ArticleJournal of ultrasound2020
Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners.
Article in Journal of ultrasound, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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The trial behind it
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
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis.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 · 2022Pooled it
- Diagnostic accuracy of S-Detect in distinguishing benign and malignant thyroid nodules: A meta-analysis.PloS one · 2022Pooled it
- US-Elastography With Different Techniques for Thyroid Nodule Characterization: Systematic Review and Meta-analysis.Frontiers in oncology · 2022Pooled it
- Revolutionizing thyroid nodule diagnosis in Hashimoto's thyroiditis: AI-driven radiomics and deep learning model.International journal of surgery (London, England) · 2025Article
- Accelerated inference for thyroid nodule recognition in ultrasound imaging using FPGA.Physical and engineering sciences in medicine · 2025Article
- Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.BMJ open · 2025Article
- Role of Artificial Intelligence in Thyroid Cancer Diagnosis.Journal of clinical medicine · 2025Review
- Thyroid nodules: diagnosis and management.Nature reviews. Endocrinology · 2024Review
- Student ultrasound education, current view and controversies. Role of Artificial Intelligence, Virtual Reality and telemedicine.The ultrasound journal · 2024Review
- Article
- Artificial intelligence in thyroid ultrasound.Frontiers in oncology · 2023Review
- Review
- The European Institute of Oncology Thyroid Imaging Reporting and Data System for Classification of Thyroid Nodules: A Prospective Study.Journal of clinical medicine · 2022Article
- Article
- Accuracy of Ultrasound Diagnosis of Thyroid Nodules Based on Artificial Intelligence-Assisted Diagnostic Technology: A Systematic Review and Meta-Analysis.International journal of endocrinology · 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
- Discriminative deep learning based benignity/malignancy diagnosis of dermatologic ultrasound skin lesions with pretrained artificial intelligence architecture.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2022Article
- Reliability of a computer-aided system in the evaluation of indeterminate ultrasound images of thyroid nodules.European thyroid journal · 2022Article
- Comparative Analysis of Computer-Aided Diagnosis and Computer-Assisted Subjective Assessment in Thyroid Ultrasound.Life (Basel, Switzerland) · 2021Article
- Multiparametric ultrasound evaluation of parotid gland tumors: B-mode and color Doppler in comparison and in combination with contrast-enhanced ultrasound and elastography. A case report of a misleading diagnosis.Journal of ultrasound · 2021Article
Corrections and comments
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Authors and funding
20 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeComputer-aided diagnosis (CAD) may improve interobserver agreement in the risk stratification of thyroid nodules. This study aims to evaluate the performance of the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification as estimated by an expert radiologist, a senior resident, a medical student, and a CAD system, as well as the interobserver agreement among them.
methodsBetween July 2016 and 2018, 107 nodules (size 5-40 mm, 27 malignant) were classified according to the K-TIRADS by an expert radiologist and CAD software. A third-year resident and a medical student with basic imaging training, both blinded to previous findings, retrospectively estimated the K-TIRADS classification. The diagnostic performance was calculated, including sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve.
resultsThe CAD system and the expert achieved a sensitivity of 70.37% (95% CI 49.82-86.25%) and 81.48% (61.92-93.7%) and a specificity of 87.50% (78.21-93.84%) and 88.75% (79.72-94.72%), respectively. The specificity of the student was significantly lower (76.25% [65.42-85.05%], p = 0.02).
conclusionIn our opinion, the CAD evaluation of thyroid nodules stratification risk has a potential role in a didactic field and does not play a real and effective role in the clinical field, where not only images but also specialistic medical practice is fundamental to achieve a diagnosis based on family history, genetics, lab tests, and so on. The CAD system may be useful for less experienced operators as its specificity was significantly higher.
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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.