ArticleBMC oral health2026
A study on a screening and diagnostic model for adenoid faces based on an improved convolutional neural network.
Article in BMC oral health, 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
5 authors.
Funding
Abstract
objectiveTo develop an automated, efficient model for screening and diagnosing adenoid faces by integrating multiple advanced Convolutional Neural Networks (CNNs), thereby providing a scientific basis for clinical diagnostics and large-scale epidemiological screening.
methodsThis case-control study recruited 325 patients with adenoid faces and 445 controls with normal facial morphology from the Department of Pediatric Dentistry at the Stomatology Hospital of Yan’an University Affiliated Hospital (January 2024 - December 2024). Frontal and lateral photographs were collected from all participants (aged 11–14), with no significant demographic differences in age or gender between the groups (P > 0.05). We extracted facial features from the images to build seven distinct models: ViT, CrossViT, SimpleViT, DeepViT, ResNet34, VGG19_BN, and MedMamba. Model performance was rigorously evaluated on a test set using accuracy, precision, sensitivity, F1-score, and specificity, complemented by Precision-Recall (PR) and Receiver Operating Characteristic (ROC) curve analyses.
resultsThe MedMamba model outperformed all other models, achieving an accuracy of 0.933, precision of 0.909, sensitivity of 0.975, F1-score of 0.941, and specificity of 0.882. ROC and PR curve analyses confirmed the robust diagnostic power of all seven models. The MedMamba and ResNet34 models exhibited the highest classification performance, with AUC values of 0.957 and 0.955, respectively. These findings position the MedMamba model as a highly effective preliminary screening tool for large-scale epidemiological surveys, capable of rapidly identifying suspected cases and optimizing resource allocation for subsequent professional assessment.
conclusionThe proposed adenoid faces screening model, built upon an integration of advanced CNNs, exhibits high accuracy and stability. It offers a valuable,objective decision-support tool for clinicians and establishes a solid foundation for future large-scale epidemiological research.
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.