ArticleScientific reports2023
Screening obstructive sleep apnea patients via deep learning of knowledge distillation in the lateral cephalogram.
Article in Scientific reports, 2023. 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, 8 citations in OpenAlex.
- Three-class obstructive sleep apnea severity assessment: a parallel AHI and ODI explainable artificial intelligence framework using craniofacial-enriched clinical data.Sleep & breathing = Schlaf & Atmung · 2026Article
- Integrative Assessment of Cephalometric and Polysomnographic Features in Paediatric Obstructive Sleep Apnea: A Cross-Sectional Study.Nature and science of sleep · 2026Article
- Interpretable two-stage deep learning for pediatric obstructive sleep apnea diagnosis using lateral cephalograms.Frontiers in pediatrics · 2026Article
- An Exploration of Craniofacial Features in Obstructive Sleep Apnea Using the Explainable Deep Learning Approaches.Nature and science of sleep · 2026Article
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Advances in Machine Learning Prediction Models for the Screening of Obstructive Sleep Apnea in Adults.Nature and science of sleep · 2025Review
- Lightweight and efficient deep learning models for fruit detection in orchards.Scientific reports · 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
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lateral cephalogram in orthodontics is a valuable screening tool on undetected obstructive sleep apnea (OSA), which can lead to consequences of severe systematic disease. We hypothesized that a deep learning-based classifier might be able to differentiate OSA as anatomical features in lateral cephalogram. Moreover, since the imaging devices used by each hospital could be different, there is a need to overcome modality difference of radiography. Therefore, we proposed a deep learning model with knowledge distillation to classify patients into OSA and non-OSA groups using the lateral cephalogram and to overcome modality differences simultaneously. Lateral cephalograms of 500 OSA patients and 498 non-OSA patients from two different devices were included. ResNet-50 and ResNet-50 with a feature-based knowledge distillation models were trained and their performances of classification were compared. Through the knowledge distillation, area under receiver operating characteristic curve analysis and gradient-weighted class activation mapping of knowledge distillation model exhibits high performance without being deceived by features caused by modality differences. By checking the probability values predicting OSA, an improvement in overcoming the modality differences was observed, which could be applied in the actual clinical situation.
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.