Gray-tone appearances on 4-hour delayed gadolinium-enhanced magnetic resonance imaging indicate severe inner ear pathology and symptoms in sudden sensorineural hearing loss.

Objective: Hybrid of reversed image of positive endolymph signal and negative image of perilymph signal (HYDROPS) in delayed gadolinium-enhanced magnetic resonance imaging (MRI) typically depicts normal inner ear as "white-tone" and endolymphatic hydrops as "black-transparent" appearances, whereas ears with auditory and vestibular disorders are occasionally depicted as "gray-tone." This study aimed to investigate the pathological basis of sudden sensorineural hearing loss (SSNHL) patients with "gray-tone" appearances on HYDROPS. Methods: Delayed gadolinium-enhanced MRI examinations were conducted on 29 subjects with unilateral SSNHL. We mainly analyzed positive perilymph image (PPI) and positive endolymph image (PEI), which were components HYDROPS. Results: On PPI, signal intensity (SI) values extracted from the cochlear and vestibular region of interest (ROI) were higher in the SSNHL ears with dizziness/vertigo symptom at the first visit compared to the healthy ear. Additionally, the PPI/PEI enhancement pattern in the vestibule was associated with a high prevalence of hearing and vestibular deteriorations at the first visit and poor hearing improvement after treatment. Conclusion: Enhancement on PPI/PEI may result from leakage of gadolinium into the inner ear following breakdown of the blood-labyrinth barrier, with high SI being correlated with the amount of leakage. Particularly, a significant leakage into the endolymphatic space, defined as PPI+/PEI+, indicates severe inner ear pathology. Ultimately, we emphasize that the "gray-tone" appearance in the inner ear on HYDROPS comprises enhancements on both PPI and PEI and propose a new classification for evaluating SSNHL Peri- and Endolymphatic image Enhancement pattern in Delayed gadolinium-enhanced MRI (SPEED). Level of Evidence: 4.

Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method.

Walking speed is considered a reliable assessment tool for any movement-related functional activities of an individual (i.e., patients and healthy controls) by caregivers and clinicians. Traditional video surveillance gait monitoring in clinics and aged care homes may employ modern artificial intelligence techniques to utilize walking speed as a screening indicator of various physical outcomes or accidents in individuals. Specifically, ratio-based body measurements of walking individuals are extracted from marker-free and two-dimensional video images to create a walk pattern suitable for walking speed classification using deep learning based artificial intelligence techniques. However, the development of successful and highly predictive deep learning architecture depends on the optimal use of extracted data because redundant data may overburden the deep learning architecture and hinder the classification performance. The aim of this study was to investigate the optimal combination of ratio-based body measurements needed for presenting potential information to define and predict a walk pattern in terms of speed with high classification accuracy using a deep learning-based walking speed classification model. To this end, the performance of different combinations of five ratio-based body measurements was evaluated through a correlation analysis and a deep learning-based walking speed classification test. The results show that a combination of three ratio-based body measurements can potentially define and predict a walk pattern in terms of speed with classification accuracies greater than 92% using a bidirectional long short-term memory deep learning method.

Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification.

Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.