Integrated Vocal Deviation Index (IVDI): A Machine Learning Model to Classifier of the General Grade of Vocal Deviation.

Objective: To develop a multiparametric index based on machine learning (ML) to predict and classify the overall degree of vocal deviation (GG).

Method: The sample consisted of 300 dysphonic and non-dysphonic participants of both sexes. Two speech tasks were sustained vowel [a] and connected speech (counting numbers from 1 to 10).

Assessment of Volumetric Dense Tissue Segmentation in Tomosynthesis Using Deep Virtual Clinical Trials.

The adoption of artificial intelligence (AI) in medical imaging requires careful evaluation of machine-learning algorithms. We propose the use of a "deep virtual clinical trial" (DeepVCT) method to effectively evaluate the performance of AI algorithms. In this paper, DeepVCTs have been proposed to elucidate limitations of AI applications and predictions of clinical outcomes, avoiding biases in study designs.

Glutathione effect on functional and histological recovery after spinal cord injury in rats.

Objective: The aim of this study was to evaluate the GSH effect on functional and histological recovery after experimental spinal cord injury in rats.

Methods: Forty Wistar rats were subjected to spinal cord injury through the Multicenter Animal Spinal Cord Injury Study (MASCIS) Impactor system. The rats were sorted and divided into four groups, as follows: Group 1 ‒ Laminectomy and spinal cord injury; Group 2 ‒ Laminectomy, spinal cord injury and Saline Solution (SS) 0.

Gait Analysis of Amputee Dogs Using a Pressure-Sensitive Walkway.

Objective:  The aim of this study was to perform gait analysis using a pressure-sensitive walkway in dogs submitted to high (total) or low amputation (partial) of one forelimb or hindlimb.

Study Design:  A total of 39 dogs met the inclusion criteria. The reasons for the amputations were motor vehicle accidents in 38 dogs and possible limb malformation in 1 dog.

Impact of Tomosynthesis Acquisition on 3D Segmentations of Breast Outline and Adipose/Dense Tissue with AI: A Simulation-Based Study.

Digital breast tomosynthesis (DBT) reconstructions introduce out-of-plane artifacts and false-tissue boundaries impacting the dense/adipose and breast outline (convex hull) segmentations. A virtual clinical trial method was proposed to segment both the breast tissues and the breast outline in DBT reconstructions. The DBT images of a representative population were simulated using three acquisition geometries: a left-right scan (conventional, I), a two-directional scan in the shape of a "T" (II), and an extra-wide range (XWR, III) left-right scan at a six-times higher dose than I.