Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study

Riela, Paolo Marco; Iannella, Giannicola; Lechien, Jérôme R.; Mantia, Ignazio La; Vincentiis, Marco de; Cammaroto, Giovanni; Calvo-Henríquez, Christian; Luca, Milena Di; Chiesa‐Estomba, Carlos Miguel; Saibene, Alberto Maria; Pollicina, Isabella; Stilo, Giovanna; Mauro, Paola Di; Cannavicci, Angelo; Lugo, Rodolfo; Magliulo, Giuseppe; Greco, Antonio; Pace, Annalisa; Meccariello, Giuseppe; Cocuzza, Salvatore; Vicini, Claudio

doi:10.3390/life13030702

Cited by 21 publications

(12 citation statements)

References 27 publications

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“…A recent study used clinical features to identify adult OSA and obtained an AUC value of 0.92, which has an advantage over our predictive model. One possible reason is that part of the predictive characteristics of children come from parents’ description of their children's sleep, which may reduce the reliability of the features ( 51 ).…”

Section: Discussionmentioning

confidence: 99%

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Qin,

Zhang,

et al. 2024

Front. Pediatr.

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ObjectiveThe objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments.Patients and methodsThis study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3–18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants’ data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results.ResultsFeature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity.ConclusionsThis study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

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Section: Discussionmentioning

confidence: 99%

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Qin,

Zhang,

et al. 2024

Front. Pediatr.

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“…Furthermore, the study's Vision Transformer model might have overfit the training set, which would have prevented it from generalizing to previously undiscovered scenarios. To lessen this risk, strategies like regularization and cross-validation could be applied [42]. Therefore, a prospective multi-center study using a bigger cohort with real-time video images and with annotation will be required to further develop our findings.…”

Section: Discussionmentioning

confidence: 99%

Development of an Artificial Intelligence Diagnostic System Using Linked Color Imaging for Barrett’s Esophagus

Takeda,

Asaoka,

Ueyama

et al. 2024

JCM

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Background: Barrett’s esophagus and esophageal adenocarcinoma cases are increasing as gastroesophageal reflux disease increases. Using artificial intelligence (AI) and linked color imaging (LCI), our aim was to establish a method of diagnosis for short-segment Barrett’s esophagus (SSBE). Methods: We retrospectively selected 624 consecutive patients in total at our hospital, treated between May 2017 and March 2020, who experienced an esophagogastroduodenoscopy with white light imaging (WLI) and LCI. Images were randomly chosen as data for learning from WLI: 542 (SSBE+/− 348/194) of 696 (SSBE+/− 444/252); and LCI: 643 (SSBE+/− 446/197) of 805 (SSBE+/− 543/262). Using a Vision Transformer (Vit-B/16-384) to diagnose SSBE, we established two AI systems for WLI and LCI. Finally, 126 WLI (SSBE+/− 77/49) and 137 LCI (SSBE+/− 81/56) images were used for verification purposes. The accuracy of six endoscopists in making diagnoses was compared to that of AI. Results: Study participants were 68.2 ± 12.3 years, M/F 330/294, SSBE+/− 409/215. The accuracy/sensitivity/specificity (%) of AI were 84.1/89.6/75.5 for WLI and 90.5/90.1/91.1/for LCI, and those of experts and trainees were 88.6/88.7/88.4, 85.7/87.0/83.7 for WLI and 93.4/92.6/94.6, 84.7/88.1/79.8 for LCI, respectively. Conclusions: Using AI to diagnose SSBE was similar in accuracy to using a specialist. Our finding may aid the diagnosis of SSBE in the clinic.

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“…This is supported by epidemiological evidence showing that an estimated 50–60% of obese people and patients who have MetS also have OSA, a comorbidity thought to exacerbate MetS’s metabolic, inflammatory, and vascular impairments [ 26 ]. A recent study which used a Support Vector Machines (SVM) ML algorithm for identification of OSA severity, demonstrated a higher average impact of dyslipidemia, choking, diabetes, mood disorders, and familiarity for OSA among the independent variable predictors of OSA severity [ 61 ]. A review summarized that predictors that warrant screening for OSA include typical symptoms (e.g., snoring, restless sleep, and daytime hyperactivity) or risk factors (e.g., neurologic, genetic, and craniofacial disorders) [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

et al. 2023

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This study aimed to analyze the associations of obstructive sleep apnea (OSA) with dental parameters while controlling for socio-demographics, health-related habits, and each of the diseases comprising metabolic syndrome (MetS), its consequences, and related conditions. We analyzed data from the dental, oral, and medical epidemiological (DOME) cross-sectional records-based study that combines comprehensive socio-demographic, medical, and dental databases of a nationally representative sample of military personnel for one year. Analysis included statistical and machine learning models. The study included 132,529 subjects; of these, 318 (0.2%) were diagnosed with OSA. The following parameters maintained a statistically significant positive association with OSA in the multivariate binary logistic regression analysis (descending order from highest to lowest OR): obesity (OR = 3.104 (2.178–4.422)), male sex (OR = 2.41 (1.25–4.63)), periodontal disease (OR = 2.01 (1.38–2.91)), smoking (OR = 1.45 (1.05–1.99)), and age (OR = 1.143 (1.119–1.168)). Features importance generated by the XGBoost machine learning algorithm were age, obesity, and male sex (located on places 1–3), which are well-known risk factors of OSA, as well as periodontal disease (fourth place) and delivered dental fillings (fifth place). The Area Under Curve (AUC) of the model was 0.868 and the accuracy was 0.92. Altogether, the findings supported the main hypothesis of the study, which was that OSA is linked to dental morbidity, in particular to periodontitis. The findings highlight the need for dental evaluation as part of the workup of OSA patients and emphasizes the need for dental and general medical authorities to collaborate by exchanging knowledge about dental and systemic morbidities and their associations. The study also highlights the necessity for a comprehensive holistic risk management strategy that takes systemic and dental diseases into account.

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Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study

Cited by 21 publications

References 27 publications

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Development of an Artificial Intelligence Diagnostic System Using Linked Color Imaging for Barrett’s Esophagus

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis—Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study

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