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External validation of AIBx, an artificial intelligence model for risk stratification, in thyroid nodules by Kristine Z Swan; Johnson Thomas; Viveque E Nielsen; Marie Louise Jespersen; Steen J Bonnema is a Medicine article available to read on EtoBox.

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## Background Artificial intelligence algorithms could be used to risk-stratify thyroid nodules and may reduce the subjectivity of ultrasonography. One such algorithm is AIBx which has shown good performance. However, external validation is crucial prior to clinical implementation. ## Materials and methods Patients harboring thyroid nodules 1–4 cm in size, undergoing thyroid surgery from 2014 to 2016 in a single institution, were included. A histological diagnosis was obtained in all cases. Medullary thyroid cancer, metastasis from other cancers, thyroid lymphomas, and purely cystic nodules were excluded. Retrospectively, transverse ultrasound images of the nodules were analyzed by AIBx, and the results were compared with histopathology and Thyroid Imaging Reporting and Data System (TIRADS), calculated by experienced physicians. ## Results Out of 329 patients, 257 nodules from 209 individuals met the eligibility criteria. Fifty-one nodules (20%) were malignant. AIBx had a negative predictive value (NPV) of 89.2%. Sensitivity, specificity, and positive predictive values (PPV) were 78.4, 44.2, and 25.8%, respectively. Considering both TIRADS 4 and TIRADS 5 nodules as malignant lesion

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Author

Kristine Z Swan; Johnson Thomas; Viveque E Nielsen; Marie Louise Jespersen; Steen J Bonnema

Publisher

Bioscientifica

Published

2022

Language

EN

Field

Medicine (Health Sciences)