A Pilot Dashboard System to Track Cumulative Exposure to Sound Levels during Music Instruction: A Technical Report.

Background:  Music-induced hearing loss (MIHL) is a critical public health issue. During music instruction, students and teachers are at risk of developing hearing loss due to exposure to loud and unsafe sound levels that can exceed 100 dBA. Prevention of MIHL in music students must be a desired action of all music educators.

Machine Learning Models for Parkinson Disease: Systematic Review.

Background: With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly used in disease detection and prediction, including for Parkinson disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world use. In particular, a lack of external validity may result in poor performance of these systems in clinical practice.

Creating an autoencoder single summary metric to assess gait quality to compare surgical outcomes in children with cerebral palsy: The Shriners Gait Index (SGI).

Gait for individuals with movement disorders varies widely and the variability makes it difficult to assess outcomes of surgical and therapeutic interventions. Although specific joints can be assessed by fewer individual measures, gait depends on multiple parameters making an overall assessment metric difficult to determine. A holistic, summary measure can permit a standard comparison of progress throughout treatments and interventions, and permit more straightforward comparison across varied subjects.

AI-organoid integrated systems for biomedical studies and applications.

In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone.

An LSTM-based Gesture-to-Speech Recognition System.

Fast and flexible communication options are limited for speech-impaired people. Hand gestures coupled with fast, generated speech can enable a more natural social dynamic for those individuals - particularly individuals without the fine motor skills to type on a keyboard or tablet reliably. We created a mobile phone application prototype that generates audible responses associated with trained hand movements and collects and organizes the accelerometer data for rapid training to allow tailored models for individuals who may not be able to perform standard movements such as sign language.

Limited Incision Rhinoplasty: An Approach for All Primary and Revision Cases.

Introduction: Rhinoplasty was one of the most frequently performed aesthetic surgeries in the USA in 2022. Traditionally, the open approach has been preferred by the majority of surgeons often due to familiarity and ease of visualization. However, patient interest in closed and preservation rhinoplasty is driving a resurgence in the popularity of endonasal techniques.

Feasibility of PROMIS using computerized adaptive testing during inpatient rehabilitation.

Background: There has been an increased significance on patient-reported outcomes in clinical settings. We aimed to evaluate the feasibility of administering patient-reported outcome measures by computerized adaptive testing (CAT) using a tablet computer with rehabilitation inpatients, assess workload demands on staff, and estimate the extent to which rehabilitation inpatients have elevated T-scores on six Patient Reported Outcomes Measurement Information System® (PROMIS®) measures.

Methods: Patients (N = 108) with stroke, spinal cord injury, traumatic brain injury, and other neurological disorders participated in this study.

A Low-Power Wireless System for Predicting Early Signs of Sudden Cardiac Arrest Incorporating an Optimized CNN Model Implemented on NVIDIA Jetson.

The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA's characteristics will be key to developing preventive strategies. Many lives could be saved if SCA's early onset could be detected or predicted.

Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation.

We created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. Eight clinical outcomes were distilled into a single metric using a seven-layer deep autoencoder, with the developed metric compared to similar results from principal component analysis (PCA). The proposed methods were used on data collected from ten participants with a dysvascular transfemoral amputation recruited for a prosthetics research study.

Wearable airbag technology and machine learned models to mitigate falls after stroke.

Background: Falls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed nor validated for the stroke population and thus, may inadequately detect falls in individuals with stroke-related motor impairments.

Predicting UPDRS Motor Symptoms in Individuals With Parkinson's Disease From Force Plates Using Machine Learning.

Parkinson's disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD include a section where a trained specialist scores qualitative symptoms using the motor subscale of the Unified Parkinson's Disease Rating Scale (UPDRS-III). The aim of this feasibility study was twofold.

The Principles and Practice of Primary Endonasal Rhinoplasty.

The purpose of this article is to provide a guide for plastic surgeons, regardless of experience level, seeking to improve his/her endonasal rhinoplasty skills and comfort level. We have presented the advantages of our technique and its unifying principles and demonstrated how endonasal rhinoplasty can be used to achieve safe, anatomical, and predictable outcomes. Endonasal rhinoplasty is a separate thought process from open rhinoplasty and should not be viewed as a competing but rather parallel technique that is broadly applicable to many nasal deformities.

On the Role of LGN/V1 Spontaneous Activity as an Innate Learning Pattern for Visual Development.

Correlated, spontaneous neural activity is known to play a necessary role in visual development, but the higher-order statistical structure of these coherent, amorphous patterns has only begun to emerge in the past decade. Several computational studies have demonstrated how this endogenous activity can be used to train a developing visual system. Models that generate spontaneous activity analogous to retinal waves have shown that these waves can serve as stimuli for efficient coding models of V1.

A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls.

Background: Falls are a leading cause of accidental deaths and injuries worldwide. The risk of falling is especially high for individuals suffering from balance impairments. Retrospective surveys and studies of simulated falling in lab conditions are frequently used and are informative, but prospective information about real-life falls remains sparse.

Using a microprocessor knee (C-Leg) with appropriate foot transitioned individuals with dysvascular transfemoral amputations to higher performance levels: a longitudinal randomized clinical trial.

Background: Individuals with transfemoral amputations who are considered to be limited community ambulators are classified as Medicare functional classification (MFCL) level K2. These individuals are usually prescribed a non-microprocessor controlled knee (NMPK) with an appropriate foot for simple walking functions. However, existing research suggests that these individuals can benefit from using a microprocessor controlled knee (MPK) and appropriate foot for their ambulation, but cannot obtain one due to insurance policy restrictions.

Variable abnormality of the melanopsin-derived portion of the pupillary light reflex (PLR) in patients with Parkinson's disease (PD) and parkinsonism features.

Objectives: Ascertain and quantify abnormality of the melanopsin-derived portion of the pupillary light reflex (PLR) in patients with Parkinson's disease (PD) and parkinsonism features based on a statistical predictive modeling strategy for PLR classification.

Methods: Exploratory cohort analysis of pupillary kinetics in non-disease controls, PD subjects, and subjects with parkinsonism features using chromatic pupillometry. Receiver operating characteristic (ROC) curve interpretation of pupillary changes consistent with abnormality of intrinsically photosensitive retinal ganglion cells (ipRGCs) was employed using a thresholding algorithm to discriminate pupillary abnormality between study groups.

Detection of Parkinson's Disease Through Automated Pupil Tracking of the Post-illumination Pupillary Response.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders, but it is often diagnosed after the majority of dopaminergic cells are already damaged. It is critical to develop biomarkers to identify the disease as early as possible for early intervention. PD patients appear to have an altered pupillary response consistent with an abnormality in photoreceptive retinal ganglion cells.

Impact of FDA Updates on Public Interest in Breast Implant-associated Anaplastic Large Cell Lymphoma.

In the United States, the Food and Drug Administration (FDA) is responsible for protecting the public health by assuring the safety, efficacy, and security of drugs, biological products, and medical devices. In that role, FDA releases timely updates with regard to medical devices and their possible adverse effects. However, the impact of such FDA updates on public interest has not been studied.

Accelerometer-Based Automated Counting of Ten Exercises without Exercise-Specific Training or Tuning.

Measuring physical activity using wearable sensors is essential for quantifying adherence to exercise regiments in clinical research and motivating individuals to continue exercising. An important aspect of wearable activity tracking is counting particular movements. One limitation of many previous models is the need to design the counting for a specific exercise.

Hidden Markov model-based activity recognition for toddlers.

Objective: Physical activity has been shown to impact future health outcomes in adults, but little is known about the long-term impact of physical activity in toddlers. Accurately measuring the specific types and amounts of physical activity in toddlers will help us to understand, predict, and better affect their future health outcomes. Although activity recognition has been extensively developed for adults as well as older children, toddlers move in ways that are significantly different from older children, indicating the need for a more tailored approach.

Hip and Wrist-Worn Accelerometer Data Analysis for Toddler Activities.

Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2 year(s), accelerometry data recorded during such behaviors have been far less examined. In particular, toddler's unique behaviors, such as riding in a stroller or being carried by an adult, have not yet been examined. The objective of this study was to describe accelerometry signal outputs recorded during participation in nine types of behaviors (i.

Addendum of: Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications.

[This corrects the article DOI: 10.2196/mhealth.8201.

Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications.

Background: Automatically detecting falls with mobile phones provides an opportunity for rapid response to injuries and better knowledge of what precipitated the fall and its consequences. This is beneficial for populations that are prone to falling, such as people with lower limb amputations. Prior studies have focused on fall detection in able-bodied individuals using data from a laboratory setting.