ArticleiScience2023
AI diagnosis of Bethesda category IV thyroid nodules.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 4 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Integration of AI-driven ultrasound and molecular testing to improve diagnosis of indeterminate thyroid nodules.Frontiers in endocrinology · 2026Pooled it
- Diagnostic performance of ultrasound characteristics-based artificial intelligence models for thyroid nodules: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Beyond genomics: artificial intelligence-powered diagnostics for indeterminate thyroid nodules-a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Global research trends in the application of artificial intelligence in oncology care: a bibliometric study.Frontiers in oncology · 2024Pooled it
- Machine Learning for diagnosis of malignant thyroid nodules based on thyroid ultrasound: Systematic review and meta-analysis of studies with external datasets.European journal of radiology open · 2026Article
- Development and validation of a predictive nomogram for high-risk thyroid nodules: a retrospective analysis of sedentary time, insomnia, and elevated weight.Frontiers in oncology · 2026Article
- Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning.Scientific reports · 2025Article
- Papillary Thyroid Carcinoma With a Rare TFG-MET Fusion in a Chernobyl Survivor: A Case Report.Cureus · 2025Article
- Multimodal GPT model for assisting thyroid nodule diagnosis and management.NPJ digital medicine · 2025Article
- A model based on Chinese thyroid imaging reporting and data systems for predicting Bethesda III/IV thyroid nodules.Frontiers in endocrinology · 2025Article
- Prediction of malignancy risk in Bethesda III nodules: development and validation of multiple machine learning models.Frontiers in endocrinology · 2025Article
- A cascaded clinical-ultrasound-biochemical model for precise prediction before thyroid nodule fine-needle aspiration biopsy.Frontiers in medicine · 2025Article
- Thyroglobulin-to-tumor volume ratio combined with ultrasound features for diagnosing thyroid follicular neoplasms.Frontiers in endocrinology · 2025Article
- Deep learning model for diagnosis of thyroid nodules with size less than 1 cm: A multicenter, retrospective study.European journal of radiology open · 2024Article
- Role of clinicopathologic and sonographic characteristics for the management of patients with cytological diagnosis of thyroid follicular neoplasm.Gland surgery · 2024Article
- Optimizing thyroid AUS nodules malignancy prediction: a comprehensive study of logistic regression and machine learning models.Frontiers in endocrinology · 2024Article
- Utilizing machine learning for early screening of thyroid nodules: a dual-center cross-sectional study in China.Frontiers in endocrinology · 2024Article
- Establishment and validation of a multivariate logistic model for risk factors of thyroid nodules using lasso regression screening.Frontiers in endocrinology · 2024Article
- A model based on C-TIRADS combined with SWE for predicting Bethesda I thyroid nodules.Frontiers in oncology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thyroid nodules are a common disease, and fine needle aspiration cytology (FNAC) is the primary method to assess their malignancy. For the diagnosis of follicular thyroid nodules, however, FNAC has limitations. FNAC can classify them only as Bethesda IV nodules, leaving their exact malignant status and pathological type undetermined. This imprecise diagnosis creates difficulties in selecting the follow-up treatment. In this retrospective study, we collected ultrasound (US) image data of Bethesda IV thyroid nodules from 2006 to 2022 from five hospitals. Then, US image-based artificial intelligence (AI) models were trained to identify the specific category of Bethesda IV thyroid nodules. We tested the models using two independent datasets, and the best AI model achieved an area under the curve (AUC) between 0.90 and 0.95, demonstrating its potential value for clinical application. Our research findings indicate that AI could change the diagnosis and management process of Bethesda IV thyroid nodules.
Indexed as
Identifiers
What OpenQuestion holds
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.