Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review
Background: Obstructive sleep apnea (OSA) is a prevalent and potentially severe sleep disorder characterized by repeated interruptions in breathing during sleep. Machine learning models have been increasingly applied in various aspects of OSA research, including diagnosis, treatment optimization, an...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025001527 |
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| author | Matheus Lima Diniz Araujo Trevor Winger Samer Ghosn Carl Saab Jaideep Srivastava Louis Kazaglis Piyush Mathur Reena Mehra |
| author_facet | Matheus Lima Diniz Araujo Trevor Winger Samer Ghosn Carl Saab Jaideep Srivastava Louis Kazaglis Piyush Mathur Reena Mehra |
| author_sort | Matheus Lima Diniz Araujo |
| collection | DOAJ |
| description | Background: Obstructive sleep apnea (OSA) is a prevalent and potentially severe sleep disorder characterized by repeated interruptions in breathing during sleep. Machine learning models have been increasingly applied in various aspects of OSA research, including diagnosis, treatment optimization, and developing biomarkers for endotypes and disease mechanisms. Objective: This narrative review evaluates the application of machine learning in OSA research, focusing on model performance, dataset characteristics, demographic representation, and validation strategies. We aim to identify trends and gaps to guide future research and improve clinical decision-making that leverages machine learning. Methods: This narrative review examines data extracted from 254 scientific publications published in the PubMed database between January 2018 and March 2023. Studies were categorized by machine learning applications, models, tasks, validation metrics, data sources, and demographics. Results: Our analysis revealed that most machine learning applications focused on OSA classification and diagnosis, utilizing various data sources such as polysomnography, electrocardiogram data, and wearable devices. We also found that study cohorts were predominantly overweight males, with an underrepresentation of women, younger obese adults, individuals over 60 years old, and diverse racial groups. Many studies had small sample sizes and limited use of robust model validation. Conclusion: Our findings highlight the need for more inclusive research approaches, starting with adequate data collection in terms of sample size and bias mitigation for better generalizability of machine learning models in OSA research. Addressing these demographic gaps and methodological opportunities is critical for ensuring more robust and equitable applications of artificial intelligence in healthcare. |
| format | Article |
| id | doaj-art-e43c7bd4065e4fb4b4d463ac580f5b66 |
| institution | OA Journals |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-e43c7bd4065e4fb4b4d463ac580f5b662025-08-20T02:13:40ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-012816717410.1016/j.csbj.2025.04.033Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative reviewMatheus Lima Diniz Araujo0Trevor Winger1Samer Ghosn2Carl Saab3Jaideep Srivastava4Louis Kazaglis5Piyush Mathur6Reena Mehra7Cleveland Clinic Foundation, Cleveland, OH, USA; Corresponding author.University of Minnesota, Minneapolis, MN, USACleveland Clinic Foundation, Cleveland, OH, USACleveland Clinic Foundation, Cleveland, OH, USAUniversity of Minnesota, Minneapolis, MN, USAEpic Systems Corporation, Verona, WI, USACleveland Clinic Foundation, Cleveland, OH, USAUniversity of Washington, Seattle, WA, USABackground: Obstructive sleep apnea (OSA) is a prevalent and potentially severe sleep disorder characterized by repeated interruptions in breathing during sleep. Machine learning models have been increasingly applied in various aspects of OSA research, including diagnosis, treatment optimization, and developing biomarkers for endotypes and disease mechanisms. Objective: This narrative review evaluates the application of machine learning in OSA research, focusing on model performance, dataset characteristics, demographic representation, and validation strategies. We aim to identify trends and gaps to guide future research and improve clinical decision-making that leverages machine learning. Methods: This narrative review examines data extracted from 254 scientific publications published in the PubMed database between January 2018 and March 2023. Studies were categorized by machine learning applications, models, tasks, validation metrics, data sources, and demographics. Results: Our analysis revealed that most machine learning applications focused on OSA classification and diagnosis, utilizing various data sources such as polysomnography, electrocardiogram data, and wearable devices. We also found that study cohorts were predominantly overweight males, with an underrepresentation of women, younger obese adults, individuals over 60 years old, and diverse racial groups. Many studies had small sample sizes and limited use of robust model validation. Conclusion: Our findings highlight the need for more inclusive research approaches, starting with adequate data collection in terms of sample size and bias mitigation for better generalizability of machine learning models in OSA research. Addressing these demographic gaps and methodological opportunities is critical for ensuring more robust and equitable applications of artificial intelligence in healthcare.http://www.sciencedirect.com/science/article/pii/S2001037025001527Sleep ApneaMachine Learning |
| spellingShingle | Matheus Lima Diniz Araujo Trevor Winger Samer Ghosn Carl Saab Jaideep Srivastava Louis Kazaglis Piyush Mathur Reena Mehra Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review Computational and Structural Biotechnology Journal Sleep Apnea Machine Learning |
| title | Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review |
| title_full | Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review |
| title_fullStr | Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review |
| title_full_unstemmed | Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review |
| title_short | Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review |
| title_sort | status and opportunities of machine learning applications in obstructive sleep apnea a narrative review |
| topic | Sleep Apnea Machine Learning |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025001527 |
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