Purpose This study examined the prognostic value of the lateral pharyngeal wall (LPW)-based obstruction and obstructive sleep apnoea (OSA) prediction using ultrasound (US) and MRI (magnetic resonance imaging). Methods One hundred patients with and without OSA were enrolled, according to overnight polysomnography. The LPW thickness (LPWT) was measured using a Philips Ingenia 1.5 T MRI device, and US measurements were carried out at rest and during Müller’s manoeuvre (MM) with a Samsung RS85 device. The obstruction was localised under drug-induced sleep endoscopy. Results Significantly greater LPWT using MRI was observed in the OSA group compared to the control group, while US results showed a significant difference only in the case of LPWT during MM on the left side. Obese patients presented significantly higher LPWT values. A significant correlation between BMI and LPWT was observed. Men presented significantly higher LPWT MRI values and left-sided LPWT using US compared to women. LPWT and AHI parameters were significantly correlated. The severity of LPW obstruction correlated with LPWT, while the LPW collapse significantly correlated with AHI. The severity of LPW collapse differed depending on the AHI values. Using US LPWT values and anthropometric parameters, a 93% effectiveness in OSA prognostication and 89% in LPWT-based obstruction were detected. MRI detected OSA in 90% and LPW-based obstruction in 84%. US successfully detected LPW-based collapse severity in 67%. Conclusion US LPWT measurements were helpful in detecting OSA and LPWT-based obstruction. These examinations may be useful for surgical planning.

To examine the geometrical parameters of the tongue in obstructive sleep apnoea (OSA) based on sex, age and BMI parameters and ultrasound (US) and MRI. The presence of OSA and tongue-based obstruction can be predicted using these parameters. Of 100 patients, 64% were diagnosed with OSA according to overnight polysomnography. MRI and US devices were used to measure tongue parameters. The location of the obstruction was identified using drug-induced sleep endoscopy. Statistical analysis was performed using the quadratic discriminant analysis. Men presented higher tongue volumes and axial diameter during Müller’s maneuver (MM) of US and coronal diameter of the MRI. In women, all examined MRI parameters were significantly correlated with apnoea-hypopnea index (AHI). A stronger correlation between BMI and AHI parameters was observed in women than in men. Using our algorithm, which includes tongue parameters and anthropometric values, the presence of OSA could be predicted in 91% with US and 82% with MRI. The detection of tongue-based obstruction was successful in 89% using US and 87% using MRI, whereas tongue-based obstruction was successful in 70% using US. Using MRI and US of the tongue combined with basic anthropometric parameters, the presence of OSA and tongue-based obstruction can be identified with high precision.

Introduction: Our aim was to investigate the applicability of artificial intelligence in predicting obstructive sleep apnoea (OSA) and upper airway obstruction using ultrasound (US) measurements of subcutaneous adipose tissues (SAT) in the regions of the neck, chest and abdomen. Methods: One hundred patients were divided into mild (32), moderately severe-severe (32) OSA and non-OSA (36), according to the results of the polysomnography. These patients were examined using anthropometric measurements and US of SAT and drug-induced sleep endoscopy. Results: Using SAT US and anthropometric parameters, oropharyngeal obstruction could be predicted in 64% and tongue-based obstruction in 72%. In predicting oropharyngeal obstruction, BMI, abdominal and hip circumferences, submental SAT and SAT above the second intercostal space on the left were identified as essential parameters. Furthermore, tongue-based obstruction was predicted mainly by height, SAT measured 2 cm above the umbilicus and submental SAT. The OSA prediction was successful in 97% using the parameters mentioned above. Moreover, other parameters, such as US-based SAT, with SAT measured 2 cm above the umbilicus and both-sided SAT above the second intercostal spaces as the most important ones. Discussion: Based on our results, several categories of OSA can be predicted using artificial intelligence with high precision by using SAT and anthropometric parameters.

This study aimed to analyse the thickness of the adipose tissue (AT) around the upper airways with anthropometric parameters in the prediction and pathogenesis of OSA and obstruction of the upper airways using artificial intelligence. One hundred patients were enrolled in this prospective investigation, who were divided into control (non-OSA) and mild, moderately severe, and severe OSA according to polysomnography. All participants underwent drug-induced sleep endoscopy, anthropometric measurements, and neck MRI. The statistical analyses were based on artificial intelligence. The midsagittal SAT, the parapharyngeal fat, and the midsagittal tongue fat were significantly correlated with BMI; however, no correlation with AHI was observed. Upper-airway obstruction was correctly categorised in 80% in the case of the soft palate, including parapharyngeal AT, sex, and neck circumference parameters. Oropharyngeal obstruction was correctly predicted in 77% using BMI, parapharyngeal AT, and abdominal circumferences, while tongue-based obstruction was correctly predicted in 79% using BMI. OSA could be predicted with 99% precision using anthropometric parameters and AT values from the MRI. Age, neck circumference, midsagittal and parapharyngeal tongue fat values, and BMI were the most vital parameters in the prediction. Basic anthropometric parameters and AT values based on MRI are helpful in predicting OSA and obstruction location using artificial intelligence.

Purpose The aim of this study was to analyze the effect of obstructive sleep apnea (OSA) on the ultrasound (US) features of the diaphragm and to determine if diaphragmatic US may be a useful screening tool for patients with possible OSA. Methods Patients complaining of snoring were prospectively enrolled for overnight polygraphy using the ApneaLink Air device. Thickness and motion of the diaphragm during tidal and deep inspiration were measured. Logistic regression was used to assess parameters of the diaphragm associated with OSA. Results Of 100 patients, 64 were defined as having OSA. Thicknesses of the left and right hemidiaphragms were significantly different between OSA and control groups. Using a combination of diaphragmatic dimensions, diaphragm dilation, age, sex, and BMI, we developed an algorithm that predicted the presence of OSA with 91% sensitivity and 81% specificity. Conclusion A combination of anthropometric measurements, demographic factors, and US imaging may be useful for screening patients for possible OSA. These findings need to be confirmed in larger sample sizes in different clinical settings.