ArticleRadiologie (Heidelberg, Germany)2023
Study on diagnosis of thyroid nodules based on convolutional neural network.
Article in Radiologie (Heidelberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 1 citations in OpenAlex.
- Mechanistic Topology of Notopterygium incisum Essential Oil against Rheumatoid Arthritis: Machine Learning-Guided CASP1 Screening and Nanoemulsion Evaluation.Chinese journal of integrative medicine · 2026Article
- Enhancing diagnostic precision for thyroid C-TIRADS category 4 nodules: a hybrid deep learning and machine learning model integrating grayscale and elastographic ultrasound features.Quantitative imaging in medicine and surgery · 2025Article
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7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveAn artificial intelligence (AI) algorithm based on convolutional neural networks was used in ultrasound diagnosis in order to evaluate its performance in judging the nature of thyroid nodules and nodule classification.
methodsA total of 105 patients with thyroid nodules confirmed by surgery or biopsy were retrospectively analyzed. The properties, characteristics, and classification of thyroid nodules were evaluated by sonographers and by AI to obtain combined diagnoses. Receiver operating characteristic curves were generated to evaluate the performance of AI, the sonographer, and their combined effort in diagnosing the nature of thyroid nodules and classifying their characteristics. In the diagnosis of thyroid nodules with solid components, hypoechoic appearance, indistinct borders, Anteroposterior/transverse diameter ratio > 1(A/T > 1), and calcification performed by sonographers and by AI, the properties exhibited statistically significant differences.
resultsSonographers had a sensitivity of 80.7%, specificity of 73.7%, accuracy of 79.0%, and area under the curve (AUC) of 0.751 in the diagnosis of benign and malignant thyroid nodules. AI had a sensitivity of 84.5%, specificity of 81.0%, accuracy of 84.7%, and AUC of 0.803. The combined AI and sonographer diagnosis had a sensitivity of 92.1%, specificity of 86.3%, accuracy of 91.7%, and AUC of 0.910.
conclusionThe efficacy of a combined diagnosis for benign and malignant thyroid nodules is higher than that of an AI-based diagnosis alone or a sonographer-based diagnosis alone. The combined diagnosis can reduce unnecessary fine-needle aspiration biopsy procedures and better evaluate the necessity of surgery in clinical practice.
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