Machine learning‐based model for prediction of outcomes in palatal surgery for obstructive sleep apnoea

Yang, Seong J; Kim, Jong Seung; Chung, Seung Yun; Song, Yong Yun

doi:10.1111/coa.13823

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…The study identified that the ML model with the highest accuracy was lasso logistic regression. The small sample size of 29 patients was a limiting factor in this study, and influenced which ML model was most accurate as the small data set compared to number of variables regularization methods are typically most useful [ 30 ].…”

Section: Main Textmentioning

confidence: 99%

The role of artificial intelligence in the treatment of obstructive sleep apnea

Brennan

Kirby

2023

Journal of Otolaryngology - Head & Neck Surgery

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Background The first-line and most common treatment for obstructive sleep apnea is nasal continuous positive airway pressure, which serves as a pneumatic splint to stabilize the upper airway and is effective when used with appropriate adherence. Continuous positive airway pressure compliance rates remain significantly low despite machine improvements and compliance intervention. Other treatment options include oral appliances, myofunctional therapy, and surgery. The aim of this project is to elucidate the role of artificial intelligence within improving the treatment of obstructive sleep apnea. Methods Related publications between 1999 and 2022 were reviewed from PubMed and Embase databases utilizing search terms “artificial intelligence,” “machine learning,” “obstructive sleep apnea,” and “treatment.” Both authors independently screened the results by title/abstract then by full text review. 126 non-duplicate articles were screened, 38 articles were included after title and abstract screen and 30 articles were included after full text review. The inclusion criteria are outline in the PICO framework and involved studies focused on artificial intelligence application in guiding and evaluating obstructive sleep apnea treatment. Non-English articles were excluded. Results The role of artificial intelligence in the treatment of OSA was categorized into the following sections: Predicting treatment outcomes of various treatment options, Improving/Evaluating treatment, and Personalizing treatment with improving understanding of underlying mechanisms of OSA. Conclusions Artificial intelligence has the capacity to improve the treatment of OSA through predicting outcomes of treatment options, evaluating the treatment the patient is currently utilizing and increasing understanding of the mechanisms that contribute to OSA disease process and physiology. Implementing AI in guiding treatment decisions allows patients to connect with treatment methods that would be most effective on an individual basis.

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Section: Main Textmentioning

confidence: 99%

The role of artificial intelligence in the treatment of obstructive sleep apnea

Brennan

Kirby

2023

Journal of Otolaryngology - Head & Neck Surgery

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Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery

Dong,

Yan,

Zhang

et al. 2024

Heliyon

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Machine Learning in Clinical Diagnosis of Head and Neck Cancer

Black,

Young,

Rogers

et al. 2024

Clinical Otolaryngology

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ObjectiveMachine learning has been effective in other areas of medicine, this study aims to investigate this with regards to HNC and identify which algorithm works best to classify malignant patients.DesignAn observational cohort study.SettingQueen Elizabeth University Hospital.ParticipantsPatients who were referred via the USOC pathway between January 2019 and May 2021.Main Outcome MeasuresPredicting the diagnosis of patients from three categories, benign, potential malignant and malignant, using demographics and symptoms data.ResultsThe classic statistical method of ordinal logistic regression worked best on the data, achieving an AUC of 0.6697 and balanced accuracy of 0.641. The demographic features describing recreational drug use history and living situation were the most important variables alongside the red flag symptom of a neck lump.ConclusionFurther studies should aim to collect larger samples of malignant and pre‐malignant patients to improve the class imbalance and increase the performance of the machine learning models.

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Machine learning‐based model for prediction of outcomes in palatal surgery for obstructive sleep apnoea

Cited by 4 publications

References 10 publications

The role of artificial intelligence in the treatment of obstructive sleep apnea

The role of artificial intelligence in the treatment of obstructive sleep apnea

Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery

Machine Learning in Clinical Diagnosis of Head and Neck Cancer

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