Abstract
Introduction:
This pilot study aims to evaluate the clinical impact of artificial intelligence–based decision support, Koios Decision Support™, on the diagnostic performance of ultrasound assessment of thyroid nodules, and as a result to avoid fine needle aspiration.
Methods:
This retrospective pilot study was conducted on ultrasound images of thyroid nodules investigated with fine needle aspiration from January 2022 to December 2022. Orthogonal ultrasound images of thyroid nodules, previously investigated with fine needle aspiration, were compared with the Koios Decision Support™ suggestion to perform fine needle aspiration. Surgical histology was used as ground truth.
Results:
A total of 29 patients (76% women) with a mean age of 48 ± 16.5 years were evaluated, n = 15 (52%) were histologically proven benign and n = 14 (48%) were malignant. In the benign group, Koios Decision Support™ suggested avoidable fine needle aspiration in n = 8 (53%). In the malignant group, Koios Decision Support™ suggested follow-up or no fine needle aspiration in n = 2 (14%). Sensitivity is 85.7% (n = 12) (p = 0.027), whereas specificity is 53.3% (n = 8) (p = 0.027). The positive predictive value is 63.2% (n = 12), negative predictive value is 80% (n = 8), false-negative value is 20% (n = 2) and false-positive value is 36.8% (n = 7). Based on artificial intelligence decision, one cancer would have been missed.
Conclusion:
Artificial intelligence can improve specificity without significantly compromising sensitivity. There was a suggested reduction in the fine needle aspiration rate, in the histologically proven benign nodules, by 53%. This had no statistical significance, likely due to the small population, however, it is thought to be the largest study to date. Further investigation with wider-ranging studies is suggested.
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