ArticleNPJ digital medicine2025
A deep learning based ultrasound diagnostic tool driven by 3D visualization of thyroid nodules.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- DSDFF-YOLO: an automated computer-aided diagnostic method for post-therapy radioiodine whole-body scan.European radiology · 2026Article
- Article
- Multimodal Ultrasound-Based Decision Support for Bethesda IV Thyroid Nodules: Integration of Clinical, Radiomics, Deep Learning, and Topological Features.Journal of imaging informatics in medicine · 2026Article
- A vision-language foundation model improves preoperative diagnosis of follicular thyroid neoplasms using ultrasound images.NPJ precision oncology · 2026Article
- Training the diagnostic artificial intelligence in thyroid sonography: how well is deep learning truly learning?Updates in surgery · 2026Article
- Thyroid Nodule Detection and Classification on Small Datasets: An Ensemble Deep Learning Approach with Attention Mechanism and Focal Loss.Diagnostics (Basel, Switzerland) · 2026Article
- A reliable and explainable deep learning framework for clinical-grade endocrine disorder risk prediction and decision support.Frontiers in artificial intelligence · 2026Article
- A "calcification"-enhanced deep learning approach for precise differentiation of thyroid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Study on differentiating benign and malignant thyroid nodules based on CT multi-phase artificial intelligence models.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recognizing the limitations of computer-assisted tools for thyroid nodule diagnosis using static ultrasound images, this study developed a diagnostic tool utilizing dynamic ultrasound video, namely Thyroid Nodules Visualization (TNVis), by leveraging a two-stage deep learning framework that involved three-dimensional (3D) visualization. In this multicenter study, 4569 cases were included for framework development, and data from seven hospitals were employed for diagnostic validation. TNVis achieved a Dice similarity coefficient of 0.90 after internal testing. For the external validation, TNVis significantly improved radiologists' performance, reaching an AUC of 0.79, compared to their diagnostic performance without the use of TNVis (AUC: 0.66; p < 0.001) and those with partial assistance (AUC: 0.72; p < 0.001). In conclusion, the TNVis-assisted diagnostic strategy not only significantly improves the diagnostic ability of radiologists but also closely imitates their clinical diagnostic procedures and provides them with an objective 3D representation of the nodules for precise and personalized diagnosis and treatment planning.
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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.