PedBotLab: A Novel Video Game-Based Robotic Ankle Platform Created for Therapeutic Exercise for Children With Neurological Impairments.

AIMS: Assess the potential benefits of using PedBotLab, a clinic based robotic ankle platform with integrated video game software, to improve ankle active and passive range of motion, strength, selective motor control, gait efficiency, and balance. METHODS: Ten participants with static neurological injuries and independent ambulation participated in a 10-week pilot study (Pro00013680) to assess feasibility and efficacy of PedBotLab as a therapeutic device twice weekly. Isometric ankle strength, passive and active ankle range of motion, plantarflexor spasticity, selective motor control of the lower extremity, balance, and gait speed were measured pre- and post-trial. RESULTS: Statistically significant improvements were seen in flexibility, active range of motion, and strength in multiple planes of ankle motion. Ankle dorsiflexion with knee flexion and knee extension demonstrated statistically significant results in all outcome measures. No significant changes were observed in gait speed outcomes. CONCLUSIONS: The use of PedbotLab can lead to improvements in ankle strength, flexibility, and active range of motion for children with static neurological injuries. Future studies aim to evaluate the effect on gait quality and work toward developing a home-based device.

StethAid: A Digital Auscultation Platform for Pediatrics.

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.

EasyTBSA as a method for calculating total body surface area burned: a validation study.

BACKGROUND: Current methods of burn estimation can lead to incorrect estimates of the total body surface area (TBSA) burned, especially among injured children. Inaccurate estimation of burn size can impact initial management, including unnecessary transfer to burn centres and fluid overload during resuscitation. To address these challenges, we developed a smartphone application (EasyTBSA) that calculates the TBSA of a burn using a body-part by body-part approach. The aims of this study were to assess the accuracy of the EasyTBSA application and compare its performance to three established methods of burn size estimation (Lund-Browder Chart, Rule of Nines and Rule of Palms). METHODS: Twenty-four healthcare providers used each method to estimate burn sizes on moulaged manikins. The manikins represented different ages (infant, child and adult) with different TBSA burns (small <20%, medium 20%-49% and large >49%). We calculated the accuracy of each method as the difference between the user-estimated and actual TBSA. The true value of the complete body surface area of the manikins was obtained by three-dimensional scans. We used multivariable modelling to control for manikin size and method. RESULTS: Among all age groups and burn sizes, the EasyTBSA application had the greatest accuracy for burn size estimation (-0.01%, SD 3.59%) followed by the Rule of Palms (3.92%, SD 10.71%), the Lund-Browder Chart (4.42%, SD 5.52%) and the Rule of Nines (5.05%, SD 6.87%). CONCLUSIONS: The EasyTBSA application may improve the estimation of TBSA compared with existing methods.

Frequency-Based Maternal Electrocardiogram Attenuation for Fetal Electrocardiogram Analysis.

Fetal electrocardiogram (ECG) waveform analysis along with cardiac time intervals (CTIs) measurements are critical for the management of high-risk pregnancies. Currently, there is no system that can consistently and accurately measure fetal ECG. In this work, we present a new automatic approach to attenuate the maternal ECG in the frequency domain and enhance it with measurable CTIs. First, the coherent components between the maternal ECG and abdominal ECG were identified and subtracted from the latter in the frequency domain. The residual was then converted into the time domain using the inverse Fourier transform to yield the fetal ECG. This process was improved by averaging multiple beats. Two fetal cardiologists, blinded to the method, assessed the quality of fetal ECG based on a grading system and measured the CTIs. We evaluated the fetal ECG quality of our method and time-based methods using one synthetic dataset, one human dataset available in the public domain, and 37 clinical datasets. Among the 37 datasets analyzed, the mean average (± standard deviation) grade was 3.49 ± 1.22 for our method vs. 2.64 ± 1.26 for adaptive interference cancellation (p-value < 0.001), thus showing the frequency-based fetal ECG extraction was the superior method, as assessed from our clinicians' perspectives. This method has the potential for use in clinical settings.

PedBotHome: A Video Game-Based Robotic Ankle Device Created for Home Exercise in Children With Neurological Impairments.

PURPOSE: This pilot study assesses the feasibility of using PedBotHome to promote adherence to a home exercise program, the ability of the device to withstand frequent use, and changes in participant ankle mobility.PedBotHome is a robotic ankle device with integrated video game software designed to improve ankle mobility in children with cerebral palsy. METHODS: Eight participants enrolled in a 28-day trial of PedBotHome. Ankle strength, range of motion, and plantar flexor spasticity were measured pre- and posttrial. Performance was monitored remotely, and game settings were modified weekly by physical therapists. RESULTS: Four participants met the study goal of 20 days of use. There were statistically significant improvements in ankle strength, spasticity, and range of motion. CONCLUSIONS: PedBotHome is a feasible device to engage children with static neurological injuries in ankle home exercise. This pilot study expands the paradigm for future innovative home-based robotic rehabilitation.

Innovation at a Children's Hospital: Personal Protective Equipment Efforts During the Pandemic.

The COVID-19 pandemic has affected life for everyone, and hospitals, in particular have been hard hit. In this study, we describe our efforts to develop personal protective equipment at a children's hospital early in the pandemic. We convened an innovation working group to organize our efforts and respond to the rapidly changing situation. We describe our work in four areas: (1) plexiglass shields for the emergency department, (2) face shields for clinical providers, (3) breath shields for ophthalmology, and (4) flip-up safety glasses for nurses. The hospital's supply chain is now caught up with addressing many pandemic-related shortages. Nevertheless, through our multidisciplinary approach to reacting to the pandemic's urgent needs, we demonstrated agility to bring stakeholders together to maximize the use of scarce resources and build resiliency. We believe this method can be rapidly replicated as future needs arise.

Toward Evaluation of the Subjective Experience of a General Class of User-Controlled, Robot-Mediated Rehabilitation Technologies for Children with Neuromotor Disability.

Technological advances in game-mediated robotics provide an opportunity to engage children with cerebral palsy (CP) and other neuromotor disabilities in more frequent and intensive therapy by making personalized, programmed interventions available 24/7 in children's homes. Though shown to be clinically effective and feasible to produce, little is known of the subjective factors impacting acceptance of what we term assistive/rehabilitative (A/R) gamebots by their target populations. This research describes the conceptualization phase of an effort to develop a valid and reliable instrument to guide the design of A/R gamebots. We conducted in-depth interviews with 8 children with CP and their families who had trialed an exemplar A/R gamebot, PedBotHome, for 28 days in their homes. The goal was to understand how existing theories and instruments were either appropriate or inappropriate for measuring the subjective experience of A/R gamebots. Key findings were the importance of differentiating the use case of therapy from that of assistance in rehabilitative technology assessment, the need to incorporate the differing perspectives of children with CP and those of their parents into A/R gamebot evaluation, and the potential conflict between the goals of preserving the quality of the experience of game play for the child while also optimizing the intensity and duration of therapy provided during play.

Pedbothome: Robotically-Assisted Ankle Rehabilitation System For Children With Cerebral Palsy.

Our research team has developed two versions of an ankle robot for children with cerebral palsy. Both devices provide three degrees of freedom and are connected to an airplane video game. The child uses his/her foot as the controller for the plane and attempts to fly through a series of hoops arranged to manipulate the foot across the ankle joint. The first device is for lab-based therapy and four children have completed 20 sessions each with the device. The second device is for home-based therapy and two children have completed a 28-day trial using the device at home. Both studies were done under Institutional Review Board approval and all participants improved ankle range of motion. Further studies are ongoing to gather more data and validate the results.

Ultrasound-Induced Insulin Release as a Potential Novel Treatmentfor Type 2 Diabetes Mellitus.

Therapeutic ultrasound presents a potential novel treatment for type 2 diabetes mellitus that utilizes the non-invasive application of ultrasound energy to treat secretory defects in the earlier stages of the disease. Our previous studies have shown that ultrasound is capable of stimulating insulin release from pancreatic beta cells, safely and effectively. This study aims to both examine the calcium-dependent mechanisms of ultrasound-mediated insulin release from pancreatic beta cells using three complementary modalities - carbon fiber amperometry, ELISA studies, and Ca2+ fluorescence imaging - and to study the translational potential of therapeutic ultrasound using transgenic hyperglycemic mice for safety and efficacy studies.