Deep Learning Analyses of Brain MRI to Identify Sustained Attention Deficit in Treated Obstructive Sleep Apnea: A Pilot Study

Agarwal, Chirag; Gupta, Saransh; Najjar, Muhammad; Weaver, Terri E.; Zhou, Xiaohong Joe; Schonfeld, Dan; Prasad, Bharati

doi:10.1007/s41782-021-00190-0

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…Finally, Agarwal et al. ( 2022 ) employed a convolutional neural network model to accurately categorize MR images into sleep apnea duration (+SAD) and non‐sleep apnea duration (−SAD) categories (Agarwal et al., 2022 ).…”

Section: Resultsmentioning

confidence: 99%

Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments

Ghaderi,

Mohammadi,

Mohammadi

2023

Brain and Behavior

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Background and objectiveObstructive sleep apnea (OSA) is a common sleep disorder that causes intermittent hypoxia and sleep fragmentation, leading to attention impairment and other cognitive deficits. Magnetic resonance imaging (MRI) is a powerful modality that can reveal the structural and functional brain alterations associated with attention impairment in OSA patients. The objective of this systematic review is to identify and synthesize the evidence on MRI biomarkers and neuropsychological assessments of attention deficits in OSA patients.MethodsWe searched the Scopus and PubMed databases for studies that used MRI to measure biomarkers related to attention alteration in OSA patients and reported qualitative and quantitative data on the association between MRI biomarkers and attention outcomes. We also included studies that found an association between neuropsychological assessments and MRI findings in OSA patients with attention deficits.ResultsWe included 19 studies that met our inclusion criteria and extracted the relevant data from each study. We categorized the studies into three groups based on the MRI modality and the cognitive domain they used: structural and diffusion tensor imaging MRI findings, functional, perfusion, and metabolic MRI findings, and neuropsychological assessment findings.ConclusionsWe found that OSA is associated with structural, functional, and metabolic brain alterations in multiple regions and networks that are involved in attention processing. Treatment with continuous positive airway pressure can partially reverse some of the brain changes and improve cognitive function in some domains and in some studies. This review suggests that MRI techniques and neuropsychological assessments can be useful tools for monitoring the progression and response to treatment of OSA patients.

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Section: Resultsmentioning

confidence: 99%

Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments

Ghaderi,

Mohammadi,

Mohammadi

2023

Brain and Behavior

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“…With a high number of cases, the lack of access to datasets from the EOG, EMG, EEG, ECG, SpO2, and respiration signals is still a serious challenge. In Agarwal et al (2022), Schwab et al (2003), and Woughter et al (2015), the researchers used MRI modality for the diagnosis of sleep apnea, but the MRI data is not provided for free, which is another challenge of the dataset. The diagnosis of sleep apnea using ML methods was provided as another challenge.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, a variety of studies on the diagnosis of sleep apnea from magnetic resonance imaging (MRI) modalities are being conducted (Schwab et al, 2003; Woughter et al, 2015). In clinical studies (Agarwal et al, 2022; Schwab et al, 2003; Woughter et al, 2015), researchers investigate to what point apnea affects the structure and function of the brain. So far, no dataset containing MRI modalities has been presented, which is a challenge in this field.…”

Section: Challengesmentioning

confidence: 99%

“…Providing datasets with a high number of cased is of paramount importance for future studies. Furthermore, several clinical studies try to investigate the efficiency of MRI modalities in the diagnosis of apnea (Agarwal et al, 2022; Schwab et al, 2003; Woughter et al, 2015). Thereby, researchers' access to datasets of MRI modalities allows them to study the brain function during sleep apnea and compare the patients' brain structure and the function to those of normal individuals.…”

Section: Future Workmentioning

confidence: 99%

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Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Moridian

Shoeibi

Khodatars

et al. 2022

WIREs Data Min & Knowl

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Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining

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“…Similarly, Shu et al [ 87 ] used rs-fMRI and ML analyses to investigate cognitive impairment in OSA. Agarwal et al [ 88 ] used a convolutional neural network analysis of brain MRI scans to predict whether OSA patients treated with CPAP would experience a negative neurological condition post-treatment. These studies highlight the potential for the image-based analysis of the related and downstream effects of OSA in a multiorgan setting.…”

Section: Applying Machine Learning and Artificial Intelligence To Obs...mentioning

confidence: 99%

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

Cohen,

Kundel,

Robson

et al. 2024

JCM

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Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea–hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

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Deep Learning Analyses of Brain MRI to Identify Sustained Attention Deficit in Treated Obstructive Sleep Apnea: A Pilot Study

Cited by 4 publications

References 17 publications

Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments

Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

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