Publication: Diagnostic performance of artificial intelligence in interpreting thyroid nodules on ultrasound images: a multicenter retrospective study
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Issued Date
2024-05-01
Resource Type
ISSN
22234292
eISSN
22234306
Scopus ID
2-s2.0-85192105766
Journal Title
Quantitative Imaging in Medicine and Surgery
Volume
14
Issue
5
Start Page
3676
End Page
3694
Rights Holder(s)
SCOPUS
Bibliographic Citation
Quantitative Imaging in Medicine and Surgery Vol.14 No.5 (2024) , 3676-3694
Suggested Citation
Namsena P., Songsaeng D., Keatmanee C., Klabwong S., Kunapinun A., Soodchuen S., Tarathipayakul T., Tanasoontrarat W., Ekpanyapong M., Dailey M.N. Diagnostic performance of artificial intelligence in interpreting thyroid nodules on ultrasound images: a multicenter retrospective study. Quantitative Imaging in Medicine and Surgery Vol.14 No.5 (2024) , 3676-3694. 3694. doi:10.21037/qims-23-1650 Retrieved from: https://hdl.handle.net/20.500.14740/20495
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis. Methods: This retrospective study evaluates an artificial intelligence (AI)-driven system for thyroid nodule assessment, integrating clinical practices from multiple prominent Thai medical centers. We included patients who underwent thyroid ultrasonography complemented by ultrasound-guided fine needle aspiration (FNA) between January 2015 and March 2021. Participants formed a consecutive series, enhancing the study’s validity. A comparative analysis was conducted between the AI model’s diagnostic performance and that of both an experienced radiologist and a third-year radiology resident, using a dataset of 600 ultrasound images from three distinguished Thai medical institutions, each verified with cytological findings. Results: The AI system demonstrated superior diagnostic performance, with an overall sensitivity of 80% [95% confidence interval (CI): 59.3–93.2%] and specificity of 71.4% (95% CI: 53.7–85.4%). At Siriraj Hospital, the AI achieved a sensitivity of 90.0% (95% CI: 55.5–99.8%), specificity of 100.0% (95% CI: 69.2–100%), positive prediction value (PPV) of 100.0%, negative prediction value (NPV) of 90.9%, and an overall accuracy of 95.0%, indicating the benefits of AI’s extensive training across diverse datasets. The experienced radiologist’s sensitivity was 40.0% (95% CI: 21.1–61.3%), while the specificity was 80.0% (95% CIs: 63.6–91.6%), respectively, showing that the AI significantly outperformed the radiologist in terms of sensitivity (P=0.043) while maintaining comparable specificity. The inter-observer variability analysis indicated a moderate agreement (K=0.53) between the radiologist and the resident, contrasting with fair agreement (K=0.37/0.33) when each was compared with the AI system. Notably, 95% CIs for these diagnostic indexes highlight the AI system’s consistent performance across different settings. Conclusions: The findings advocate for the integration of AI into clinical settings to enhance the diagnostic accuracy of radiologists in assessing thyroid nodules. The AI system, designed as a supportive tool rather than a replacement, promises to revolutionize thyroid nodule diagnosis and management by providing a high level of diagnostic precision.