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Brain-computer interface (BCI) systems combined with virtual reality (VR) gaming have the potential to revolutionize human-computer interaction by providing immersive and intuitive control mechanisms. This study aimed to evaluate the performance of BCI-VR in a goalkeeper gaming task and explore the influence of cognitive abilities on BCI performance. Forty-four healthy volunteers participated in the study. All participants carried out a BCI-VR Goalkeeper task and underwent a left-hand versus right-hand movement imagery task while wearing a VR headset. Twenty-two participants carried out the Flanker task and the Spatial Cueing task and another twenty-two participants carried out the Mental Body Rotation (MBRT) and Spatial Orientation (SOT) tasks. Six classification algorithms were employed for offline and real-time analysis. The Random Forest algorithm exhibited the highest accuracy rates both offline and in real-time. Results from the Flanker task revealed a positive correlation between the mean accuracy for the congruent trials of the Flanker task and the mean offline classification accuracy in the BCI-VR Goalkeeper task. Additionally, High Achievers in the BCI-VR Goalkeeper task had larger benefits from attentional cues in service of perception than from attentional cues in service of visual working memory (VWM). These findings suggest the impact of cognitive abilities on BCI-VR performance and emphasize the need to consider cognitive mechanisms and develop cognitive training interventions to enhance humans to produce appropriate EEG patterns while improving BCI accuracy. Further research should explore other cognitive factors and strive to improve the usability and effectiveness of BCI-VR systems for real-world applications. Overall, the current findings contribute to advancing BCI technology and its potential for neurorehabilitation, assistive technologies, and gaming entertainment.

Immersive experiential technologies find fertile grounds to grow and support healthcare education. Virtual, Augmented, or Mixed reality (VR/AR/MR) have proven to be impactful in both the educational and the affective state of the healthcare student’s increasing engagement. However, there is a lack of guidance for healthcare stakeholders on developing and integrating virtual reality resources into healthcare training. Thus, the authors applied Bardach’s Eightfold Policy Analysis Framework to critically evaluate existing protocols to determine if they are inconsistent, ineffective, or result in uncertain outcomes, following systematic pathways from concepts to decision-making. Co-creative VR resource development resulted as the preferred method. Best practices for co-creating VR Reusable e-Resources identified co-creation as an effective pathway to the prolific use of immersive media in healthcare education. Co-creation should be considered in conjunction with a training framework to enhance educational quality. Iterative cycles engaging all stakeholders enhance educational quality, while co-creation is central to the quality assurance process both for technical and topical fidelity, and tailoring resources to learners’ needs. Co-creation itself is seen as a bespoke learning modality. This paper provides the first body of evidence for co-creative VR resource development as a valid and strengthening method for healthcare immersive content development. Despite prior research supporting co-creation in immersive resource development, there were no established guidelines for best practices.

Monitoring disease evolution in Multiple sclerosis (MS) subjects may aid in decision making for personalizing treatment and disease evolution prediction. We investigate the use of disability progression, using clinical features, the expanded disability status scale (EDSS), and their relationship with texture features and Amplitude Modulation-Frequency Modulation (AM-FM) features extracted from MRI MS detectable lesions for the prognosis of future disability on magnetic resonance imaging (MRI). MS detectable brain lesions from N=38 symptomatic untreated subjects diagnosed with clinically isolated syndrome (CIS), were manually segmented, by an experienced MS neurologist, on transverse T2-weighted (T2W) images obtained from serial brain MRI scans at the baseline (Time0M) and the repeat (Time16-12M) examinations. The subjects were separated into two different groups based on their EDSS: (G1: $1\le $ EDSS $_{\mathrm {2Y}}\le 3.5$ (N=26) and G2: $3.5 < $ EDSS $_{\mathrm {2Y}}\le 8.5$ (N=12) and were monitored over ten years’ time (Time $_{\mathrm {10Y}}$ ). After intensity normalization and image registration, texture and AM-FM features were extracted from all MS lesions at Time0M and Time16-12M. The extracted features were used to develop models that correlated with the disease progression in Time10Y. We found statistically significant differences for features extracted from the two different groups (G1 vs G2 at Time $_{\mathrm {10Y}}$ ) and these might be used to predict the development and or the severity of the MS disease. The best model for classifying G1 vs G2 subjects at Time10Y included information taken from the MS lesion images, texture features and AM-FM features extracted from those MS lesion images (with a correct classification score of %CC=94). The proposed methodology may contribute to additional factors for predicting the development and assessing the severity of the MS disease. However, a larger scale study is needed to establish the application in clinical practice and for computing additional features that may provide information for better and earlier differentiation between normal tissue and MS lesions.

The present article explores the most popular approaches and the best practices for the design and implementation of cognitive gaming interventions that combine Brain Computer Interface (BCI) systems with Virtual Reality (VR). We focus on interventions that target cognitive skills related to perception, visuospatial attention and visuospatial memory. To this purpose, we review the techniques and algorithms that are commonly used for data pre-processing, feature extraction, and classification in such interventions. We discuss issues related to BCI-VR Cognitive Gaming, including the BCI paradigms, the action tasks and environments, user characteristics, algorithms, channels, accuracy, and the most prominent findings. We conclude with a discussion of the current challenges, limitations, future research directions, and the potential commercial applications of BCI-VR in cognitive gaming.

IntroductionAlzheimer’s disease (AD) even nowadays remains a complex neurodegenerative disease and its diagnosis relies mainly on cognitive tests which have many limitations. On the other hand, qualitative imaging will not provide an early diagnosis because the radiologist will perceive brain atrophy on a late disease stage. Therefore, the main objective of this study is to investigate the necessity of quantitative imaging in the assessment of AD by using machine learning (ML) methods. Nowadays, ML methods are used to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers in the assessment of AD.MethodsIn this study radiomic features from both entorhinal cortex and hippocampus were extracted from 194 normal controls (NC), 284 mild cognitive impairment (MCI) and 130 AD subjects. Texture analysis evaluates statistical properties of the image intensities which might represent changes in MRI image pixel intensity due to the pathophysiology of a disease. Therefore, this quantitative method could detect smaller-scale changes of neurodegeneration. Then the radiomics signatures extracted by texture analysis and baseline neuropsychological scales, were used to build an XGBoost integrated model which has been trained and integrated.ResultsThe model was explained by using the Shapley values produced by the SHAP (SHapley Additive exPlanations) method. XGBoost produced a f1-score of 0.949, 0.818, and 0.810 between NC vs. AD, MC vs. MCI, and MCI vs. AD, respectively.DiscussionThese directions have the potential to help to the earlier diagnosis and to a better manage of the disease progression and therefore, develop novel treatment strategies. This study clearly showed the importance of explainable ML approach in the assessment of AD.

The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.

Video compression is the core technology in mobile (mHealth) and electronic (eHealth) health video streaming applications. With global video traffic projected to reach 82% of all Internet traffic by 2022, there is a strong need to develop efficient compression algorithms to accommodate expected future growth. For the first time in decades, and especially since ISO/IEC MPEG and ITU-T VCEG expert groups strategically joined forces to develop the highly successful H.264/AVC standard, we have two distinct initiatives competing for the best performing video codec. On the one hand, we have the Alliance for Open Media (AOM) that support a new, royalty free video codec generation, termed AV1, based on VP8 and VP9 efforts. On the other hand, the Joint Video Exploration Team (JVET) has been developing the Versatile Video Codec (VVC) as the successor of the High Efficiency Video Coding (HEVC) standard. At the same time, the breadth of applications utilizing video codecs, involving significant content variability and moving across the video resolution ladder, to satisfy different constraints, have resulted in mixed literature results, with respect to the best performing codec. In this paper, we compare the performance of emerging VVC and AV1 codecs, along with popular HEVC implementations, namely the HEVC Test Model (HM) and x265, as well as earlier, VP9 codec, and investigate their suitability for medical applications. To the best of our knowledge, this is the first performance comparison of emerging VVC and AV1 video codecs for use in the healthcare domain. Experimental evaluation based on three datasets (ultrasound, emergency scenery, and general-purpose videos) demonstrate that VVC outperforms all rival codecs while AV1 achieves better compression efficiency than HEVC in all cases but low-resolution ($560\times448$ @40Hz) ultrasound videos of the common carotid artery. Furthermore, the use of video despeckling prior to ultrasound video compression can provide significant bitrate savings.

This review of our experience in computer-assisted tissue image analysis (CATIA) research shows that significant information can be extracted and used to diagnose and distinguish normal from abnormal endometrium. CATIA enabled the evaluation and differentiation between the benign and malignant endometrium during diagnostic hysteroscopy. The efficacy of texture analysis in the endometrium image during hysteroscopy was examined in 40 women, where 209 normal and 209 abnormal regions of interest (ROIs) were extracted. There was a significant difference between normal and abnormal endometrium for the statistical features (SF) features mean, variance, median, energy and entropy; for the spatial grey-level difference matrix (SGLDM) features contrast, correlation, variance, homogeneity and entropy; and for the gray-level difference statistics (GLDS) features homogeneity, contrast, energy, entropy and mean. We further evaluated 52 hysteroscopic images of 258 normal and 258 abnormal endometrium ROIs, and tissue diagnosis was verified by histopathology after biopsy. The YCrCb color system with SF, SGLDM and GLDS color texture features based on support vector machine (SVM) modeling correctly classified 81% of the cases with a sensitivity and a specificity of 78% and 81%, respectively, for normal and hyperplastic endometrium. New technical and computational advances may improve optical biopsy accuracy and assist in the precision of lesion excision during hysteroscopy. The exchange of knowledge, collaboration, identification of tasks and CATIA method selection strategy will further improve computer-aided diagnosis implementation in the daily practice of hysteroscopy.

Alzheimer’s disease (AD) brain magnetic resonance imaging (MRI) biomarkers based on larger-scale tissue neurodegeneration changes, such as atrophy, are currently widely used. Texture analysis evaluates the statistical properties of the tissue image quantitatively; therefore, it could detect smaller-scale changes of neurodegeneration. Entorhinal cortex is the first region affected, and no study has investigated texture analysis on this region before. This study aims to differentiate AD patients from Normal Control (NC) and Mild Cognitive Impairment (MCI) subjects using entorhinal cortex texture features. Furthermore, it was evaluated whether texture has association to MCI beyond that of volume, to evaluate if atrophy development may precede. Texture features were extracted from 194 NC, 200 MCI, 84 MCI who converted to AD (MCIc), and 130 AD subjects. Receiving operating characteristic curves determined the performance of the various features in discriminating the groups, and a predictive model was used to predict conversion of MCIc subjects to AD. An area under the curve (AUC) of 0.872, 0.710, 0.730, and 0.764 was seen between NC vs. AD, NC vs. MCI, MCI vs. MCIc, and MCI vs. AD subjects, respectively. Including entorhinal cortex volume improved the AUCs to 0.914, 0.740, 0.756, and 0.780, respectively. For the disease prediction, binary logistic regression was applied on five randomly selected test groups and achieved on average AUC’s of 0.760 and 0.764 on the training and validation cohorts, respectively. Entorhinal cortex texture features were significantly different between the four groups and in many cases provided better results compared to other methods such as volumetry.

Measurements of ultrasound diaphragmatic motion, amplitude, force, and velocity of contraction may provide important and essential information about diaphragmatic fatigue, weakness, or paralysis. In this paper, we propose and evaluate a semi-automated analysis system for measuring the diaphragmatic motion and estimating the maximum relaxation rate (MRR_SAUS) from ultrasound M-mode images of the diaphragmatic muscle. The system was evaluated on 27 M-mode ultrasound images of the diaphragmatic muscle [20 with no resistance (NRES) and 7 with resistance (RES)]. We computed semi-automated ultrasound MRR measurements on all NRES/RES images, using the proposed system (MRR_SAUS = 3.94 ± 0.91/4.98 ± 1.98 [1/s]), and compared them with the manual measurements made by a clinical expert (MRR_MUS = 2.36 ± 1.19/5.8 ± 2.1 [1/s],) and those made by a reference manual method (MRR_MB = 3.93 ± 0.89/3.73 ± 0.52 [1/sec], performed manually with the Biopac system. MRR_SAUS and MRR_MB measurements were not statistically significantly different for NRES and RES subjects but were significantly different with the MRR-MUS measurements made by the clinical expert. It is anticipated that the proposed system might be used in the future in the clinical practice in the assessment and follow up of patients with diaphragmatic weakness or paralysis. It may thus potentially help to understand post-operative pulmonary dysfunction or weaning failure from mechanical ventilation. Further validation and additional experimentation in a larger sample of images and different patient groups is required for further validating the proposed system.

Patients with multiple sclerosis (MS) are characterized by, among other symptoms, impaired functional capacity and walking difficulties. Polyunsaturated fatty acids (PUFAs) have been found to improve MS patients’ clinical outcomes; however, their effect on other parameters associated with daily living activities need further investigation. The current study aimed to examine the effect of a 24-month supplementation with a cocktail dietary supplement formula, the NeuroaspisTM PLP10, containing specific omega-3 and omega-6 PUFAs and specific antioxidant vitamins on gait and functional capacity parameters of patients with MS. Fifty-one relapsing-remitting MS (RRMS) patients with low disability scores (age: 38.4 ± 7.1 years; 30 female) were randomized 1:1 to receive either a 20 mL daily dose of the dietary formula containing a mixture of omega-3 and omega-6 PUFAs (12,150 mg), vitamin A (0.6 mg), vitamin E (22 mg), and γ-tocopherol (760 mg), the OMEGA group (n = 27; age: 39 ± 8.3 years), or 20 mL placebo containing virgin olive oil, the placebo group (n = 24; age: 37.8 ± 5.3 years). The mean ± SD (standard deviation) Expanded Disability Status Scale (EDSS) score for the placebo group was 2.36 and for the OMEGA group 2.22. All enrolled patients in the study were on Interferon-β treatment. Spatiotemporal gait parameters and gait deviation index (GDI) were assessed using a motion capture system. Functional capacity was examined using various functional tests such as the six-minute walk test (6MWT), two sit-to-stand tests (STS-5 and STS-60), and the Timed Up and Go test (TUG). Isometric handgrip strength was assessed by a dynamometer. Leg strength was assessed using an isokinetic dynamometer. All assessments were performed at baseline and at 12 and 24 months of supplementation. A total of 36 patients completed the study (18 from each group). Six patients from the placebo group and 9 patients from the OMEGA group dropped out from the study or were lost to follow-up. The dietary supplement significantly improved the single support time and the step and stride time (p < 0.05), both spatiotemporal gait parameters. In addition, while GDI of the placebo group decreased by about 10% at 24 months, it increased by about 4% in the OMEGA group (p < 0.05). Moreover, performance in the STS-60 test improved in the OMEGA group (p < 0.05) and there was a tendency for improvement in the 6MWT and TUG tests. Long-term supplementation with high dosages of omega-3 and omega-6 PUFAs (compared to previous published clinical studies using PUFAs) and specific antioxidant vitamins improved some functional capacity and gait parameters in RRMS patients.

The objective of this paper was to investigate texture feature variability in ultrasound video of the carotid artery during the cardiac cycle in an attempt to define new discriminatory biomarkers of the vulnerable plaque. More specifically, in this paper, 120 longitudinal ultrasound videos, acquired from 40 normal (N) subjects from the common carotid artery and 40 asymptomatic (A) and 40 symptomatic (S) subjects from the proximal internal carotid artery were investigated. The videos were intensity normalized and despeckled, and the intima-media complex (IMC) (from the N subjects) and the atherosclerotic carotid plaques (from the A and S subjects) were segmented from each video, in order to extract the M-mode image, and the texture features associated with cardiac states of systole and diastole. The main results of this paper can be summarized as follows: 1) texture features varied significantly throughout the cardiac cycle with significant differences identified between the cardiac systolic and cardiac diastolic states; 2) gray scale median was significantly higher at cardiac systole versus diastole for the N, A, and S groups investigated; 3) plaque texture features extracted during the cardiac cycle at the systolic and diastolic states were statistically significantly different between A and S subjects (and can thus be used to discriminate between A and S subjects successfully). The combination of systolic and diastolic features yields better performance than those alone. It is anticipated that the proposed system may aid the physician in clinical practice in classifying between N, A, and S subjects using texture features extracted from ultrasound videos of IMC and carotid artery plaque. However, further evaluation has to be carried out with more videos and additional features.

Recent advancements in Virtual Reality (VR) immersive technologies provide new tools for the development of novel and promising applications for neurological rehabilitation. The purpose of this paper is to review the emerging VR applications developed for the evaluation and treatment of patients with neurological diseases. We start by discussing the impact of novel VR tasks that encourage and facilitate the patient's empowerment and involvement in the rehabilitation process. Then, a systematic review was carried out on six well-known electronic libraries using the terms: “Virtual Reality AND Neurorehabilitation,” or “Head Mounted Display AND Neurorehabilitation.” This review focused on fully-immersive VR systems for which 12 relevant studies published in the time span of the last five years (from 2014 to 2019) were identified. Overall, this review paper examined the use of VR in certain neurological conditions such as dementia, stroke, spinal cord injury, Parkinson's, and multiple sclerosis. Most of the studies reveal positive results suggesting that VR is a feasible and effective tool in the treatment of neurological disorders. In addition, the finding of this systematic literature review suggested that low-cost, immersive VR technologies can prove to be effective for clinical rehabilitation in healthcare, and home-based setting with practical implications and uses. The development of VR technologies in recent years has resulted in more accessible and affordable solutions that can still provide promising results. Concluding, VR and interactive devices resulted in the development of holistic, portable, accessible, and usable systems for certain neurological disease interventions. It is expected that emerging VR technologies and tools will further facilitate the development of state of the art applications in the future, exerting a significant impact on the wellbeing of the patient.

The intima-media thickness (IMT) of the common carotid artery (CCA) is an established indicator of cardiovascular disease (CVD). There have been reports about the difference between the left and the right sides of the CCA IMT and their relation with CVD. In this paper, we propose an automated system based on image normalization, speckle reduction filtering, and snakes segmentation, for segmenting the CCA, perform IMT measurements, and provide the differences between the left and the right sides. The study was performed on 1104 longitudinal-section ultrasound images acquired from 568 men and 536 women out of which 125 had cardiovascular symptoms (CVD). A cardiovascular expert manually delineated the IMT for the normal and the CVD groups. The corresponding (normal versus CVD) IMT mean ± standard deviation values for the left and the right sides were 0.74 ± 0.24 versus 0.87 ± 0.24 mm and 0.70 ± 0.17 versus 0.80 ± 0.18 mm, respectively. The main findings of this paper can be summarized as follows: 1) there was no significant difference between the CCA left side IMT and the right side IMT. These findings suggest that the measurement of the CCA IMT on one side only is needed for the normal group (and this is in agreement with other studies); 2) there were statistical significant differences for the IMT measurements between the normal group and the CVD group for both the left and the right sides; 3) there was an increasing linear relationship of the left and the right IMT measurements with age for the normal group; and to a lesser extend for the CVD group; 4) no statistical significant differences were found between the manual and the automated IMT measurements for both sides; and 5) the best result for classification disease modeling, using support vector machines, to discriminate between the normal and the CVD groups was a 64%±3.5% correct classifications score when using both the left and the right IMT automated measurements. Further research is required for estimating differences and similarities between left and right intima media complex structure and morphology and their variability with texture features for differentiating between the normal and the CVD group.