TY  - JOUR
AU  - Barczyński, Marcin
AU  - Stopa-Barczyńska, Małgorzata
AU  - Wojtczak, Beata
AU  - Czarniecka, Agnieszka
AU  - Konturek, Aleksander
PY  - 2020
TI  - Clinical validation of S-DetectTM mode in semi-automated ultrasound classification of thyroid lesions in surgical office
JF  - Gland Surgery; Vol 9, Supplement 2 (February 19, 2020): Gland Surgery (Novel Technologies in Endocrine Surgery)
Y2  - 2020
KW  - 
N2  - Background: In recent years well-recognized scientific societies introduced guidelines for ultrasound (US) malignancy risk stratification of thyroid nodules. These guidelines categorize the risk of malignancy in relation to a combination of several US features. Based on these US image lexicons an US-based computer-aided diagnosis (CAD) systems were developed. Nevertheless, their clinical utility has not been evaluated in any study of surgeon-performed office US of the thyroid. Hence, the aim of this pilot study was to validate s-DetectTM mode in semi-automated US classification of thyroid lesions during surgeon-performed office US.  Methods: This is a prospective study of 50 patients who underwent surgeon-performed thyroid US (basic US skills without CAD vs. with CAD vs. expert US skills without CAD) in the out-patient office as part of the preoperative workup. The real-time CAD system software using artificial intelligence (S-DetectTM for Thyroid; Samsung Medison Co.) was integrated into the RS85 US system. Primary outcome was CAD system added-value to the surgeon-performed office US evaluation. Secondary outcomes were: diagnostic accuracy of CAD system, intra and interobserver variability in the US assessment of thyroid nodules. Surgical pathology report was used to validate the pre-surgical diagnosis.  Results: CAD system added-value to thyroid assessment by a surgeon with basic US skills was equal to 6% (overall accuracy of 82% for evaluation with CAD vs. 76% for evaluation without CAD system; P
UR  - https://gs.amegroups.org/article/view/34714