ArticleWorld journal of surgical oncology2019
Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network.
Article in World journal of surgical oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 4 of them syntheses that pooled it.
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Who cites it
66 citing papers in PubMed, 4 syntheses or guidelines pooled it, 166 citations in OpenAlex.
- Beyond genomics: artificial intelligence-powered diagnostics for indeterminate thyroid nodules-a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Diagnostic performance of ultrasound characteristics-based artificial intelligence models for thyroid nodules: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- 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
- Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations.RMD open · 2019Pooled it
- Artificial Intelligence for Thyroid Nodule Diagnosis: From Multimodal Integration to Clinical Utility.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence-based systems as auxiliary tools for thyroid nodule malignancy detection on ultrasound: a systematic review and meta-analysis of diagnostic accuracy.Gland surgery · 2026Article
- The Current Research Landscape on Integrating Artificial Intelligence with Ultrasound Imaging for Cancer Diagnosis: A Dual-Database Bibliometric Study.Current medicinal chemistry · 2026Article
- Assessing agreement with a single-center expert consensus: artificial intelligence-assisted teleultrasound for thyroid nodules categorized as C-TIRADS 4A or higher.Frontiers in endocrinology · 2026Article
- Improving Non-Invasive Prediction of Thyroid Nodule Malignancy: A Machine Learning-Based Clinical Approach.Cancer management and research · 2026Article
- Application research of artificial intelligence software in the analysis of thyroid nodule ultrasound image characteristics.PloS one · 2025Article
- Identification of lesion location and discrimination between benign and malignant findings in thyroid ultrasound imaging.Scientific reports · 2024Article
- Video-based AI module with raw-scale and ROI-scale information for thyroid nodule diagnosis.Heliyon · 2024Article
- Real-time carotid plaque recognition from dynamic ultrasound videos based on artificial neural network.Ultraschall in der Medizin (Stuttgart, Germany : 1980) · 2024Article
- Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- A Multi-View Deep Learning Model for Thyroid Nodules Detection and Characterization in Ultrasound Imaging.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules.Computer modeling in engineering & sciences : CMES · 2023Article
- Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation.Journal of digital imaging · 2023Article
- Diagnostic value of a computer-assisted diagnosis system for the ultrasound features in thyroid nodules.Archives of endocrinology and metabolism · 2023Article
- Objective assessment of segmentation models for thyroid ultrasound images.Journal of ultrasound · 2023Article
6 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated.
methodsThe ultrasound images of 276 patients were retrospectively selected. The diagnoses of the radiologists were determined according to the Thyroid Imaging Reporting and Data System; the images were automatically recognized and diagnosed by the established artificial intelligence system. Pathological diagnosis was the gold standard for the final diagnosis. The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared.
resultsThe artificial intelligence diagnosis system correctly identified the lesion area, with an area under the receiver operating characteristic (ROC) curve of 0.902, which is higher than that of the radiologists (0.859). This finding indicates a higher diagnostic accuracy (p = 0.0434). The sensitivity, positive predictive value, negative predictive value, and accuracy of the artificial intelligence diagnosis system for the diagnosis of malignant thyroid nodules were 90.5%, 95.22%, 80.99%, and 90.31%, respectively, and the performance did not significantly differ from that of the radiologists (p > 0.05). The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98%, p = 0.026).
conclusionsCompared with the performance of experienced radiologists, the artificial intelligence system has comparable sensitivity and accuracy for the diagnosis of malignant thyroid nodules and better diagnostic ability for benign thyroid nodules. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules.
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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.