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Training the diagnostic artificial intelligence in thyroid sonography: how well is deep learning truly learning?

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Summary

Artificial intelligence (AI) significantly improved thyroid nodule classification accuracy using ultrasound, reaching 92.6% agreement after software updates. While AI is a solid diagnostic tool, experienced clinicians remain essential for complex cases.

Keywords:
ACR TI-RADSArtificial intelligence (AI)Deep learningEndocrine surgeryThyroid nodulesThyroid ultrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Artificial intelligence (AI) demonstrates potential in enhancing TI-RADS classification for thyroid nodules via neck sonography.
  • Evaluating the real-world learning curve of AI in thyroid nodule assessment is crucial.

Purpose of the Study:

  • To assess the learning curve and performance improvement of AI in ACR TI-RADS classification of thyroid nodules.
  • To compare AI-driven TI-RADS assessments with those of experienced clinicians.

Main Methods:

  • Utilized 3D-ultrasound PIUR tUS Infinity software for AI-based TI-RADS classification of 176 nodules in 110 patients.
  • Repeated the study with software updates, classifying 228 nodules in 133 patients.
  • Compared AI classifications against an experienced endocrine surgeon's assessments.

Main Results:

  • Initial AI-TI-RADS correspondence was 73% (128/176 nodules), improving to 92.6% (210/227 nodules) after software updates.
  • AI initially misinterpreted 20% of nodules with microcalcifications; this decreased to 4.8% post-update.
  • Post-update, significant discrepancies were rare (2.6%), primarily in complex cases.

Conclusions:

  • Deep learning significantly enhanced AI's accuracy in ACR TI-RADS classification, particularly for nodules in conglomerates.
  • AI-supported ultrasound is effective for thyroid nodules in non-inflamed tissue but cannot fully replace expert clinicians for complex cases.
  • The rapid learning curve of AI in this application is highly encouraging for future clinical integration.