Radiographs: A Multicenter Reader Study.

Publication ,  Journal Article
Robert, D; Sathyamurthy, S; Singh, AK; Matta, SA; Tadepalli, M; Tanamala, S; Bosemani, V; Mammarappallil, J; Kundnani, B
Published in: Acad Radiol
March 2025

RATIONALE AND OBJECTIVES: Missed nodules in chest radiographs (CXRs) are common occurrences. We assessed the effect of artificial intelligence (AI) as a second reader on the accuracy of radiologists and non-radiology physicians in lung nodule detection and localization in CXRs. MATERIALS AND METHODS: This retrospective study using the multi-reader multi-case design included 300 CXRs acquired from 40 hospitals across the US. All CXRs had a paired follow-up image (chest CT or CXR) to augment the ground truth establishment for the presence and location of nodules on CXRs by five independent thoracic radiologists. 15 readers (nine radiologists and six non-radiology physicians) read each CXR twice in a second-reader paradigm, once without AI and then immediately with AI assistance. The primary analysis assessed the difference in area-under-the-alternative-free-response-receiver-operating-characteristic-curve (AFROC) of readers with and without AI. Case-level area-under-the-receiver-operating-characteristic-curve (AUROC), sensitivity, and specificity were assessed in secondary analyses. RESULTS: A total of 300 CXRs (147 with nodules, 153 without nodules) from 300 patients (mean age, 64 years ± 15 [standard deviation]; 174 women) were included. The mean AFROC of readers was 0.73 without AI and 0.81 with AI (95% CI of difference, 0.05-0.10). Case-level AUROC was 0.77 without AI and 0.84 with AI (95% CI of difference, 0.04-0.09). Case-level sensitivity was 72.8% and 83.5% (95% CI of difference, 6.8-14.6) and specificity was 71.1% and 72.0% (95% CI of difference, -0.8-2.6) without and with AI, respectively. CONCLUSION: Using AI, readers detected and localized more nodules without any significant difference in false positive interpretations.

Duke Scholars

Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

March 2025

Volume

32

Issue

3

Start / End Page

1706 / 1717

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Solitary Pulmonary Nodule
  • Sensitivity and Specificity
  • Retrospective Studies
  • Reproducibility of Results
  • Radiologists
  • Radiography, Thoracic
  • Radiographic Image Interpretation, Computer-Assisted
  • Observer Variation
  • Nuclear Medicine & Medical Imaging
 

Citation

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MLA
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Robert, D., Sathyamurthy, S., Singh, A. K., Matta, S. A., Tadepalli, M., Tanamala, S., … Kundnani, B. (2025). Effect of Artificial Intelligence as a Second Reader on the Lung Nodule Detection and Localization Accuracy of Radiologists and Non-radiology Physicians in Chest Radiographs: A Multicenter Reader Study. Acad Radiol, 32(3), 1706–1717. https://doi.org/10.1016/j.acra.2024.11.003
Robert, Dennis, Saigopal Sathyamurthy, Anshul Kumar Singh, Sri Anusha Matta, Manoj Tadepalli, Swetha Tanamala, Vijay Bosemani, Joseph Mammarappallil, and Bunty Kundnani. “Effect of Artificial Intelligence as a Second Reader on the Lung Nodule Detection and Localization Accuracy of Radiologists and Non-radiology Physicians in Chest Radiographs: A Multicenter Reader Study.Acad Radiol 32, no. 3 (March 2025): 1706–17. https://doi.org/10.1016/j.acra.2024.11.003.
Robert D, Sathyamurthy S, Singh AK, Matta SA, Tadepalli M, Tanamala S, Bosemani V, Mammarappallil J, Kundnani B. Effect of Artificial Intelligence as a Second Reader on the Lung Nodule Detection and Localization Accuracy of Radiologists and Non-radiology Physicians in Chest Radiographs: A Multicenter Reader Study. Acad Radiol. 2025 Mar;32(3):1706–1717.
Journal cover image

Published In

Acad Radiol

DOI

EISSN

1878-4046

Publication Date

March 2025

Volume

32

Issue

3

Start / End Page

1706 / 1717

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Solitary Pulmonary Nodule
  • Sensitivity and Specificity
  • Retrospective Studies
  • Reproducibility of Results
  • Radiologists
  • Radiography, Thoracic
  • Radiographic Image Interpretation, Computer-Assisted
  • Observer Variation
  • Nuclear Medicine & Medical Imaging