AI-Assisted Sonography for the Diagnosis of Thyroid Nodules.
Purpose To evaluate the diagnostic accuracy, clinical utility, and workflow integration of artificial intelligence (AI)--assisted sonography for the assessment of thyroid nodules and to assess the tool's potential for improving diagnostic consistency, reducing the number of unnecessary biopsies, and...
| Publicado en: | Radiologic Technology Vol. 98; no. 1; pp. 21 - 28 |
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| Autores principales: | , |
| Formato: | research systematic review Journal Article |
| Publicado: |
American Society of Radiologic Technologists
Sep/Oct2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196324025&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196324025 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00338397 39J jtl: Radiologic Technology issn: 00338397 maglogo: N pubinfo: dt: Sep/Oct2026 vid: 98 iid: 1 pid: 7052 pub: American Society of Radiologic Technologists place: Alburquerque, New Mexico artinfo: ui: 196324025 196324025 196324025 196324025 ppf: 21 ppct: 7 formats: fmt: @attributes: type: P tig: atl: AI-Assisted Sonography for the Diagnosis of Thyroid Nodules. aug: au: Khan, Javeria Rasheed, Arif affil: Lecturer at IBADAT International University Islamabad sug: subj: Thyroid Nodule Diagnosis Ultrasonography Artificial Intelligence Decision Making, Clinical Diagnosis, Computer Assisted Sensitivity and Specificity Biopsy, Needle Workflow Evaluation Human Systematic Review Histocytochemistry Turnaround Time Convolutional Neural Networks Descriptive Statistics Thyroid Nodule Radiography Deep Learning Data Analysis Software Ultrasonography, Doppler Machine Learning Radiomics ab: Purpose To evaluate the diagnostic accuracy, clinical utility, and workflow integration of artificial intelligence (AI)--assisted sonography for the assessment of thyroid nodules and to assess the tool's potential for improving diagnostic consistency, reducing the number of unnecessary biopsies, and assisting with clinical decision-making. Methods Database searches were conducted to identify studies published from January 2018 to June 2025. Eligible studies compared AI-based models for thyroid sonography using histopathology or fine-needle aspiration (FNA) as reference standards. Data extraction was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 criteria. Variables included AI architecture, sonography mode or technique, study design, clinical impact measures (ie, FNA reduction, time savings), and diagnostic metrics (ie, sensitivity, specificity, and area under curve). Results A total of 30 studies were included. Convolutional neural networks were the most common architecture. Area under curve values ranged from 0.78 to 0.97, specificity from 70.4% to 90.7% and sensitivity from 75.6% to 94.0%. Clinical advantages included a 10% to 45% reduction in the number of FNAs performed, with some studies reporting between 2.5 to 3.1 minutes of interpretation time saved per case. Risk-of-bias assessment determined 53.3% of the studies to be low risk, 33.3% to be moderate risk, and 13.3% to be high risk, primarily because of the retrospective design or small sample size. Discussion Thyroid nodules are prevalent; however, interpretation of sonograms is prone to interobserver variability. Results from this systematic review indicate that AI-aided sonography exhibits high levels of diagnostic efficacy and quantifiable clinical advantages, especially when combined with the American College of Radiology Thyroid Imaging and Reporting Data System and externally validated. Conclusion This review maps the evolution of experimental models to clinically implementable systems and calls for prospective multicenter trials, standardized reporting, and explainable AI to facilitate safe and reproducible usage in routine thyroid imaging. These findings support Sustainable Development Goal (SDG) 3 (good health and well-being) by promoting higher-quality diagnostic care and SDG 9 (industry, innovation, and infrastructure) by advancing AI in medical imaging. pubtype: Academic Journal doctype: research systematic review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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