ArticleEndocrine2025
Human-machine collaborative risk assessment model for thyroid nodules based on local attention and multi-scale feature extraction: a multi-center clinical study.
Article in Endocrine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence model based on the detection of key anatomical structures for identifying standard planes on ultrasound of the first-trimester fetal central nervous system: a multicenter study.Quantitative imaging in medicine and surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
purposeWe aimed to evaluate a human-machine collaborative risk assessment model for thyroid nodules using local attention mechanisms and multi-scale feature extraction and compare its performance with those of radiologists of varying experience levels.
methodsA multi-center diagnostic study was conducted using ultrasound image datasets from six hospitals in China. The model was trained on 8397 images from 8063 patients (training set) and validated on 253 images from 245 patients across multiple centers. The diagnostic performance of the model was compared with those of radiologists with varying levels of experience. An assistive strategy was developed where radiologists adjusted their diagnoses based on model results.
resultsThe model achieved recognition accuracies of 0.966, 0.809, 0.826, 0.837, and 0.861 for composition, echogenicity, margin, echogenic foci, and orientation, respectively. The area under the receiver operating characteristic curve (AUROC) for the model in diagnosing benign and malignant nodules was 0.882, significantly higher than that of the junior radiologist (0.789; P < 0.0001). The AUROC of the model was between that of the intermediate (0.837) and senior (0.892) radiologists, with no significant difference compared to either group (both P > 0.05). The assistive strategy improved the AUROC for the junior radiologist from 0.789 to 0.859 (P < 0.0001) and increased sensitivity from 66.11% to 80.00% (P < 0.05), with specificity unchanged.
conclusionThe model accurately identified thyroid nodule risk features and enhanced diagnostic performance, particularly for the junior radiologist, improving sensitivity in diagnosing thyroid nodules.
Indexed as
Identifiers
40408037What 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.