Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test.

A popular and widely suggested measure for assessing unilateral hand motor skills in stroke patients is the box and block test (BBT). Our study aimed to create an augmented reality enhanced version of the BBT (AR-BBT) and evaluate its correlation to the original BBT for stroke patients. Following G-power analysis, clinical examination, and inclusion-exclusion criteria, 31 stroke patients were included in this study. AR-BBT was developed using the Open Source Computer Vision Library (OpenCV). The MediaPipe's hand tracking library uses a palm and a hand landmark machine learning model to detect and track hands. A computer and a depth camera were employed in the clinical evaluation of AR-BBT following the principles of traditional BBT. A strong correlation was achieved between the number of blocks moved in the BBT and the AR-BBT on the hemiplegic side (Pearson correlation = 0.918) and a positive statistically significant correlation (p = 0.000008). The conventional BBT is currently the preferred assessment method. However, our approach offers an advantage, as it suggests that an AR-BBT solution could remotely monitor the assessment of a home-based rehabilitation program and provide additional hand kinematic information for hand dexterities in AR environment conditions. Furthermore, it employs minimal hardware equipment.

Image Quality Assessment Tool for Conventional and Dynamic Magnetic Resonance Imaging Acquisitions.

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification.

Multimodal automatic assessment of acute pain through facial videos and heart rate signals utilizing transformer-based architectures.

Accurate and objective pain evaluation is crucial in developing effective pain management protocols, aiming to alleviate distress and prevent patients from experiencing decreased functionality. A multimodal automatic assessment framework for acute pain utilizing video and heart rate signals is introduced in this study. The proposed framework comprises four pivotal modules: the Spatial Module, responsible for extracting embeddings from videos; the Heart Rate Encoder, tasked with mapping heart rate signals into a higher dimensional space; the AugmNet, designed to create learning-based augmentations in the latent space; and the Temporal Module, which utilizes the extracted video and heart rate embeddings for the final assessment. The Spatial-Module undergoes pre-training on a two-stage strategy: first, with a face recognition objective learning universal facial features, and second, with an emotion recognition objective in a multitask learning approach, enabling the extraction of high-quality embeddings for the automatic pain assessment. Experiments with the facial videos and heart rate extracted from electrocardiograms of the BioVid database, along with a direct comparison to 29 studies, demonstrate state-of-the-art performances in unimodal and multimodal settings, maintaining high efficiency. Within the multimodal context, 82.74% and 39.77% accuracy were achieved for the binary and multi-level pain classification task, respectively, utilizing 9.62 million parameters for the entire framework.

Virtual Hemodynamic Assessment of Coronary Lesions: The Advent of Functional Angiography and Coronary Imaging.

The fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive and invasive imaging modalities. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy. METHODS: Search of the bibliographic database for articles published from 2011 to 2023 using the following search terms: invasive FFR and non-invasive FFR. Pooled analysis of the sensitivity and specificity, with the corresponding confidence intervals from 32% to 94%. In addition, the summary processing times were determined. RESULTS: In total, 24 studies published between 2011 and 2023 were included, with a total of 13,591 patients and 3345 vessels. The diagnostic accuracy of the invasive and non-invasive techniques at the per-patient level was 89% (95% CI, 85-92%) and 76% (95% CI, 61-80%), respectively, while on the per-vessel basis, it was 92% (95% CI, 82-88%) and 81% (95% CI, 75-87%), respectively. CONCLUSION: These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice.

Using the recurrence plots as indicators for the recognition of Parkinson's disease through phonemes assessment.

Parkinson's disease (PD) is considered to be the second most common neurodegenerative disease which affects the patients' life throughout the years. As a consequence, its early diagnosis is of major importance for the improvement of life quality, implying that the severe symptoms can be delayed through appropriate clinical intervention and treatment. Among the most important premature symptoms of PD are the voice impairments of articulation, phonation and prosody. The objective of this study is to investigate whether the voice's dynamic behavior can be used as possible indicator for PD. Thus in this work, we employ the recurrence plots (RPs) which derive from the analysis of the three modulated vowels /a/, /e/ and /o/, which belong to the PC-GITA dataset, and are fed as input images to a 3-channel Convolutional Neural Network-based (CNN) architecture, which, finally, differentiates the 50 PD patients from 50 healthy subjects. The experimental results obtained provide evidence that the RP-based approach is a promising tool for the recognition of PD patients through the analysis of voice recordings, with a classification accuracy achieved equal to 87%.

An integrated rehabilitation system for the upper limb spasticity assessment and treatment: the Rehabotics passive exoskeletal system.

Limb spasticity is caused by stroke, multiple sclerosis, traumatic brain injury and various central nervous system pathologies such as brain tumors resulting in joint stiffness, loss of hand function and severe pain. This paper presents with the Rehabotics integrated rehabilitation system aiming to provide highly individualized assessment and treatment of the function of the upper limbs for patients with spasticity after stroke, focusing on the developed passive exoskeletal system. The proposed system can: (i) measure various motor and kinematic parameters of the upper limb in order to evaluate the patient's condition and progress, as well as (ii) offer a specialized rehabilitation program (therapeutic exercises, retraining of functional movements and support of daily activities) through an interactive virtual environment. The outmost aim of this multidisciplinary research work is to create new tools for providing high-level treatment and support services to patients with spasticity after stroke.Clinical Relevance- This paper presents a new passive exoskeletal system aiming to provide enhanced treatment and assessment of patients with upper limb spasticity after stroke.

An Automated Image-Based Dietary Assessment System for Mediterranean Foods.

Goal: The modern way of living has significantly influenced the daily diet. The ever-increasing number of people with obesity, diabetes and cardiovascular diseases stresses the need to find tools that could help in the daily intake of the necessary nutrients. Methods: In this paper, we present an automated image-based dietary assessment system of Mediterranean food, based on: 1) an image dataset of Mediterranean foods, 2) on a pre-trained Convolutional Neural Network (CNN) for food image classification, and 3) on stereo vision techniques for the volume and nutrition estimation of the food. We use a pre-trained CNN in the Food-101 dataset to train a deep learning classification model employing our dataset Mediterranean Greek Food (MedGRFood). Based on the EfficientNet family of CNNs, we use the EfficientNetB2 both for the pre-trained model and its weights evaluation, as well as for classifying food images in the MedGRFood dataset. Next, we estimate the volume of the food, through 3D food reconstruction of two images taken by a smartphone camera. The proposed volume estimation subsystem uses stereo vision techniques and algorithms, and needs the input of two food images to reconstruct the point cloud of the food and to compute its quantity. Results: The classification accuracy where true class matches with the most probable class predicted by the model (Top-1 accuracy) is 83.8%, while the accuracy where true class matches with any one of the 5 most probable classes predicted by the model (Top-5 accuracy) is 97.6%, for the food classification subsystem. The food volume estimation subsystem achieves an overall mean absolute percentage error 10.5% for 148 different food dishes. Conclusions: The proposed automated image-based dietary assessment system provides the capability of continuous recording of health data in real time.

An All-in-One Tool for 2D Atherosclerotic Disease Assessment and 3D Coronary Artery Reconstruction.

Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Computed tomography coronary angiography (CTCA) is also used as a non-invasive imaging alternative. In this work, we present a novel and unique tool for 3D coronary artery reconstruction and plaque characterization using the abovementioned imaging modalities or their combination. In particular, image processing and deep learning algorithms were employed and validated for the lumen and adventitia borders and plaque characterization at the IVUS and OCT frames. Strut detection is also achieved from the OCT images. Quantitative analysis of the X-ray angiography enables the 3D reconstruction of the lumen geometry and arterial centerline extraction. The fusion of the generated centerline with the results of the OCT or IVUS analysis enables hybrid coronary artery 3D reconstruction, including the plaques and the stent geometry. CTCA image processing using a 3D level set approach allows the reconstruction of the coronary arterial tree, the calcified and non-calcified plaques as well as the detection of the stent location. The modules of the tool were evaluated for efficiency with over 90% agreement of the 3D models with the manual annotations, while a usability assessment using external evaluators demonstrated high usability resulting in a mean System Usability Scale (SUS) score equal to 0.89, classifying the tool as "excellent".

GlucoseML Mobile Application for Automated Dietary Assessment of Mediterranean Food.

Over the years and with the help of technology, the daily care of type 1 diabetes has been improved significantly. The increased adoption of continuous glucose monitoring, the continuous subcutaneous insulin injection and the accurate behavioral monitoring mHealth solutions have contributed to this phenomenon. In this study we present a mobile application for automated dietary assessment of Mediterranean food images as part of the GlucoseML system. Based on short-term predictive analysis of the glucose trajectory, GlucoseML is a type-1 diabetes self-management system. A computer vision approach is used as main part of the GlucoseML dietary assessment system calculating food carbohydrates, fats and proteins, relying on: (i) a deep learning subsystem for food image classification, and (ii) a 3D food image reconstruction subsystem for the volume estimation of food. The deep learning subsystem achieves 82.4% and 97.5% top-1 and top-5 accuracy, respectively, for food image classification while the subsystem for volume estimation of food achieves a mean absolute percentage error 10.7% for the four main categories of MedGRFood dataset.

Exploring the Acceptability and Feasibility of Providing a Balance Tele-Rehabilitation Programme to Older Adults at Risk for Falls: An Initial Assessment.

Falls are a major health concern. The HOLOBALANCE tele-rehabilitation system was developed to deliver an evidence based, multi-sensory balance rehabilitation programme, to the elderly at risk of falls. The system delivers a series of balance physiotherapy exercises and cognitive and auditory training tasks prescribed by an expert balance physiotherapist following an initial balance assessment. The HOLOBALANCE system uses augmented reality (AR) to deliver exercises and games, and records task performance via a combination of body worn sensors and a depth camera. The HOLOBALANCE tele-rehabilitation system provides feedback to the supervising clinical team regarding task performance, participant usage and user feedback. Herewith we present the findings from the first 25 study participants regarding the feasibility and acceptability of the proposed system. The results of the clinical study indicate that the system is acceptable by the end users and also feasible for using in hospital and home environments.

Simulated versus physical bench tests: The economic evaluation of the InSilc platform for designing, developing, and assessing vascular scaffolds.

BACKGROUND: In silico medicine allows for pre-clinical and clinical simulated assessment of medical technologies and the building of patient-specific models to support medical decisions and forecast personal health status. While there is increasing trust in the potential central role of in silico medicine, there is a need to recognize its degree of reliability and evaluate its economic impact. An in silico platform has been developed within a Horizon 2020-funded project (In-Silc) for simulations functional to designing, developing, and assessing drug-eluting bioresorbable vascular scaffolds.The main purpose of this study was to compare the costs of 2 alternative strategies: the adoption of In-Silc platform versus the performance of only physical bench tests. METHODS: A case study was provided by a medical device company. The values of the model parameters were principally set by the project partners, with use of interviews and semi-structured questionnaires, and, when not available, through literature searches or derived by statistical techniques. An economic model was built to represent the 2 scenarios. RESULTS: The InSilc strategy is superior to the adoption of physical bench tests only. Ceteris paribus, the costs are 424,355€ for the former versus 857,811€ for the latter. CONCLUSIONS: In silico medicine tools can decrease the cost of the research and development of medical devices such as bioresorbable vascular scaffolds. Further studies are needed to explore the impact of such solutions on the innovation capacity of companies and the consequent potential advantages for target patients and the healthcare system.

Assessment of plasma ceramides as predictor for subclinical atherosclerosis.

Background and aims: Ceramides have been identified as novel biomarkers for cardiovascular disease (CVD) related events and mortality but their role in etiology of subclinical atherosclerosis is unknown. We aimed to assess association between plasma ceramides and carotid intima-media thickness (CIMT) and evaluate predictive value of the ceramides for high CIMT over traditional CVD risk factors. Methods: Association between plasma ceramides and CIMT in the Young Finns Study participants was analyzed with CIMT as outcome and ceramides along with traditional risk factors as predictors with regression model. Predictive value of the ceramides and related coronary event risk test (CERT) score for high CIMT as surrogate marker of subclinical atherosclerosis was assessed by comparing logistic regression-based prediction models including, i) traditional risk factors and ceramides, ii) traditional risk factors and CERT score, iii) age, sex and ceramides or alternatively CERT score with a reference model including only traditional risk factors. The prediction models were fitted to training data (70% data) and tested on test data (30% data). The predictive models were assessed with area under the receiver operating curve (AUC). The variance of AUC was estimated by repeating the model fitting and testing for 1000 bootstraps of the original data. Results: Predictive models with plasma ceramides or alternatively with CERT score in addition to age and sex variables were able to predict high CIMT with AUC 0.726 and 0.720 respectively. However, the ceramides and CERT score did not have statistically significant added predictive value for high CIMT over traditional risk factors. Conclusions: The new systemic biomarkers, high-risk plasma ceramides and CERT score, showed promising predictive performance for high CIMT with only age and sex as additional variables. This may help in predicting subclinical atherosclerosis for primary prevention.

Medical data quality assessment: On the development of an automated framework for medical data curation.

Data quality assessment has gained attention in the recent years since more and more companies and medical centers are highlighting the importance of an automated framework to effectively manage the quality of their big data. Data cleaning, also known as data curation, lies in the heart of the data quality assessment and is a key aspect prior to the development of any data analytics services. In this work, we present the objectives, functionalities and methodological advances of an automated framework for data curation from a medical perspective. The steps towards the development of a system for data quality assessment are first described along with multidisciplinary data quality measures. A three-layer architecture which realizes these steps is then presented. Emphasis is given on the detection and tracking of inconsistencies, missing values, outliers, and similarities, as well as, on data standardization to finally enable data harmonization. A case study is conducted in order to demonstrate the applicability and reliability of the proposed framework on two well-established cohorts with clinical data related to the primary Sjögren's Syndrome (pSS). Our results confirm the validity of the proposed framework towards the automated and fast identification of outliers, inconsistencies, and highly-correlated and duplicated terms, as well as, the successful matching of more than 85% of the pSS-related medical terms in both cohorts, yielding more accurate, relevant, and consistent clinical data.

Virtual Functional Assessment of Coronary Stenoses Using Intravascular Ultrasound Imaging: A Proof-of-Concept Pilot Study.

AIMS: We aimed to investigate the performance of virtual functional assessment of coronary stenoses using intravascular ultrasound (IVUS)-based three-dimensional (3D) coronary artery reconstruction against the invasively measured fractional flow reserve (FFR). METHODS AND RESULTS: Twenty-two (22) patients with either typical symptoms of stable angina or a positive stress test, who underwent IVUS and FFR, were included in this study. Five (5) patients presented FFR values lower than the 0.80 threshold, indicating ischaemia. IVUS-based 3D reconstruction and blood flow simulation were performed and the virtual functional assessment index (vFAI) was calculated. A strong correlation between IVUS-based vFAI and FFR was observed (Spearman correlation coefficient [rs]=0.88, p<0.0001). There was a small overestimation of the FFR by the IVUS-based vFAI (mean difference=0.0196±0.037; p=0.023 for difference from zero). All cases with haemodynamically significant stenoses (FFR≤0.8) were correctly categorised by the IVUS-based vFAI (vFAI≤0.8). CONCLUSION: The proposed approach allows the complete and comprehensive assessment of coronary stenoses providing anatomic and physiologic information, pre- and post-intervention, using only an IVUS catheter without the use of a pressure wire.

Noninvasive CT-based hemodynamic assessment of coronary lesions derived from fast computational analysis: a comparison against fractional flow reserve.

OBJECTIVES: Application of computational fluid dynamics (CFD) to three-dimensional CTCA datasets has been shown to provide accurate assessment of the hemodynamic significance of a coronary lesion. We aim to test the feasibility of calculating a novel CTCA-based virtual functional assessment index (vFAI) of coronary stenoses > 30% and ≤ 90% by using an automated in-house-developed software and to evaluate its efficacy as compared to the invasively measured fractional flow reserve (FFR). METHODS AND RESULTS: In 63 patients with chest pain symptoms and intermediate (20-90%) pre-test likelihood of coronary artery disease undergoing CTCA and invasive coronary angiography with FFR measurement, vFAI calculations were performed after 3D reconstruction of the coronary vessels and flow simulations using the finite element method. A total of 74 vessels were analyzed. Mean CTCA processing time was 25(± 10) min. There was a strong correlation between vFAI and FFR, (R = 0.93, p < 0.001) and a very good agreement between the two parameters by the Bland-Altman method of analysis. The mean difference of measurements from the two methods was 0.03 (SD = 0.033), indicating a small systematic overestimation of the FFR by vFAI. Using a receiver-operating characteristic curve analysis, the optimal vFAI cutoff value for identifying an FFR threshold of ≤ 0.8 was ≤ 0.82 (95% CI 0.81 to 0.88). CONCLUSIONS: vFAI can be effectively derived from the application of computational fluid dynamics to three-dimensional CTCA datasets. In patients with coronary stenosis severity > 30% and ≤ 90%, vFAI performs well against FFR and may efficiently distinguish between hemodynamically significant from non-significant lesions. KEY POINTS: Virtual functional assessment index (vFAI) can be effectively derived from 3D CTCA datasets. In patients with coronary stenoses severity > 30% and ≤ 90%, vFAI performs well against FFR. vFAI may efficiently distinguish between functionally significant from non-significant lesions.

Art care: A multi-modality coronary 3D reconstruction and hemodynamic status assessment software.

BACKGROUND: Due to the incremental increase of clinical interest in the development of software that allows the 3-dimensional (3D) reconstruction and the functional assessment of the coronary vasculature, several software packages have been developed and are available today. OBJECTIVE: Taking this into consideration, we have developed an innovative suite of software modules that perform 3D reconstruction of coronary arterial segments using different coronary imaging modalities such as IntraVascular UltraSound (IVUS) and invasive coronary angiography images (ICA), Optical Coherence Tomography (OCT) and ICA images, or plain ICA images and can safely and accurately assess the hemodynamic status of the artery of interest. METHODS: The user can perform automated or manual segmentation of the IVUS or OCT images, visualize in 3D the reconstructed vessel and export it to formats, which are compatible with other Computer Aided Design (CAD) software systems. We employ finite elements to provide the capability to assess the hemodynamic functionality of the reconstructed vessels by calculating the virtual functional assessment index (vFAI), an index that corresponds and has been shown to correlate well to the actual fractional flow reserve (FFR) value. RESULTS: All the modules of the proposed system have been thoroughly validated. In brief, the 3D-QCA module, compared to a successful commercial software of the same genre, presented very good correlation using several validation metrics, with a Pearson's correlation coefficient (R) for the calculated volumes, vFAI, length and minimum lumen diameter of 0.99, 0.99, 0.99 and 0.88, respectively. Moreover, the automatic lumen detection modules for IVUS and OCT presented very high accuracy compared to the annotations by medical experts with the Pearson's correlation coefficient reaching the values of 0.94 and 0.99, respectively. CONCLUSIONS: In this study, we have presented a user-friendly software for the 3D reconstruction of coronary arterial segments and the accurate hemodynamic assessment of the severity of existing stenosis.

The Evolution of mHealth Solutions for Heart Failure Management.

In the last decade, the uptake of information and communication technologies and the advent of mobile internet resulted in improved connectivity and penetrated different fields of application. In particular, the adoption of the mobile devices is expected to reform the provision and delivery of healthcare, overcoming geographical, temporal, and other organizational limitations. mHealth solutions are able to provide meaningful clinical information allowing effective and efficient management of chronic diseases, such as heart failure. A variety of data can be collected, such as lifestyle, sensor/biosensor, and health-related information. The analysis of these data empowers patients and the involved ecosystem actors, improves the healthcare delivery, and facilitates the transformation of existing health services. The aim of this study is to provide an overview of (i) the current practice in the management of heart failure, (ii) the available mHealth solutions, either in the form of the commercial applications, research projects, or related studies, and (iii) the several challenges related to the patient and healthcare professionals' acceptance, the payer and provider perspective, and the regulatory constraints.

Functional assessment of lesion severity without using the pressure wire: coronary imaging and blood flow simulation.

INTRODUCTION: Hemodynamic indices derived from measurements with the pressure wire (primarily fractional flow reserve [FFR]) have been established as a reliable tool for assessing coronary stenoses and improving clinical decision making. However, the use of the pressure wire constitutes a hurdle for the universal adoption of physiology-guided patient management. Technological advancements have enabled the large-scale application of blood flow simulation (computational fluid dynamics [CFD]) to medical imaging, thereby enabling the virtual assessment of coronary physiology. Areas covered: This review summarizes the stand-alone non-invasive (coronary computed tomographic imaging) and invasive (coronary angiography) imaging approaches which were initially used for predicting FFR, and focuses on the use of blood flow modeling for functional assessment of coronary lesions in clinical practice. Expert commentary: Validation studies of CFD-derived methodologies for functional assessment have shown that virtual indices correlate well and have good diagnostic accuracy compared to pressure wire-FFR despite inherent limitations of spatial resolution and assumptions regarding boundary conditions in flow modeling. Beyond point-to-point agreement with FFR, further studies are needed to demonstrate the clinical safety/efficacy of these computational tools regarding patient outcomes. Such evidence base could support the incorporation of these methodologies into routine patient management for decision making and reliable risk stratification.

HMMs in Protein Fold Classification.

The limitation of most HMMs is their inherent high dimensionality. Therefore we developed several variations of low complexity models that can be applied even to protein families with a few members. In this chapter we present these variations. All of them include the use of a hidden Markov model (HMM), with a small number of states (called reduced state-space HMM), which is trained with both amino acid sequence and secondary structure of proteins whose 3D structure is known and it is used for protein fold classification. We used data from Protein Data Bank and annotation from SCOP database for training and evaluation of the proposed HMM variations for a number of protein folds that belong to major structural classes. Results indicate that the variations have similar performance, or even better in some cases, on classifying proteins than SAM, which is a widely used HMM-based method for protein classification. The major advantage of the proposed variations is that we employed a small number of states and the algorithms used for training and scoring are of low complexity and thus relatively fast. The main variations examined include a version of the reduced state-space HMM with seven states (7-HMM), a version of the reduced state-space HMM with three states (3-HMM) and an optimized version of the reduced state-space HMM with three states, where an optimization process is applied to its scores (optimized 3-HMM).