ArticleEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery2017
Computer-aided diagnosis of malignant or benign thyroid nodes based on ultrasound images.
Article in European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Trial
- Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging.BioMed research international · 2020Trial
- Intelligent Diagnosis of Follicular Carcinoma Thyroid Cancer with a Novel Deep Learning Model.Journal of imaging informatics in medicine · 2026Article
- Diagnostic value of an interpretable machine learning model based on clinical ultrasound features for follicular thyroid carcinoma.Quantitative imaging in medicine and surgery · 2024Article
- Artificial intelligence in thyroid ultrasound.Frontiers in oncology · 2023Review
- Diagnostic performance of artificial intelligence-based computer-aided diagnosis system in longitudinal and transverse ultrasonic views for differentiating thyroid nodules.Frontiers in endocrinology · 2023Article
- Comparison of S-Detect and thyroid imaging reporting and data system classifications in the diagnosis of cytologically indeterminate thyroid nodules.Frontiers in endocrinology · 2023Article
- Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography-A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- A NovelComputational and mathematical methods in medicine · 2022Article
- Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques.Medicina (Kaunas, Lithuania) · 2021Article
- An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images.Quantitative imaging in medicine and surgery · 2021Article
- Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland.Ultrasonography (Seoul, Korea) · 2020Article
- Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis.European thyroid journal · 2020Review
- Computer-Aided Diagnosis System for the Evaluation of Thyroid Nodules on Ultrasonography: Prospective Non-Inferiority Study according to the Experience Level of Radiologists.Korean journal of radiology · 2020Article
- Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives.Frontiers in oncology · 2020Review
- Article
- Update on thyroid ultrasound: a narrative review from diagnostic criteria to artificial intelligence techniques.Chinese medical journal · 2019Article
- Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study.Annals of translational medicine · 2019Article
- Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network.World journal of surgical oncology · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study is to evaluate the diagnostic value of combination of artificial neural networks (ANN) and support vector machine (SVM)-based CAD systems in differentiating malignant from benign thyroid nodes with gray-scale ultrasound images. Two morphological and 65 texture features extracted from regions of interest in 610 2D-ultrasound thyroid node images from 543 patients (207 malignant, 403 benign) were used to develop the ANN and SVM models. Tenfold cross validation evaluated their performance; the best models showed accuracy of 99% for ANN and 100% for SVM. From 50 thyroid node ultrasound images from 45 prospectively enrolled patients, the ANN model showed sensitivity, specificity, positive and negative predictive values, Youden index, and accuracy of 88.24, 90.91, 83.33, 93.75, 79.14, and 90.00%, respectively, the SVM model 76.47, 90.91, 81.25, 88.24, 67.38, and 86.00%, respectively, and in combination 100.00, 87.88, 80.95, 100.00, 87.88, and 92.00%, respectively. Both ANN and SVM had high value in classifying thyroid nodes. In combination, the sensitivity increased but specificity decreased. This combination might provide a second opinion for radiologists dealing with difficult to diagnose thyroid node ultrasound images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.