Conventional metrics such as the apnea-hypopnea index (AHI) may not fully capture the diverse clinical manifestations of obstructive sleep apnea (OSA). This study aims to establish a novel OSA subtype classification based on the ...

• New obstructive sleep apnea subtypes identified via apneic and hypopneic hypoxic burden patterns.
• Identified five distinct OSA subtypes correlating with cardiometabolic and brain health outcomes.
• Apneic hypoxic burden links to ...

Self-report questionnaires play a crucial role in healthcare for assessing disease risks, yet their extensive length can be burdensome for respondents, potentially compromising data quality. To address this, machine learning-based shortened ...

• SymScore generates accurate and interpretable score tables for risk assessment.
• It combines machine learning accuracy with practical transparency in healthcare.
• It eases risk assessment for clinicians without needing computational ...

Hearing loss is a crucial global health hazard exerting considerable social and physiological effects on spoken language and cognition. Patients affected by this condition may experience social and professional hardships ...

• We propose a three-phase approach to build a hearing loss prediction model.
• In phase I, two feature selection methods are utilized to select the most influential features.
• In phase II, an oversampling technique is employed to ...

This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. ...

• Reviews recent progress in Motor Imagery-based BCIs.
• Summarizes key findings from 2017 to 2023 studies.
• Examines datasets and pre-processing methods used.
• Analyzes feature extraction and deep learning models.
• Provides ...

Computer vision has emerged as a promising technology with numerous applications in healthcare. This systematic review provides an overview of advancements and challenges associated with computer vision in healthcare. The review highlights the ...

• Comprehensive overview of recent advancements in computer vision for healthcare applications.
• In-depth analysis of algorithms for healthcare computer vision tasks, aiding selection decisions.
• Reviews top computer vision papers in ...

Spine segmentation poses significant challenges due to the complex anatomical structure of the spine and the variability in imaging modalities, which often results in unclear boundaries and overlaps with surrounding tissues. In this research, a ...

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• A lightweight deep learning model for spine segmentation.
• The spatial and channel attention-based approach.
• Less computationally expensive due to MobileNetv3.
• Feature concatenation and segmentation using the upsampling part of ...

Health care images contain a variety of imaging information that has specific features, which can make it challenging to assess and decide on the methods necessitated to safeguard the highly classified visuals from unauthorized exposure during ...

• A hybrid methodology has been employed to securely communicate and also proficiently identify textual content and kinds of characters from intervention text-based healthcare image records.
• Proposed methodology utilizes morphology ...

Augmented Reality (AR) technologies enhance the real world by integrating contextual digital information about physical entities. However, inconsistencies between physical reality and digital augmentations, which may arise from errors in the ...

• Interactive task representing a fundamental application area for AR technologies
• Incongruent AR information with real-world context elicits N400 and P600 components
• •Increased ERP latencies after non-symbolic numbers reflect stimulus ...

The COVID-19 pandemic has emerged as a global health crisis, impacting millions worldwide. Although chest computed tomography (CT) scan images are pivotal in diagnosing COVID-19, their manual interpretation by radiologists is time-consuming and ...

• We refined a multi-national CT scan dataset and proposed XCT-COVID, an automated diagnosis framework using transfer learning.
• XCT-COVID showed excellent generalizability and clinical applicability on the independent and external ...

In the realm of emotion detection, comfort and portability play crucial roles in enhancing user experiences. However, few works study the reduction in the number of electrodes used to detect emotions, and none of them compare the ...

• Evaluated the potential of the low-cost Muse S headband for emotion detection using EEG signals.
• Conducted comprehensive experiments comparing the Muse S with the DEAP dataset's 32-electrode setup.
• Found the Gamma band to be ...

In recent years, deep learning has become a popular tool to analyze and classify medical images. However, challenges such as limited data availability, high labeling costs, and privacy concerns remain significant obstacles. As such, generative ...

• Addressed the gap of detecting incidental vertebral fractures in routine chest CT scans.
• Collected and cleaned a relevant dataset from the American University of Beirut Medical Center (AUBMC).
• Applied transfer learning on a VAE-GAN ...

Emotion recognition holds great promise in healthcare and in the development of affect-sensitive systems such as brain–computer interfaces (BCIs). However, the high cost of labeled data and significant differences in electroencephalogram (EEG) ...

• Proposing a domain-adaptive deep network for EEG-based emotion classification.
• Enhancing cross-domain utilization of a model by mitigating feature distribution discrepancies.
• Proposing a technique for selecting reliable target ...

Recent healthcare advancements highlight the potential of Artificial Intelligence (AI) - and especially, among its subfields, Machine Learning (ML) - in enhancing Breast Cancer (BC) clinical care, leading to improved patient outcomes ...

• Recent works on Artificial Intelligence (AI) applied to breast imaging are reviewed.
• Their quality is assessed via the MI-CLAIM checklist and the HTA Core model.
• 78.84% of the works examine the clinical effectiveness of AI-based ...

Age-related hearing loss (ARHL) is primarily attributed to inner-ear factors, yet the role of age-related middle ear characteristics remains elusive. Employing a finite element (FE) model, we conducted a comparative analysis with clinical data ...

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• This study examines age-related middle ear changes in hearing loss using a finite element model and retrospective clinial data.
• Significant intergroup differences in middle ear function show basilar membrane damge in older adults, ...

Accurate preoperative assessment of ureteral length is crucial for effective ureteral stenting.

Utilize a deep learning approach to measure ureter length on CT urography (CTU) images and compare the obtained results with those ...

• A deep learning model based on CTU images was used to measure ureteral length and performed superiorly to other methods.
• Bland-Altman analysis found a -1.3 mm bias between the model and reference, not significant (P = 0.057).
• The ...

Existing prediction methods for academic majors based on personality traits have notable gaps, including limited model complexity and generalizability.The current study aimed to utilize advanced Machine Learning (ML) algorithms with ...

• Predict advanced machine learning algorithms in psychological assessment and compare them with traditional approaches.
• Applying techniques and concepts of artificial intelligence and machine learning instead of developing and designing ...

Accurate assessment of sleepiness is pivotal in managing the fatigue-associated risks stemming from sleep deprivation. Speech signals are easy to obtain, allowing detection of sleepiness anywhere. Previous machine learning (ML) studies using ...

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• Speech conveys diverse information through vocal components, while electrodermal activity (EDA) offers a more direct measure of sympathetic nervous system (SNS) activity.
• Sleep deprivation significantly involves physical fatigue, which ...

Sleep is an essential biological function that is critical for a healthy and fulfilling life. Available sleep quality assessment tools contain long questionnaires covering a long period of time, not taking into account ...

• SmartHypnos is a freely available Android application for personalized sleep monitoring.
• Daily physical activity is recorded using a single Android device.
• The user answers daily short (three items) questionnaires relating to sleep ...

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