RESUMO
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. METHODS: Acoustic recordings from 10 vocally-healthy speakers, as well as 70 patients with one of seven voice disorders (n = 10 per diagnosis), were acquired from a clinical database. Acoustic signals were converted into spectrograms and used to train a convolutional neural network developed with the Keras library. The network architecture was trained separately for each of the seven diagnostic categories. Binary classification tasks (ie, to classify normal vs. disordered) were performed for each of the seven diagnostic categories. All models were validated using the 10-fold cross-validation technique. RESULTS: Binary classification averaged accuracies ranged from 58% to 90%. Models were most accurate in their classification of adductor spasmodic dysphonia, unilateral vocal fold paralysis, vocal fold polyp, polypoid corditis, and recurrent respiratory papillomatosis. Despite a small sample size, these findings are consistent with previously published data utilizing deep neural networks for classification of voice disorders. CONCLUSION: Promising preliminary results support further study of deep neural networks for clinical detection and diagnosis of human voice disorders. Current models should be optimized with a larger sample size. LEVELS OF EVIDENCE: Level III.
RESUMO
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 = 12.87), with 69.1 % females. All participants answered a set of questionnaires and provided demographic, anthropometric and clinical data. RESULTS: Overweight/obese individuals had higher mean scores than normal weight individuals on food cravings (including its nine dimensions) and food and body weight/shape thought suppression. Large effect sizes were found for body weight/shape thoughts suppression and lack of control over eating, where overweight and obese individuals showed the highest scores. This trend was also found for food thoughts suppression, food cravings trait, cue-dependent eating, preoccupation with food and guilty feelings, with effect sizes from medium to large. Finally, medium effect sizes were observed for intention to eat and negative affect. CONCLUSION: Overweight/obese individuals experienced more food cravings and food and body weight/shape thought suppression than normal weight individuals among Cuban adults.
