Evidence map›Paper›PMID 39194422›Full record

ReviewAdvances in respiratory medicine2024

A Survey Study of the 3D Facial Landmark Detection Techniques Used as a Screening Tool for Diagnosis of the Obstructive Sleep Apnea Syndrome.

Rastislav Hornák, František Duchoň

Abstract readReview
In one paragraph

Review in Advances in respiratory medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Rastislav HornákInstitute of Robotics and Cybernetics, Slovak University of Technology, Ilkovičova 3, 84104 Bratislava, Slovakia.
František DuchoňInstitute of Robotics and Cybernetics, Slovak University of Technology, Ilkovičova 3, 84104 Bratislava, Slovakia.ORCID 0000-0003-4140-9737

Funding

Faculty of Electrical Engineering and Information Technology of STU in Bratislava AIRMinistry of Education, Research, Development and Youth of the Slovak Republic KEGA 028STU-4/2022Ministry of Education, Research, Development and Youth of the Slovak Republic NFP313010ATR9
6 · The paper itself

Abstract

Obstructive Sleep Apnea (OSA) is a common disorder affecting both adults and children. It is characterized by repeated episodes of apnea (stopped breathing) and hypopnea (reduced breathing), which result in intermittent hypoxia. We recognize pediatric and adult OSA, and this paper focuses on pediatric OSA. While adults often suffer from daytime sleepiness, children are more likely to develop behavioral abnormalities. Early diagnosis and treatment are important to prevent negative effects on children's development. Without the treatment, children may be at increased risk of developing high blood pressure or other heart problems. The gold standard for OSA diagnosis is the polysomnography (sleep study) PSG performed at a sleep center. Not only is it an expensive procedure, but it can also be very stressful, especially for children. Patients have to stay at the sleep center during the night. Therefore, screening tools are very important. Multiple studies have shown that OSA screening tools can be based on facial anatomical landmarks. Anatomical landmarks are landmarks located at specific anatomical locations. For the purpose of the screening tool, a specific list of anatomical locations needs to be identified. We are presenting a survey study of the automatic identification of these landmarks on 3D scans of the patient's head. We are considering and comparing both knowledge-based and AI-based identification techniques, with a focus on the development of the automatic OSA screening tool.

Indexed as

Sleep Apnea, ObstructiveAdultAnatomic LandmarksChildFaceFemaleHumansImaging, Three-DimensionalMaleMass ScreeningPolysomnography3D anatomical landmark detectiondata augmentationdeep learninghybrid template and knowledge-based landmark detectionobstructive sleep apneatransfer learning

Identifiers

PMID39194422
PMCPMC11351861

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.