Craniofacial Phenotyping in Obstructive Sleep Apnea — A Novel Quantitative Photographic Approach

Lee, Richard W. W.; Chan, Andrew S. L.; Grunstein, Ronald R.; Cistulli, Peter A.

doi:10.5665/sleep/32.1.37

Cited by 43 publications

(11 citation statements)

References 28 publications

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“…Photographic measurements, with the advantages of non-invasiveness and easy acceptance, have been applied in adults for craniofacial assessments to predict OSA. 13 , 14 Sutherland also confirmed that clinical photography is feasible for evaluating facial features in children with OSA. 15 …”

Section: Introductionmentioning

confidence: 71%

“… 19 We comprehensively analyzed the distances, ratios, and angles of both the frontal and profile views, observing a trend towards increased coronal and vertical plane distances in children in the OSA group, which was similarly observed in adults. 13 Lee et al discovered that the strongest correlations were demonstrated between tongue volume and face width when compared with BMI in adults. 19 Our study identified LFW as a marker that can distinguish OSA from other children.…”

Section: Discussionmentioning

confidence: 99%

“…Craniofacial Photogrammetry has been widely used to evaluate maxillofacial structures in adults with OSA. 9 , 13 We applied an accessible smartphone to photograph the front and right sides of the subjects. Subjects were required to look straight and place objects of interest in front if necessary to keep the natural head posture, and their ears were symmetrically visible in the front photo.…”

Section: Methodsmentioning

confidence: 99%

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Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea

Wang¹,

Xu²,

Zhao³

et al. 2023

NSS

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Purpose Distinguishing obstructive sleep apnea (OSA) in a high-risk population remains challenging. This study aimed to investigate clinical features to identify children with OSA combined with craniofacial photographic analysis. Methods One hundred and forty-five children (30 controls, 62 with primary snoring, and 53 with OSA) were included. Differences in general demographic characteristics and surface facial morphology among the groups were compared. Risk factors and prediction models for determining the presence of OSA (obstructive sleep apnea-hypopnea index>1) were developed using logistic regression analysis. Results The BMI (z-score), tonsil hypertrophy, and lower face width (adjusted age, gender, and BMI z-score) were showed significantly different in children with OSA compared with primary snoring and controls (adjusted p<0.05). The screening model based on clinical features and photography measurements correctly classified 79.3% of the children with 64.2% sensitivity and 89.1% specificity. The area under the curve of the model was 81.0 (95% CI, 73.5–98.4%). Conclusion A screening model based on clinical features and photography measurements would be helpful in clinical decision-making for children with highly suspected OSA if polysomnography remains inaccessible in resource-stretched healthcare systems.

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Section: Introductionmentioning

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea

Wang¹,

Xu²,

Zhao³

et al. 2023

NSS

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“…Since imaging modalities are cumbersome and expensive, recent research has investigated the predictive value of facial imaging [27]. Lee et al [28] analyzed frontal and profile images of 180 patients, manually deriving measurements on the face and neck to classify subjects with or without OSA using logistic regression.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans

Hanif

Leary

Schneider

et al. 2021

IEEE J. Biomed. Health Inform.

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“…The study of craniofacial morphology to evaluate the risk of insurgence of OSAs in patients was studied [18]. The Neck circumference, Facial profile, mandible length and mandible position were the most commonly evaluated [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Correlation between Craniofacial Structures and Severity of Obstructive Sleep Apnea

Ciavarella¹,

Laurenziello²,

Russo³

et al. 2018

Dentistry

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Objective: In the present paper authors evaluated, on supine lateral head films, the correlation of Upper Airway Space (UAS), posterior maxillary structures (i.e., palatal morphology), maxillo-mandibular and hyoid position with different severity of OSA evaluated on polysomnography test. Materials and Methods: One hundred patients (mean age 51.4 years old; 92 man and 8 women) with OSA were enrolled in the present study. An Epworth questionnaire and a complete overnight Polysomnography (PSG) were done. Head films were taken with the patient in supine position fixed in a cephalostat, in centric occlusion, with adequate visualization of reference structures. Results: The cephalometric evaluation showed how patients presented a reduction of Upper airway space, a post inclination of the mandible, an increase of the ANB Angle and an increase of palatal length, palatal height and palatal angle. At Spearman test a great correlation between IAS and OSA data, a direct correlation between the palatal length and thickness with IAS and an inverse correlation between the mandibular post inclination and backward position and IAS were evaluated. Conclusion: In the present paper authors showed how PSG (AHI, SO2 and Nadir) data was correlated above all to IAS. The modification of IAS was related to the mandibular vertical and sagittal position and to palatal length and thickness.

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Craniofacial Phenotyping in Obstructive Sleep Apnea — A Novel Quantitative Photographic Approach

Cited by 43 publications

References 28 publications

Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea

Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans

Correlation between Craniofacial Structures and Severity of Obstructive Sleep Apnea

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