2023
DOI: 10.21037/jtd-22-734
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Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction

Abstract: Background: Obstructive sleep apnea (OSA) is a common sleep disorder. However, current diagnostic methods are labor-intensive and require professionally trained personnel. We aimed to develop a deep learning model using upper airway computed tomography (CT) to predict OSA and to warn the medical technician if a patient has OSA while the patient is undergoing any head and neck CT scan, even for other diseases.Methods: A total of 219 patients with OSA [apnea-hypopnea index (AHI) ≥10/h] and 81 controls (AHI <10/h… Show more

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Cited by 6 publications
(1 citation statement)
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“…Wavelet decomposition of the nocturnal heart rate variability represented an efficient marker of OSA [8]. A model for predicting OSA using upper airway computerised tomography (CT) and deep learning has been evaluated in 219 patients with OSA and 81 controls [9]. The model enabled CT identification of patients with moderate to severe OSA.…”
Section: Introductionmentioning
confidence: 99%
“…Wavelet decomposition of the nocturnal heart rate variability represented an efficient marker of OSA [8]. A model for predicting OSA using upper airway computerised tomography (CT) and deep learning has been evaluated in 219 patients with OSA and 81 controls [9]. The model enabled CT identification of patients with moderate to severe OSA.…”
Section: Introductionmentioning
confidence: 99%