AIMC Topic: Smartphone

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Proposition of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity.

Sleep & breathing = Schlaf & Atmung
PURPOSE: obstructive sleep apnea is underdiagnosed due to limited access to polysomnography (PSG). We aimed to assess the performances of Apneal, an application recording sound and movements thanks to a smartphone's microphone, accelerometer and gyro...

Enhancing real-time heading estimation for pedestrian navigation via deep learning and smartphone embedded sensors.

Scientific reports
The accurate smartphone-based pedestrian navigation significantly depends on the precise heading estimation. However, heading estimation is still a challenging problem in most pedestrian navigation applications because of the bias of low-cost smartph...

Performance of mental health chatbot agents in detecting and managing suicidal ideation.

Scientific reports
Advances in artificial intelligence (AI) technologies sparked a rapid development of smartphone applications designed to help individuals experiencing mental health problems through an AI-powered chatbot agent. However, the safety of such agents when...

Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.

Journal of medical Internet research
BACKGROUND: Depression is highly recurrent and heterogeneous. The unobtrusive, continuous collection of mobile sensing data via smartphones and wearable devices offers a promising approach to monitor and predict individual depression trajectories, di...

Digitally Enabled AI-Interpreted Salivary Ferning-Based Ovulation Prediction: Feasibility Study.

Journal of medical Internet research
BACKGROUND: Females with irregular or unpredictable cycles, including those with polycystic ovary syndrome (PCOS), have limited options for validated at-home ovulation prediction. The majority of over-the-counter ovulation prediction kits use urinary...

New Release of User-Captured Images from the Oregon Health & Science University Melanoma MoleMapper Project.

Scientific data
We announce the release of the OHSU MoleMapper Smartphone Skin Images dataset which contains over six years of new data acquired from the Oregon Health & Science University's (OHSU) MoleMapper study. This released dataset includes 27,499 mole images ...

Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol for a Case-Control Study.

JMIR research protocols
BACKGROUND: Depression is a mental health condition that affects millions of people worldwide. Although common, it remains difficult to diagnose due to its heterogeneous symptomatology. Mental health questionnaires are currently the most used assessm...

Digital Therapeutics in Cardiovascular Healthcare: A Narrative Review.

Current cardiology reports
The rapid development of digital therapeutics (DTx) presents opportunities for cardiovascular diseases (CVD) intervention. This review aims to summarize the technologies and applications of DTx in the field of cardiovascular healthcare. It seeks to i...

Early warning signals of bipolar relapse: Investigating critical slowing down in smartphone data.

Journal of affective disorders
BACKGROUND: Early warning signals (EWS) based on dynamical systems theory, such as increased autocorrelation (AR) and variance, may indicate impending mood episodes in bipolar disorder (BD). This study examines whether smartphone-based digital phenot...

AI-driven skin cancer detection from smartphone images: A hybrid model using ViT, adaptive thresholding, black-hat transformation, and XGBoost.

PloS one
Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency...