Results 271 to 280 of about 3,448,535 (370)

MoodMon: novel optimization of bipolar disorder monitoring through patient-driven voice parameter submission and AI technology. [PDF]

Postep Psychiatr Neurol
Sokół-Szawłowska M, Kamińska O, Sochacka M.   +2 more
europepmc   +1 more source

Pitfalls and Strategies for Measuring Menopausal Stage in Eating Disorder Studies of Women in Midlife

International Journal of Eating Disorders, EarlyView.
ABSTRACT Objective Researchers are increasingly interested in exploring the role of the menopausal transition in eating disorder risk. We recently embarked on studies of this critical life phase and encountered significant challenges in assessing menopausal status that limited our ability to test study hypotheses.
Kelly L. Klump, Katharine N. Thakkar, Kristen M. Culbert   +2 more
wiley   +1 more source

Exploring Drivers of Binge Eating in Individuals With Food Insecurity and Recurrent Binge Eating: A Qualitative Analysis

International Journal of Eating Disorders, EarlyView.
ABSTRACT Objective This study explored the drivers of binge eating in people with food insecurity and recurrent binge eating. Method Participants were 28 adults, ages 26–69 (M = 46.22, SD = 11.94; 64.3% female), who endorsed current food insecurity and recurrent binge eating (≥ 12 episodes in the past 3 months).
Emilie A. Green, Kristin L. Schneider, Angela Chang, Brian A. Feinstein, Isabel R. Rooper, Jennifer E. Wildes, Andrea K. Graham   +6 more
wiley   +1 more source

Longitudinal Effects of Residential Treatment for Eating Disorders: Symptom Trajectories and Predictors of Functional Outcomes

International Journal of Eating Disorders, EarlyView.
ABSTRACT Objective Residential treatment for eating disorders addresses the gap between inpatient and outpatient care, but evidence for longer‐term and functional outcomes remains limited. The current study examined both clinical and functional outcomes from admission to a 6‐month follow‐up from Australia's first residential service for eating ...
Sinead Day, Deborah Mitchison, W. Kathy Tannous, Janet Conti, Katherine Gill, Long Le, Haider Mannan, Cathrine Mihalopoulos, Lucie Ramjan, Rebekah Rankin, Phillipa Hay   +10 more
wiley   +1 more source

A review of machine learning and deep learning for Parkinson's disease detection. [PDF]

Discov Artif Intell
Rabie H, Akhloufi MA.
europepmc   +1 more source

Global utilization of artificial intelligence in the diagnosis and management of voice disorders over the past five years

Eye &ENT Research, EarlyView.
Abstract Objective This review evaluates the worldwide use of artificial intelligence (AI) for the diagnosis and treatment of voice disorders. Methods An electronic search was completed in Embase, Pubmed, Ovid MEDLINE, Scopus, Google Scholar, and Web of Science.
Amna Suleman, Amy L. Rutt
wiley   +1 more source

Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples. [PDF]

Sci Rep
Rahmatallah Y, Kemp AS, Iyer A, Pillai L, Larson-Prior LJ, Virmani T, Prior F.   +6 more
europepmc   +1 more source

Enhancing Dysarthric Voice Conversion with Fuzzy Expectation Maximization in Diffusion Models for Phoneme Prediction. [PDF]

Diagnostics (Basel)
Hsu WS, Lin GT, Wang WH.
europepmc   +1 more source

Cricothyroid Approximation Surgery (Type IV Thyroplasty): Insights From Failure Cases and an Analysis of Contributing Factors Using Three-Dimensional Computed Tomography. [PDF]

Cureus
Motohashi R, Hiramatsu H, Tokashiki R, Sakurai E, Tsukahara K.   +4 more
europepmc   +1 more source

Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression. [PDF]

Ann Fam Med
Mazur A, Costantino H, Tom P, Wilson MP, Thompson RG.   +4 more
europepmc   +1 more source