Decoding phonation with artificial intelligence (DeP AI): Proof of concept.

Objective: Acoustic analysis of voice has the potential to expedite detection and diagnosis of voice disorders. Applying an image-based, neural-network approach to analyzing the acoustic signal may be an effective means for detecting and differentially diagnosing voice disorders. The purpose of this study is to provide a proof-of-concept that embedded data within human phonation can be accurately and efficiently decoded with deep learning neural network analysis to differentiate between normal and disordered voices.

Exploring the "weight" of food cravings and thought suppression among Cuban adults.

Purpose: The current study aimed to analyze individual differences on food cravings, intrusive-related thoughts and its suppression between normal weight and overweight/obese Cuban adults.

Methods: Participants were 1,184 individuals from general population, aged between 18 and 64 years (M = 32.89; SD = 12.