ArticleScientific reports2024
Automated diagnosis of adenoid hypertrophy with lateral cephalogram in children based on multi-scale local attention.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Multidimensional analysis of the impact of adenoid hypertrophy on pediatric dentofacial and airway structures and construction of a diagnostic model.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Upper airway dimensions in adolescents with different craniofacial growth patterns: a retrospective cross-sectional comparison of independent samples from 2005 and 2020.BMC oral health · 2026Article
- Interpretable two-stage deep learning for pediatric obstructive sleep apnea diagnosis using lateral cephalograms.Frontiers in pediatrics · 2026Article
- Automatic Identification of Adenoid Hypertrophy via Ensemble Deep Learning Models Employing X-ray Adenoid Images.Journal of imaging informatics in medicine · 2025Article
- Optimized deep learning model for diagnosing tonsil and adenoid hypertrophy through X-rays.Frontiers in oncology · 2025Article
- Clinical features, pathophysiological mechanisms, and multidisciplinary management strategies for rhinitis-induced adenoid facies in children and adolescents: a review.Frontiers in allergy · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Adenoid hypertrophy can lead to adenoidal mouth breathing, which can result in "adenoid face" and, in severe cases, can even lead to respiratory tract obstruction. The Fujioka ratio method, which calculates the ratio of adenoid (A) to nasopharyngeal (N) space in an adenoidal-cephalogram (A/N), is a well-recognized and effective technique for detecting adenoid hypertrophy. However, this process is time-consuming and relies on personal experience, so a fully automated and standardized method needs to be designed. Most of the current deep learning-based methods for automatic diagnosis of adenoids are CNN-based methods, which are more sensitive to features similar to adenoids in lateral views and can affect the final localization results. In this study, we designed a local attention-based method for automatic diagnosis of adenoids, which takes AdeBlock as the basic module, fuses the spatial and channel information of adenoids through two-branch local attention computation, and combines the downsampling method without losing spatial information. Our method achieved mean squared error (MSE) 0.0023, mean radial error (MRE) 1.91, and SD (standard deviation) 7.64 on the three hospital datasets, outperforming other comparative methods.
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.