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Diagnostic Performance of Artificial Intelligence for Interpreting Thyroid Cancer in Ultrasound images

Author

Listed:
  • Piyanuch Arunrukthavon

    (Mahidol University, Thailand)

  • Dittapong Songsaeng

    (Mahidol University, Thailand)

  • Chadaporn Keatmanee

    (Ramkhamhaeng University, Thailand)

  • Songphon Klabwong

    (Asian Institute of Technology, Thailand)

  • Mongkol Ekpanyapong

    (Asian Institute of Technology, Thailand)

  • Matthew N. Dailey

    (Asian Institute of Technology, Thailand)

Abstract

Thyroid ultrasonography is mainly used for the detection and characterization of thyroid nodules. However, there is some limitation since the diagnostic performance remains highly subjective and depends on radiologist experiences. Therefore, artificial intelligence (AI) was expected to improve the diagnostic performance of thyroid ultrasound. To evaluate the diagnostic performance of the AI for differentiating malignant and benign thyroid nodules and compare it with that of an experienced radiologist and a third-year diagnostic radiology resident, 648 patients with 650 thyroid nodules, who underwent thyroid ultrasound guided-FNA biopsy and had a decisive diagnosis from FNA cytology at Siriraj Hospital between January 2014 and June 2020, were enrolled. Although the specificity and accuracy were slightly higher in AI than the experienced radiologist and the resident (specificity 78.85% vs. 67.31% vs. 69.23%; accuracy 78.46% vs. 70.77% vs. 70.77%, respectively), the AI showed comparable diagnostic sensitivity and specificity to the experienced radiologist and the resident (p=0.187-0.855).

Suggested Citation

  • Piyanuch Arunrukthavon & Dittapong Songsaeng & Chadaporn Keatmanee & Songphon Klabwong & Mongkol Ekpanyapong & Matthew N. Dailey, 2022. "Diagnostic Performance of Artificial Intelligence for Interpreting Thyroid Cancer in Ultrasound images," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 13(1), pages 1-13, January.
    • Handle: RePEc:igg:jkss00:v:13:y:2022:i:1:p:1-13
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    References listed on IDEAS

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    1. Athakorn Kengpol & Wilaitip Punyota, 2022. "Knowledge Management of Vegetarian Food for the Elderly Using DCNN: An Empirical Study in Thailand," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 13(2), pages 1-17, April.
    2. Kin Lok Keung & Carman Lee & K.K.H. Ng & Sing Sum Leung & K.L. Choy, 2018. "An Empirical Study on Patients' Acceptance and Resistance Towards Electronic Health Record Sharing System: A Case Study of Hong Kong," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 9(2), pages 1-27, April.
    3. Megha Rathi & Vikas Pareek, 2019. "Mobile Based Healthcare Tool an Integrated Disease Prediction & Recommendation System," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 10(1), pages 38-62, January.
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