ArticleAnnals of translational medicine2019
Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study.
Article in Annals of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed, 80 citations in OpenAlex.
- Comparison of breast ultrasound image classification accuracy between convolutional neural networks and human experts using multicenter external validation cohort data.Journal of medical ultrasonics (2001) · 2026Article
- Deep learning-based classification of thyroid nodules using uncertainty-aware multi-modal ultrasound imaging.Scientific reports · 2026Article
- Automated thyroid nodule classification in ultrasound imaging using a hybrid vision transformer and Wasserstein GAN with gradient penalty.Scientific reports · 2025Article
- Enhancing automatic diagnosis of thyroid nodules from ultrasound scans leveraging deep learning models.Scientific reports · 2025Article
- Evaluating ChatGPT's recommendations for systematic treatment decisions in recurrent or metastatic head and neck squamous cell carcinoma: Perspectives from experts and junior doctors.International journal of cancer · 2025Article
- The Value of Multimodal Ultrasound Based on Machine Learning Algorithms in the Diagnosis of Benign and Malignant Thyroid Nodules of TI-RADS Category 4: A Single-Center Retrospective Study.Current medical imaging · 2025Article
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- ThyroNet-X4 genesis: an advanced deep learning model for auxiliary diagnosis of thyroid nodules' malignancy.Scientific reports · 2025Article
- Thyroid nodule and lymph node metastasis assessment from ultrasound images using deep learning.Frontiers in neuroscience · 2025Article
- Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network.Computational and structural biotechnology journal · 2025Article
- Investigation of multidirectional toxicity induced by high-dose molybdenum exposure with Allium test.Scientific reports · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Diagnosis of Salivary Gland Tumors Using Transfer Learning with Fine-Tuning and Gradual Unfreezing.Diagnostics (Basel, Switzerland) · 2023Article
- [Review on ultrasonographic diagnosis of thyroid diseases based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images.BMC bioinformatics · 2023Article
- Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: a retrospective study.Scientific reports · 2023Article
- Artificial Intelligence for Pre-operative Diagnosis of Malignant Thyroid Nodules Based on Sonographic Features and Cytology Category.World journal of surgery · 2023Article
- Artificial intelligence in diagnostic ultrasonography.Diagnostic and interventional radiology (Ankara, Turkey) · 2023Review
- Artificial intelligence in thyroid ultrasound.Frontiers in oncology · 2023Review
- Diagnosis of anomalies based on hybrid features extraction in thyroid images.Multimedia tools and applications · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo explore the ability of the deep learning network Inception-v3 to differentiate between papillary thyroid carcinomas (PTCs) and benign nodules in ultrasound images.
methodsA total of 2,836 thyroid ultrasound images from 2,235 patients were divided into a training dataset and a test dataset. Inception-v3 was trained and tested to crop the margin of the images of nodules and provide a differential diagnosis. The sizes and sonographic features of nodules were further analysed to identify the factors that may influence diagnostic efficiency. Statistical analyses included χ
resultsThere were 1,275 PTCs and 1,162 benign nodules in the training group and 209 PTCs and 190 benign nodules in the test group. A margin size of 50 pixels and an input size of 384×384 showed the best outcome after training, and these parameters were selected for the test group. In the test group, the sensitivity and specificity for Inception-v3 were 93.3% (195/209) and 87.4% (166/190), respectively. Inception-v3 displayed the highest accuracy for 0.5-1.0 cm nodules. The accuracy differed according to the margin description (P=0.024). Taller nodules were more accurately diagnosed than were wider nodules (P=0.015). Microcalcification [odds ratio (OR) =0.254, 95% confidence interval (CI): 0.076-0.847, P=0.026] and taller shape (OR =0.243, 95% CI: 0.073-0.810, P=0.021) were negatively associated with misdiagnosis rate.
conclusionsInception-v3 can achieve an excellent diagnostic efficiency. Nodules that are 0.5-1.0 cm in size and have microcalcification and a taller shape can be more accurately diagnosed by Inception-v3.
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