AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review.

Post-stroke therapy restores lost skills. Traditionally, patients are supported by skilled therapists who monitor their progress and evaluate the program's effectiveness. Due to a shortage of qualified therapists, rehabilitation facilities are both expensive and inadequate. Furthermore, evaluations may be subjective and prone to errors. These limitations motivate the researchers to devise automated systems with minimal human intervention, therapist-like assessment, and broader outreach. This article reviews seminal works from 2013 onwards, qualitatively and quantitatively adapting the PRISMA approach to examine the potential of robot-assisted, virtual reality-based rehabilitation and automated assessments through data-driven learning. Extensive experimentation on KIMORE and UI-PRMD datasets reveal high agreement between automated methods and therapists. Our investigation shows that deep learning with spatio-temporal skeleton data and dynamic attention outperforms others, with an RMSE as low as 0.55. Fully automated rehabilitation is still in development, but, being an active research topic, it could hasten objective assessment and improve outreach.

Graph Convolutional Networks for Assessment of Physical Rehabilitation Exercises.

Health professionals often prescribe patients to perform specific exercises for rehabilitation of several diseases (e.g., stroke, Parkinson, backpain). When patients perform those exercises in the absence of an expert (e.g., physicians/therapists), they cannot assess the correctness of the performance. Automatic assessment of physical rehabilitation exercises aims to assign a quality score given an RGBD video of the body movement as input. Recent deep learning approaches address this problem by extracting CNN features from co-ordinate grids of skeleton data (body-joints) obtained from videos. However, they could not extract rich spatio-temporal features from variable-length inputs. To address this issue, we investigate Graph Convolutional Networks (GCNs) for this task. We adapt spatio-temporal GCN to predict continuous scores(assessment) instead of discrete class labels. Our model can process variable-length inputs so that users can perform any number of repetitions of the prescribed exercise. Moreover, our novel design also provides self-attention of body-joints, indicating their role in predicting assessment scores. It guides the user to achieve a better score in future trials by matching the same attention weights of expert users. Our model successfully outperforms existing exercise assessment methods on KIMORE and UI-PRMD datasets.

COVIDXception-Net: A Bayesian Optimization-Based Deep Learning Approach to Diagnose COVID-19 from X-Ray Images.

COVID-19 is spreading around the world like wildfire. Chest X-rays are used as one of the primary tools for diagnosing COVID-19. However, about two-thirds of the world population do not have access to sufficient radiological services. In this work, we propose a deep learning-driven automated system, COVIDXception-Net, for diagnosing COVID-19 from chest X-rays. A primary challenge in any data-driven COVID-19 detection is the scarcity of COVID-19 data, which heavily deteriorates a deep learning model's performance. To address this issue, we incorporate a weighted-loss function that ensures the COVID-19 cases are given more importance during the training process. We also propose using Bayesian Optimization to find the best architecture for detecting COVID-19. Extensive experimentation on four publicly available COVID-19 datasets shows that our proposed model achieves an accuracy of 0.94, precision 0.95, recall 0.94, specificity 0.997, F1-score 0.94, and Matthews correlation coefficient 0.992 outperforming three widely used architectures-VGG16, MobileNetV2, and InceptionV3. It also surpasses the performance of several state-of-the-art COVID-19 detection methods. We also performed two ablation studies that show our model's accuracy degrades from 0.994 to 0.950 when a random search is used and to 0.983 when a regular loss function is employed instead of the Bayesian and weighted loss, respectively.

Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach.

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result. In this work, we proposed an automated COVID-19 classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High-Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed method, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

Deep Learning-Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis.

The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.