Evidence map›Paper›PMID 39941168›Full record

ReviewDiagnostics (Basel, Switzerland)2025

The Impact of Diagnostic Imaging on Obstructive Sleep Apnea: Feedback from a Narrative Review.

Salvatore Lavalle, Alberto Caranti, Giannicola Iannella, Annalisa Pace, Mario Lentini, Antonino Maniaci, Ruggero Campisi, Luigi La Via, Caterina Giannitto, Edoardo Masiello and 2 more

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Incidence and risk factors correlated with otitis media with effusion in pediatric patients with obstructive sleep apnea: a retrospective nationwide inpatient sample database study.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 · 2025
    Article
  4. Predictive modeling of obstructive sleep apnea using pharyngeal magnetic resonance imaging radiomics and clinical data.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025
    Article
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

12 authors.

Salvatore LavalleDepartment of Medicine and Surgery, University of Enna Kore, 94100 Enna, Italy.ORCID 0009-0009-5556-6259
Alberto CarantiDepartment of Otorhinolaryngology and Audiology, University of Study of Ferrara, 44121 Ferrara, Italy.ORCID 0000-0002-0698-937X
Giannicola IannellaOtorhinolaryngology Department, Sapienza University of Rome, 00042 Rome, Italy.ORCID 0000-0003-1781-2809
Annalisa PaceOtorhinolaryngology Department, Sapienza University of Rome, 00042 Rome, Italy.
Mario LentiniDepartment of Medicine and Surgery, University of Enna Kore, 94100 Enna, Italy.ORCID 0009-0004-8127-5045
Antonino ManiaciDepartment of Medicine and Surgery, University of Enna Kore, 94100 Enna, Italy.ORCID 0000-0002-1251-0185
Ruggero CampisiDepartment of Otorhinolaryngology and Audiology, University of Study of Ferrara, 44121 Ferrara, Italy.
Luigi La ViaDepartment of Anesthesiology and Intensive Care, Policlinico San Marco, 95123 Catania, Italy.ORCID 0000-0002-2156-7554
Caterina GiannittoDepartment of Diagnostic Radiology, IRCCS Humanitas Research Hospital, 20019 Milan, Italy.
Edoardo MasielloDepartment of Radiology, IRCCS San Raffaele Scientific Institute, 20019 Milan, Italy.ORCID 0009-0007-0810-409X
Claudio ViciniDepartment of Otorhinolaryngology and Audiology, University of Study of Ferrara, 44121 Ferrara, Italy.
Daniela MessineoDepartment of Radiological Sciences, Oncology and Anatomo-Pathological Science, "Sapienza" University of Rome, 00184 Rome, Italy.ORCID 0000-0003-3059-4439

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obstructive Sleep Apnea is a prevalent sleep disorder characterized by repeated episodes of partial or complete upper airway obstruction during sleep, leading to disrupted sleep and associated comorbidities. Effective, traditional diagnostic methods, such as polysomnography, have limitations in providing comprehensive anatomical detail. Recent advancements in imaging technology have the potential to revolutionize the diagnosis and management of OSA, offering detailed insights into airway anatomy, function, and dynamics. This paper explores the latest innovations in imaging modalities, including high-resolution magnetic resonance imaging, functional MRI, three-dimensional airway reconstructions, and the integration of artificial intelligence algorithms for enhanced image analysis. We discuss the potential of these technologies to improve the precision of OSA diagnosis, tailor treatment strategies, and predict treatment outcomes. Moreover, we examine the challenges of implementing these advanced imaging techniques in clinical practice, such as cost, accessibility, and the need for validation in diverse patient populations. We also consider the ethical implications of widespread imaging, particularly regarding data security and patient privacy. The future of OSA management is poised for transformation as these imaging technologies promise to provide a more nuanced understanding of the disorder and facilitate personalized treatment approaches. This paper calls for continued research and collaboration across disciplines to ensure these innovations lead to improved patient care and outcomes in the field of sleep medicine.

Indexed as

airway managementartificial intelligence in medicinediagnostic imaging technologiesObstructive Sleep Apnea (OSA)sleep medicine advancement

Identifiers

PMID39941168
PMCPMC11816599

What OpenQuestion holds

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Registered trials

None linked

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.