ArticleEuropean thyroid journal2022
Reliability of a computer-aided system in the evaluation of indeterminate ultrasound images of thyroid nodules.
Article in European thyroid journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Integration of AI-driven ultrasound and molecular testing to improve diagnosis of indeterminate thyroid nodules.Frontiers in endocrinology · 2026Pooled it
- Artificial intelligence in thyroid nodules and cancer: clinical validation and real-world performance.Endocrine connections · 2026Article
- Deep Learning Based on B-Mode and Color Doppler Ultrasound for Differentiation of Primary Thyroid Lymphoma and Hashimoto's Thyroiditis: A Retrospective Single-Center Study.Diagnostics (Basel, Switzerland) · 2026Article
- Systematic review and meta-analysis: diagnostic value of different ultrasound for benign and malignant thyroid nodules.Gland surgery · 2022Article
- Investigating the diagnostic efficiency of a computer-aided diagnosis system for thyroid nodules in the context of Hashimoto's thyroiditis.Frontiers in oncology · 2022Article
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8 authors at 1 institution in 1 country.
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Abstract
Introduction: Computer-aided diagnostic (CAD) programs for malignancy risk stratification from ultrasound (US) imaging of thyroid nodules are being validated both experimentally and in real-world practice. However, they have not been tested for reliability in analyzing difficult or unclear images. Methods: US images with indeterminate characteristics were evaluated by five observers with different experience in US examination and by a commercial CAD program. The nodules, on which the observers widely agreed, were considered concordant and, if there was little agreement, not concordant or difficult to assess. The diagnostic performance of the readers and the CAD program was calculated and compared in both groups of nodule images. Results: In the group of concordant thyroid nodules (n = 37), the clinicians and the CAD system obtained similar levels of accuracy (77.0% vs 74.2%, respectively; P = 0.7) and no differences were found in sensitivity (SEN) (95.0% vs 87.5%, P = 0.2), specificity (SPE) (45.5 vs 49.4, respectively; P = 0.7), positive predictive value (PPV) (75.2% vs 77.7%, respectively; P = 0.8), nor negative predictive value (NPV) (85.6 vs 77.7, respectively; P = 0.3). When analyzing the non-concordant nodules (n = 43), the CAD system presented a decrease in accuracy of 4.2%, which was significantly lower than that observed by the experts (19.9%, P = 0.02). Conclusions: Clinical observers are similar to the CAD system in the US assessment of the risk of thyroid nodules. However, the AI system for thyroid nodules AmCAD-UT® showed more reliability in the analysis of unclear or misleading images.
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