ReviewThyroid research2026
Artificial intelligence-based ultrasound diagnosis of Hashimoto's thyroiditis: a systematic review and meta-analysis.
Review in Thyroid research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundHashimoto's thyroiditis (HT) is the most common autoimmune thyroid disorder, often diagnosed using ultrasound. However, conventional gray-scale ultrasound is subject to high subjectivity and operator dependence. Artificial intelligence (AI)-assisted ultrasound has the potential to enhance diagnostic accuracy, but its clinical value remains unclear due to heterogeneous study designs and reference standards.
objectiveTo systematically review and conduct a diagnostic test accuracy meta-analysis of AI-based gray-scale ultrasound models for diagnosing HT.
methodsWe searched PubMed, Embase, Cochrane Library, Scopus, and Web of Science databases up to February 2026. Studies were included if they used AI models including machine learning (ML) and deep learning (DL) for diagnosing HT via gray-scale ultrasound. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Subgroup analyses were performed based on modeling strategy, reference standard and population.
resultsSixteen studies were included, comprising research from China, Poland, Korea, and Romania. The pooled sensitivity and specificity for AI-based gray-scale ultrasound diagnosis of HT were 0.84 (95% CI: 0.77-0.90) and 0.90 (95% CI: 0.84-0.95), respectively, with a summary AUC of 0.94 (95% CI: 0.91-0.95). DL models showed superior performance, with higher sensitivity (0.87) and specificity (0.92) compared to ML models (sensitivity: 0.81, specificity: 0.88). Non-histopathological reference standards showed better diagnostic accuracy (AUC: 0.96) compared to histopathology (AUC: 0.84). Studies in European populations had higher diagnostic performance (AUC: 0.97) than those in Asian populations (AUC: 0.87).
conclusionsAI-based gray-scale ultrasound offers promising diagnostic performance for HT, with significant variability across studies.
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.