Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system

Artificial Intelligence in Otology, Rhinology, and Laryngology: A Narrative Review of Its Current and Evolving Picture

With technological advancements, artificial intelligence (AI) has progressed to become a ubiquitous part of human life. Its aspects in otorhinolaryngology are varied and are continuously evolving. Currently, AI has applications in hearing aids, imaging technologies, interpretation of auditory brain stem systems, and many more in otology. In rhinology, AI is seen to impact navigation, robotic surgeries, and the determination of various anomalies. Detection of voice pathologies and imaging are some areas of laryngology where AI is being used. This review gives an outlook on the diverse elements, applications, and advancements of AI in otorhinolaryngology. The various subfields of AI including machine learning, neural networks, and deep learning are also discussed. Clinical integration of AI and otorhinolaryngology has immense potential to revolutionize the healthcare system and improve the standards of patient care. The current applications of AI and its future scopes in developing this field are highlighted in this review.

Available rehabilitation technology with the potential to be incorporated into the clinical practice of physiotherapists: A systematic review

Background

The development of prototypes capable of intervening in the area of rehabilitation in physical therapy clinical practice activities that were previously carried out in a traditional way, that is, manually, demonstrates how technology is having an impact on professional careers such as physiotherapy.

Objective

The purpose of this study is to present a comprehensive examination of various technologies employed in the facilitation of patient rehabilitation, with a focus on their potential integration within the clinical practice of physical therapists.

Methods

We conducted a systematic search in four electronic databases (CINAHL, Embase, PEDro, and PubMed) for research on rehabilitation technologies. The eligible studies should demonstrate a clear utilization of technology in various aspects of the clinical approach to the rehabilitation process and have been published between 2000 and 2021 in either Portuguese or English.

Results

A total of 18 articles that satisfied the selection criteria were included in the study. The studies were classified into four distinct categories of rehabilitation technologies, which were determined by the specific characteristics of the technology employed and its integration with the therapeutic approach to rehabilitation. These categories include digital technologies, artificial intelligence and/or robotics, virtual technologies, and hybrid technologies.

Implications on Physiotherapy Practice

Rehabilitation technologies possess the capacity to effectively facilitate clinical activities performed by physical therapy professionals, including injury prevention, movement monitoring, and coordination of rehabilitation programs, with minimal or negligible intervention from the physical therapist. Further research is required to ascertain the precise capabilities of various technologies in collaborating with physiotherapists to deliver comprehensive care for patients' physical well-being, encompassing both therapeutic and preventive approaches.

Trial Registration

PROSPERO registration number CRD42020222288.

The Role of AI in Serious Games and Gamification for Health: Scoping Review

BACKGROUND

Artificial intelligence (AI) and game-based methods such as serious games or gamification are both emerging technologies and methodologies in health care. The merging of the two could provide greater advantages, particularly in the field of therapeutic interventions in medicine.

OBJECTIVE

This scoping review sought to generate an overview of the currently existing literature on the connection of AI and game-based approaches in health care. The primary objectives were to cluster studies by disease and health topic addressed, level of care, and AI or games technology.

METHODS

For this scoping review, the databases PubMed, Scopus, IEEE Xplore, Cochrane Library, and PubPsych were comprehensively searched on February 2, 2022. Two independent authors conducted the screening process using Rayyan software (Rayyan Systems Inc). Only original studies published in English since 1992 were eligible for inclusion. The studies had to involve aspects of therapy or education in medicine and the use of AI in combination with game-based approaches. Each publication was coded for basic characteristics, including the population, intervention, comparison, and outcomes (PICO) criteria; the level of evidence; the disease and health issue; the level of care; the game variant; the AI technology; and the function type. Inductive coding was used to identify the patterns, themes, and categories in the data. Individual codings were analyzed and summarized narratively.

RESULTS

A total of 16 papers met all inclusion criteria. Most of the studies (10/16, 63%) were conducted in disease rehabilitation, tackling motion impairment (eg, after stroke or trauma). Another cluster of studies (3/16, 19%) was found in the detection and rehabilitation of cognitive impairment. Machine learning was the main AI technology applied and serious games the main game-based approach used. However, direct interaction between the technologies occurred only in 3 (19%) of the 16 studies. The included studies all show very limited quality evidence. From the patients' and healthy individuals' perspective, generally high usability, motivation, and satisfaction were found.

CONCLUSIONS

The review shows limited quality of evidence for the combination of AI and games in health care. Most of the included studies were nonrandomized pilot studies with few participants (14/16, 88%). This leads to a high risk for a range of biases and limits overall conclusions. However, the first results present a broad scope of possible applications, especially in motion and cognitive impairment, as well as positive perceptions by patients. In future, the development of adaptive game designs with direct interaction between AI and games seems promising and should be a topic for future reviews.

Artificial intelligence in physical rehabilitation: A systematic review

BACKGROUND

Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear.

METHODS

We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey.

RESULTS

We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost.

CONCLUSION

Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.

Effectiveness and acceptance of Vestibulo-Ocular Reflex adaptation training in children with recurrent vertigo with unilateral vestibular dysfunction and normal balance function

Objective

This was a block randomized controlled study to evaluate the effectiveness and acceptance of Vestibulo-Ocular Reflex (VOR) adaptation training in children with recurrent vertigo with unilateral vestibular dysfunction (UVD) and normal balance function.

Methods

Thirty children, aged 4-13 years, diagnosed with recurrent vertigo of childhood (RVC) with UVD (according to a caloric test) and normal balance function were analyzed. These 30 children were divided into 10 blocks based on similar age and severity of vertigo. Three children in each block were randomly assigned to one of three groups to receive 1 month of treatment. Group A received vestibular-ocular reflex (VOR) adaptation training, Group B received Cawthorne-Cooksey training, and a control group received no training. All children were administered pharmacotherapy [Ginkgo biloba leaf extract (drops)]. The Dizziness Handicap Inventory (DHI), Visual Analog Scale of Quality of Life with Vertigo (VAS-QLV), and canal paralysis (CP) on the caloric test were recorded before and after treatment, and the effectiveness of treatment was evaluated. The Visual Analog Scale of Acceptance (VAS-A) was used to evaluate the acceptance of the training in the two groups that received training.

Results

There were 10 children each in Group A, Group B, and the control group; the male to female ratio was 1, and the average age in each group was 9.0 ± 3.2, 8.4 ± 3.0, and 8.3 ± 2.6 years, respectively. The effective rate was 100% in Group A, 65% in Group B, and 60% in Group C. The recovery rate on caloric testing after treatment was 100, 70, and 50%, respectively. DHI scores before and after training were 56.8 ± 12.4 and 8.8 ± 6.1 in Group A, 57.8 ± 12.6 and 18.8 ± 9.7 in Group B, and 56.8 ± 12.4 and 24.0 ± 15.3 in Group C (all P = 0.000). VAS-QLV scores before and after training were 7.5 ± 1.0 and 0.9 ± 0.9 in Group A, 6.4 ± 2.2 and 2.7 ± 1.1 in Group B, and 6.6 ± 1.6 and 2.6 ± 1.4 in Group C (all P < 0.05). The CP values before and after training were 35.7 ± 15.1 and 12.9 ± 8.7 in Group A, 33.6 ± 20.1 and 23.6 ± 19.3 in Group B, and 38.6 ± 21.1 and 24.8 ± 17.9 in Group C (P = 0.001, P = 0.015, and P = 0.050, respectively). Between-group comparisons showed that the decreases in DHI and VAS-QLV scores after training were significantly different (P = 0.015, P = 0.02), while CP values were not (P = 0.139). After training, the DHI value had decreased significantly more in Group A compared with Group C (P < 0.05), but there were no other differences. After training, VAS-QLV scores in Group A had decreased significantly more compared with Group B and C (P < 0.05). In terms of acceptance, the VAS-A score was 7.6 ± 2.2 in Group A and 3.1 ± 2.8 in Group B (P =0.004), The acceptance rate was 70% in group A and 10% in group B. there was no significant correlation between age and VAS-A in either group A or group B (P > 0.05).

Conclusion

This study strongly suggests that vestibular rehabilitation training should be performed in children with vertigo to improve symptoms. For children with RVC with UVD but normal balance function, a single VOR adaptation program can effectively improve vertigo symptoms, and given its simplicity, time-effectiveness, and excellent outcomes, it is associated with better acceptance in children compared to classic Cawthorne-Cooksey training.

Systematic Review and Meta-Analysis of the Application of Virtual Reality in Hearing Disorders

Background and Objectives

Trendy technologies, such as artificial intelligence, virtual reality (VR), and augmented reality (AR) are being increasingly used for hearing loss, tinnitus, and vestibular disease. Thus, we conducted this systematic review and meta-analysis to identify the possible benefits of the use of VR and AR technologies in patients with hearing loss, tinnitus, and/or vestibular dysfunction, with the aim of suggesting potential applications of these technologies for both researchers and clinicians.

Materials and Methods

Published articles from 1968 to 2022 were gathered from six electronic journal databases. Applying our specified inclusion and/or exclusion criteria, 23 studies were analyzed. As only one article on hearing loss and two articles on tinnitus were found, 20 studies on vestibular dysfunction were only finally included for the meta-analysis. Standardized mean differences (SMDs) were chosen as estimates to compare the studies. A funnel plot and Egger’s regression analysis were used to identify any risk of bias.

Results

High heterogeneity (I²: 83%, τ²: 0.5431, p<0.01) was identified across the studies on vestibular dysfunction. VR-based rehabilitation was significantly effective for individuals with vestibular disease (SMDs: 0.03, 95% confidence interval [CI]: -0.08 to 0.15, p<0.05). A subgroup analysis revealed that only improvement in the subjective questionnaire was meaningful and statistically significant (SMDs: -0.66, 95% CI: -1.10 to -0.22).

Conclusions

VR-based vestibular rehabilitation showed potential for subjective rating measures like Dizziness Handicap Index. The negative effect of aging on vestibular disease was indirectly confirmed. More clinical trials and an evidence-based approach are needed to confirm the implementation of state-of-the-art technology for hearing loss and tinnitus, representative diseases in neurotology.

Automated assessment of balance: A neural network approach based on large-scale balance function data

Balance impairment (BI) is an important cause of falls in the elderly. However, the existing balance estimation system needs to measure a large number of items to obtain the balance score and balance level, which is less efficient and redundant. In this context, we aim at building a model to automatically predict the balance ability, so that the early screening of large-scale physical examination data can be carried out quickly and accurately. We collected and sorted out 17,541 samples, each with 61-dimensional features and two labels. Moreover, using this data a lightweight artificial neural network model was trained to accurately predict the balance score and balance level. On the premise of ensuring high prediction accuracy, we reduced the input feature dimension of the model from 61 to 13 dimensions through the recursive feature elimination (RFE) algorithm, which makes the evaluation process more streamlined with fewer measurement items. The proposed balance prediction method was evaluated on the test set, in which the determination coefficient (R2) of balance score reaches 92.2%. In the classification task of balance level, the metrics of accuracy, area under the curve (AUC), and F1 score reached 90.5, 97.0, and 90.6%, respectively. Compared with other competitive machine learning models, our method performed best in predicting balance capabilities, which is especially suitable for large-scale physical examination.

Older Adults Get Lost in Virtual Reality: Visuospatial Disorder Detection in Dementia Using a Voting Approach Based on Machine Learning Algorithms

As the age of an individual progresses, they are prone to more diseases; dementia is one of these age-related diseases. Regarding the detection of dementia, traditional cognitive testing is currently one of the most accurate tests. Nevertheless, it has many disadvantages, e.g., it does not measure the extent of the brain damage and does not take the patient’s intelligence into consideration. In addition, traditional assessment does not measure dementia under real-world conditions and in daily tasks. It is therefore advisable to investigate the newest, more powerful applications that combine cognitive techniques with computerized techniques. Virtual reality worlds are one example, and allow patients to immerse themselves in a controlled environment. This study created the Medical Visuospatial Dementia Test (referred to as the “MVD Test”) as a non-invasive, semi-immersive, and cognitive computerized test. It uses a 3D virtual environment platform based on medical tasks combined with AI algorithms. The objective is to evaluate two cognitive domains: visuospatial assessment and memory assessment. Using multiple machine learning algorithms (MLAs), based on different voting approaches, a 3D system classifies patients into three classes: patients with normal cognition, patients with mild cognitive impairment (MCI), and patients with severe cognitive impairment (dementia). The model with the highest performance was derived from voting approach named Ensemble Vote, where accuracy was 97.22%. Cross-validation accuracy of Extra Tree and Random Forest classifiers, which was greater than 99%, indicated a greater discriminate capacity than that of other classes.

Artificial Intelligence-Driven Serious Games in Healthcare: A Scoping Review (Preprint).. [DOI: 10.2196/preprints.39840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Artificial Intelligence (AI)-driven serious games have been used in healthcare to offer a customizable and immersive experience. Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state in order to plan on how to leverage them in the current and future healthcare needs.

OBJECTIVE

The current study aimed to explore the features of AI-driven serious games in healthcare as reported by previous research.

METHODS

We carried out a scoping review to achieve the above-mentioned objective. The most popular databases in information technology and health fields (e.g., MEDLINE and IEEE Xplore) were searched using keywords related to serious games and AI. These terms were selected based on the target intervention (i.e., AI) and the target disease (i.e., COVID-19). Two reviewers independently performed the study selection process. Three reviewers independently used Microsoft Excel to extract data from the included studies. A narrative approach was used for data synthesis.

RESULTS

The search process returned 1470 records. Of these records, 46 met all eligibility criteria. 60 different serious games were found in the included studies. Motor impairment was the most common health condition targeted by these serious games. Serious games in most of the studies were used for rehabilitation. The serious games in the majority of the included studies can be played by only single player. Most serious games were played on standalone devices (offline games). The most common genres of serious games were role-playing games, puzzle games, and platformer games. Unity was the most prominent game engine used to develop serious games. Personal computers (PCs) were the most common platforms used to play serious games. The most common algorithms used in the included studies were Support Vector Machine (SVM), Convolutional Neural Network (CNN), Artificial Neural Networks (ANN), and Random Forest (RF). The most common purposes of AI were the detection of disease and the evaluation of user's performance. The dataset size ranged from 36 to 795,600, with an average of about 52,124. The most common validation techniques used in the included studies were K-fold cross-validation and training test split validation. Accuracy was the most commonly used metric to evaluate the performance of AI models.

CONCLUSIONS

The last decade witnessed an increase in the development of AI-driven serious games for healthcare purposes and targeting various health conditions and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. While the evidence uncovered in this study shows promising applications of AI-driven serious games, larger and more rigorous, diverse, and robust studies may be needed to examine the efficacy and effectiveness of AI-driven serious games in different populations with different health conditions.

The potential use of virtual reality in vestibular rehabilitation of motion sickness

OBJECTIVE

Motion sickness (MS) can be triggered by direct or indirect stimuli due to mismatches in the visual-vestibular autonomic pathways. Various studies in the literature have shown that virtual reality technologies can generate provocative stimuli for MS. Therefore, our primary aim is to investigate the usability of virtual reality systems in MS rehabilitation METHODS: 20 normal and 19 MS patients were included. A total of six virtual reality rehabilitation sessions (VRrs) with a game called "Roller Coaster Dreams" playable via PlayStation VR Head Mounted Display were applied thrice a week for 2 weeks, or twice a week for 3 weeks. Participants were evaluated at the pre-rehabilitation phase twice and after third and sixth rehabilitation sessions with the sensory organization test (SOT). The effectiveness of the rehabilitation program was statistically analyzed by comparing the results of SOTs.

RESULTS

All SOT results of the patient group were compared each other to evaluate the effectiveness of rehabilitation. According to the post-hoc comparisons, a statistically significant difference was found between the 1st, 2nd, 3rd, and 4th SOT- Equilibrium Scores; Condition 2 (p = 0.043), Condition 3 (p = 0.006), Condition 4 (p = 0.031), Condition 5 (p = 0.002) and Condition 6 (p = 0.040). There is no difference obtained in Condition 1 (p > 0.05). The Equilibrium Scores of SOT 3rd and SOT 4th were similar and 4th SOT-Equilibrium Scores were the highest among all SOT measurements. The results show that while the first three sessions were accepted as an orientation and adaptation sessions, 4^th, 5^thand 6^th sessions are habilitation sessions.

CONCLUSIONS

VR proved to be significantly effective and useful for MS rehabilitation. Additionally, observations indicated that using VR makes rehabilitation fun, increases the efficiency of the process, and reduces the risk of inadaptability to exercise.

Vestibular Rehabilitation for Peripheral Vestibular Hypofunction: An Updated Clinical Practice Guideline From the Academy of Neurologic Physical Therapy of the American Physical Therapy Association

Background:

Uncompensated vestibular hypofunction can result in symptoms of dizziness, imbalance, and/or oscillopsia, gaze and gait instability, and impaired navigation and spatial orientation; thus, may negatively impact an individual's quality of life, ability to perform activities of daily living, drive, and work. It is estimated that one-third of adults in the United States have vestibular dysfunction and the incidence increases with age. There is strong evidence supporting vestibular physical therapy for reducing symptoms, improving gaze and postural stability, and improving function in individuals with vestibular hypofunction. The purpose of this revised clinical practice guideline is to improve quality of care and outcomes for individuals with acute, subacute, and chronic unilateral and bilateral vestibular hypofunction by providing evidence-based recommendations regarding appropriate exercises.

Methods:

These guidelines are a revision of the 2016 guidelines and involved a systematic review of the literature published since 2015 through June 2020 across 6 databases. Article types included meta-analyses, systematic reviews, randomized controlled trials, cohort studies, case-control series, and case series for human subjects, published in English. Sixty-seven articles were identified as relevant to this clinical practice guideline and critically appraised for level of evidence.

Results:

Based on strong evidence, clinicians should offer vestibular rehabilitation to adults with unilateral and bilateral vestibular hypofunction who present with impairments, activity limitations, and participation restrictions related to the vestibular deficit. Based on strong evidence and a preponderance of harm over benefit, clinicians should not include voluntary saccadic or smooth-pursuit eye movements in isolation (ie, without head movement) to promote gaze stability. Based on moderate to strong evidence, clinicians may offer specific exercise techniques to target identified activity limitations and participation restrictions, including virtual reality or augmented sensory feedback. Based on strong evidence and in consideration of patient preference, clinicians should offer supervised vestibular rehabilitation. Based on moderate to weak evidence, clinicians may prescribe weekly clinic visits plus a home exercise program of gaze stabilization exercises consisting of a minimum of: (1) 3 times per day for a total of at least 12 minutes daily for individuals with acute/subacute unilateral vestibular hypofunction; (2) 3 to 5 times per day for a total of at least 20 minutes daily for 4 to 6 weeks for individuals with chronic unilateral vestibular hypofunction; (3) 3 to 5 times per day for a total of 20 to 40 minutes daily for approximately 5 to 7 weeks for individuals with bilateral vestibular hypofunction. Based on moderate evidence, clinicians may prescribe static and dynamic balance exercises for a minimum of 20 minutes daily for at least 4 to 6 weeks for individuals with chronic unilateral vestibular hypofunction and, based on expert opinion, for a minimum of 6 to 9 weeks for individuals with bilateral vestibular hypofunction. Based on moderate evidence, clinicians may use achievement of primary goals, resolution of symptoms, normalized balance and vestibular function, or plateau in progress as reasons for stopping therapy. Based on moderate to strong evidence, clinicians may evaluate factors, including time from onset of symptoms, comorbidities, cognitive function, and use of medication that could modify rehabilitation outcomes.

Discussion:

Recent evidence supports the original recommendations from the 2016 guidelines. There is strong evidence that vestibular physical therapy provides a clear and substantial benefit to individuals with unilateral and bilateral vestibular hypofunction.

Limitations:

The focus of the guideline was on peripheral vestibular hypofunction; thus, the recommendations of the guideline may not apply to individuals with central vestibular disorders. One criterion for study inclusion was that vestibular hypofunction was determined based on objective vestibular function tests. This guideline may not apply to individuals who report symptoms of dizziness, imbalance, and/or oscillopsia without a diagnosis of vestibular hypofunction.

Disclaimer:

These recommendations are intended as a guide to optimize rehabilitation outcomes for individuals undergoing vestibular physical therapy. The contents of this guideline were developed with support from the American Physical Therapy Association and the Academy of Neurologic Physical Therapy using a rigorous review process. The authors declared no conflict of interest and maintained editorial independence.

Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A369).

Feasibility and safety of an immersive virtual reality-based vestibular rehabilitation programme in people with multiple sclerosis experiencing vestibular impairment: a protocol for a pilot randomised controlled trial

INTRODUCTION

Vestibular system damage in patients with multiple sclerosis (MS) may have a central and/or peripheral origin. Subsequent vestibular impairments may contribute to dizziness, balance disorders and fatigue in this population. Vestibular rehabilitation targeting vestibular impairments may improve these symptoms. Furthermore, as a successful tool in neurological rehabilitation, immersive virtual reality (VRi) could also be implemented within a vestibular rehabilitation intervention.

METHODS AND ANALYSIS

This protocol describes a parallel-arm, pilot randomised controlled trial, with blinded assessments, in 30 patients with MS with vestibular impairment (Dizziness Handicap Inventory ≥16). The experimental group will receive a VRi vestibular rehabilitation intervention based on the conventional Cawthorne-Cooksey protocol; the control group will perform the conventional protocol. The duration of the intervention in both groups will be 7 weeks (20 sessions, 3 sessions/week). The primary outcomes are the feasibility and safety of the vestibular VRi intervention in patients with MS. Secondary outcome measures are dizziness symptoms, balance performance, fatigue and quality of life. Quantitative assessment will be carried out at baseline (T0), immediately after intervention (T1), and after a follow-up period of 3 and 6 months (T2 and T3). Additionally, in order to further examine the feasibility of the intervention, a qualitative assessment will be performed at T1.

ETHICS AND DISSEMINATION

The study was approved by the Andalusian Review Board and Ethics Committee, Virgen Macarena-Virgen del Rocio Hospitals (ID 2148-N-19, 25 March 2020). Informed consent will be collected from participants who wish to participate in the research. The results of this research will be disseminated by publication in peer-reviewed scientific journals.

TRIAL REGISTRATION NUMBER

NCT04497025.

Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation

Introduction Occupational rehabilitation often involves functional capacity evaluations (FCE) that use simulated work tasks to assess work ability. Currently, there exists no single, streamlined solution to simulate all or a large number of standard work tasks. Such a system would improve FCE and functional rehabilitation through simulating reaching maneuvers and more dexterous functional tasks that are typical of workplace activities. This paper reviews efforts to develop robotic FCE solutions that incorporate machine learning algorithms. Methods We reviewed the literature regarding rehabilitation robotics, with an emphasis on novel techniques incorporating robotics and machine learning into FCE. Results Rehabilitation robotics aims to improve the assessment and rehabilitation of injured workers by providing methods for easily simulating workplace tasks using intelligent robotic systems. Machine learning-based approaches combine the benefits of robotic systems with the expertise and experience of human therapists. These innovations have the potential to improve the quantification of function as well as learn the haptic interactions provided by therapists to assist patients during assessment and rehabilitation. This is done by allowing a robot to learn based on a therapist's motions ("demonstrations") what the desired workplace activity ("task") is and how to recreate it for a worker with an injury ("patient"). Through Telerehabilitation and internet connectivity, these robotic assessment techniques can be used over a distance to reach rural and remote locations. Conclusions While the research is in the early stages, robotics with integrated machine learning algorithms have great potential for improving traditional FCE practice.

SVM-based waist circumference estimation using Kinect

BACKGROUND AND OBJECTIVE

Conventional anthropometric studies using Kinect depth sensors have concentrated on estimating the distances between two points such as height. This paper deals with a novel waist measurement method using SVM regression, further widening spectrum of Kinect's potential applications. Waist circumference is a key index for the diagnosis of abdominal obesity, which has been linked to metabolic syndromes and other related diseases. Yet, the existing measuring method, tape measure, requires a trained personnel and is therefore costly and time-consuming.

METHODS

A dataset was constructed by recording both 30 frames of Kinect depth image and careful tape measurement of 19 volunteers by a clinical investigator. This paper proposes a new SVM regressor-based approach for estimating waist circumference. A waist curve vector is extracted from a raw depth image using joint information provided by Kinect SDK. To avoid overfitting, a data augmentation technique is devised. The 30 frontal vectors and 30 backside vectors, each sampled for 1 s per person, are combined to form 900 waist curve vectors and a total of 17,100 samples were collected from 19 individuals. On an individual basis, we performed leave-one-out validation using the SVM regressor with the tape measurement-gold standard of waist circumference measurement-values labeled as ground-truth. On an individual basis, we performed leave-one-out validation using the SVM regressor with the tape measurement-gold standard of waist circumference measurement-values labeled as ground-truth.

RESULTS

The mean error of the SVM regressor was 4.62 cm, which was smaller than that of the geometric estimation method. Potential uses are discussed.

CONCLUSIONS

A possible method for measuring waist circumference using a depth sensor is demonstrated through experimentation. Methods for improving accuracy in the future are presented. Combined with other potential applications of Kinect in healthcare setting, the proposed method will pave the way for patient-centric approach of delivering care without laying burdens on patients.

Artificial Intelligence Applications in Otology: A State of the Art Review

Objective

Recent advances in artificial intelligence (AI) are driving innovative new health care solutions. We aim to review the state of the art of AI in otology and provide a discussion of work underway, current limitations, and future directions.

Data Sources

Two comprehensive databases, MEDLINE and EMBASE, were mined using a directed search strategy to identify all articles that applied AI to otology.

Review Methods

An initial abstract and title screening was completed. Exclusion criteria included nonavailable abstract and full text, language, and nonrelevance. References of included studies and relevant review articles were cross-checked to identify additional studies.

Conclusion

The database search identified 1374 articles. Abstract and title screening resulted in full-text retrieval of 96 articles. A total of N = 38 articles were retained. Applications of AI technologies involved the optimization of hearing aid technology (n = 5; 13% of all articles), speech enhancement technologies (n = 4; 11%), diagnosis and management of vestibular disorders (n = 11; 29%), prediction of sensorineural hearing loss outcomes (n = 9; 24%), interpretation of automatic brainstem responses (n = 5; 13%), and imaging modalities and image-processing techniques (n = 4; 10%). Publication counts of the included articles from each decade demonstrated a marked increase in interest in AI in recent years.

Implications for Practice

This review highlights several applications of AI that otologists and otolaryngologists alike should be aware of given the possibility of implementation in mainstream clinical practice. Although there remain significant ethical and regulatory challenges, AI powered systems offer great potential to shape how healthcare systems of the future operate and clinicians are key stakeholders in this process.

Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability

Objective: To evaluate various machine learning algorithms in predicting peripheral vestibular dysfunction using the dataset of the center of pressure (COP) sway during foam posturography measured from patients with dizziness.

Study Design: Retrospective study.

Setting: Tertiary referral center.

Patients: Seventy-five patients with vestibular dysfunction and 163 healthy controls were retrospectively recruited. The dataset included the velocity, the envelopment area, the power spectrum of the COP for three frequency ranges and the presence of peripheral vestibular dysfunction evaluated by caloric testing in 75 patients with vestibular dysfunction and 163 healthy controls.

Main Outcome Measures: Various forms of machine learning algorithms including the Gradient Boosting Decision Tree, Bagging Classifier, and Logistic Regression were trained. Validation and comparison were performed using the area under the curve (AUC) of the receiver operating characteristic curve (ROC) and the recall of each algorithm using K-fold cross-validation.

Results: The AUC (0.90 ± 0.06) and the recall (0.84 ± 0.07) of the Gradient Boosting Decision Tree were the highest among the algorithms tested, and both of them were significantly higher than those of the logistic regression (AUC: 0.85 ± 0.08, recall: 0.78 ± 0.07). The recall of the Bagging Classifier (0.82 ± 0.07) was also significantly higher than that of logistic regression.

Conclusion: Machine learning algorithms can be successfully used to predict vestibular dysfunction as identified using caloric testing with the dataset of the COP sway during posturography. The multiple algorithms should be evaluated in each clinical dataset since specific algorithm does not always fit to any dataset. Optimization of the hyperparameters in each algorithm are necessary to obtain the highest accuracy.

Classification for Human Balance Capacity Based on Visual Stimulation under a Virtual Reality Environment

The normal and disordered people balance ability classification is a key premise for rehabilitation training. This paper proposes a multi-barycentric area model (MBAM), which can be applied for accurate video analysis based classification. First, we have invited fifty-three subjects to wear an HTC (High Tech Computer Corporation) VIVE (Very Immersive Virtual Experience) helmet and to walk ten meters while seeing a virtual environment. The subjects’ motion behaviors are collected as our balance ability classification dataset. Secondly, we use background differential algorithm and bilateral filtering as the preprocessing to alleviate the video noise and motion blur. Inspired by the balance principle of a tumbler, we introduce a MBAM model to describe the body balancing condition by computing the gravity center of a triangle area, which is surrounded by the upper, middle and lower parts of the human body. Finally, we can obtain the projection coordinates according to the center of gravity of the triangle, and get the roadmap of the subjects by connecting those projection coordinates. In the experiments, we adopt four kinds of metrics (the MBAM, the area variance, the roadmap and the walking speed) innumerical analysis to verify the effect of the proposed method. Experimental results show that the proposed method can obtain a more accurate classification for human balance ability. The proposed research may provide potential theoretical support for the clinical diagnosis and treatment for balance dysfunction patients.

Artificial Intelligence for the Otolaryngologist: A State of the Art Review

Objective

To provide a state of the art review of artificial intelligence (AI), including its subfields of machine learning and natural language processing, as it applies to otolaryngology and to discuss current applications, future impact, and limitations of these technologies.

Data Sources

PubMed and Medline search engines.

Review Methods

A structured search of the current literature was performed (up to and including September 2018). Search terms related to topics of AI in otolaryngology were identified and queried to identify relevant articles.

Conclusions

AI is at the forefront of conversation in academic research and popular culture. In recent years, it has been touted for its potential to revolutionize health care delivery. Yet, to date, it has made few contributions to actual medical practice or patient care. Future adoption of AI technologies in otolaryngology practice may be hindered by misconceptions of what AI is and a fear that machine errors may compromise patient care. However, with potential clinical and economic benefits, it is vital for otolaryngologists to understand the principles and scope of AI.

Implications for Practice

In the coming years, AI is likely to have a major impact on biomedical research and the practice of medicine. Otolaryngologists are key stakeholders in the development and clinical integration of meaningful AI technologies that will improve patient care. High-quality data collection is essential for the development of AI technologies, and otolaryngologists should seek opportunities to collaborate with data scientists to guide them toward the most impactful clinical questions.

Vestibular Rehabilitation for Peripheral Vestibular Hypofunction: An Evidence-Based Clinical Practice Guideline: FROM THE AMERICAN PHYSICAL THERAPY ASSOCIATION NEUROLOGY SECTION

BACKGROUND

Uncompensated vestibular hypofunction results in postural instability, visual blurring with head movement, and subjective complaints of dizziness and/or imbalance. We sought to answer the question, "Is vestibular exercise effective at enhancing recovery of function in people with peripheral (unilateral or bilateral) vestibular hypofunction?"

METHODS

A systematic review of the literature was performed in 5 databases published after 1985 and 5 additional sources for relevant publications were searched. Article types included meta-analyses, systematic reviews, randomized controlled trials, cohort studies, case control series, and case series for human subjects, published in English. One hundred thirty-five articles were identified as relevant to this clinical practice guideline.

RESULTS/DISCUSSION

Based on strong evidence and a preponderance of benefit over harm, clinicians should offer vestibular rehabilitation to persons with unilateral and bilateral vestibular hypofunction with impairments and functional limitations related to the vestibular deficit. Based on strong evidence and a preponderance of harm over benefit, clinicians should not include voluntary saccadic or smooth-pursuit eye movements in isolation (ie, without head movement) as specific exercises for gaze stability. Based on moderate evidence, clinicians may offer specific exercise techniques to target identified impairments or functional limitations. Based on moderate evidence and in consideration of patient preference, clinicians may provide supervised vestibular rehabilitation. Based on expert opinion extrapolated from the evidence, clinicians may prescribe a minimum of 3 times per day for the performance of gaze stability exercises as 1 component of a home exercise program. Based on expert opinion extrapolated from the evidence (range of supervised visits: 2-38 weeks, mean = 10 weeks), clinicians may consider providing adequate supervised vestibular rehabilitation sessions for the patient to understand the goals of the program and how to manage and progress themselves independently. As a general guide, persons without significant comorbidities that affect mobility and with acute or subacute unilateral vestibular hypofunction may need once a week supervised sessions for 2 to 3 weeks; persons with chronic unilateral vestibular hypofunction may need once a week sessions for 4 to 6 weeks; and persons with bilateral vestibular hypofunction may need once a week sessions for 8 to 12 weeks. In addition to supervised sessions, patients are provided a daily home exercise program.

DISCLAIMER

These recommendations are intended as a guide for physical therapists and clinicians to optimize rehabilitation outcomes for persons with peripheral vestibular hypofunction undergoing vestibular rehabilitation.Video Abstract available for more insights from the author (see Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A124).

Novel Virtual Environment for Alternative Treatment of Children with Cerebral Palsy

Cerebral palsy is a severe condition usually caused by decreased brain oxygenation during pregnancy, at birth or soon after birth. Conventional treatments for cerebral palsy are often tiresome and expensive, leading patients to quit treatment. In this paper, we describe a virtual environment for patients to engage in a playful therapeutic game for neuropsychomotor rehabilitation, based on the experience of the occupational therapy program of the Nucleus for Integrated Medical Assistance (NAMI) at the University of Fortaleza, Brazil. Integration between patient and virtual environment occurs through the hand motion sensor "Leap Motion," plus the electroencephalographic sensor "MindWave," responsible for measuring attention levels during task execution. To evaluate the virtual environment, eight clinical experts on cerebral palsy were subjected to a questionnaire regarding the potential of the experimental virtual environment to promote cognitive and motor rehabilitation, as well as the potential of the treatment to enhance risks and/or negatively influence the patient's development. Based on the very positive appraisal of the experts, we propose that the experimental virtual environment is a promising alternative tool for the rehabilitation of children with cerebral palsy.

Advantages and limitations of virtual reality for balance assessment and rehabilitation

Virtual reality (VR) is now commonly used in many domains because of its ability to provide a standardized, reproducible and controllable environment. In balance assessment, it can be used to control stimuli presented to patients and thus accurately evaluate their progression or compare them to different populations in standardized situations. In balance rehabilitation, VR allows the creation of new generation tools and at the same time the means to assess the efficiency of each parameter of these tools in order to optimize them. Moreover, with the development of low-cost devices, this rehabilitation can be continued at home, making access to these tools much easier, in addition to their entertaining and thus motivating properties. Nevertheless, and even more with low-cost systems, VR has limits that can alter the results of the studies that use it: the latency of the system (the delay cumulated on each step of the process from data acquisition on the patients to multimodal outputs); and distance perception, which tends to be underestimated in VR. After having described why VR is an essential tool for balance assessment and rehabilitation and illustrated this statement with a case study, this review discusses the previous works in the domain with regards to the technological limits of VR.