Effects of Tredmil Training with and without Mirror Therapy on Lower Limb Function, Dynamic Balance, and Gait in Chronic Stroke Patients

Stroke, a brain attack, causes approximately 17 million new strokes annually, leading to motor deficits in up to 80% of patients, 30% experiencing long-term deficits in independent walking, and two-thirds experiencing functional limitations in lower limbs. The objective of this research is to determine the effect of treadmill training with and without mirror therapy on lower limb function, dynamic balance, and gait in chronic stroke patients. This research involved 24 chronic stroke patients divided into two groups: Group A received lower-limb treadmill training with mirror treatment, and Group B received treadmill training without mirror treatment. Both groups received traditional physiotherapy methods. The study found that MAS was non-parametric, while other measures were parametric. The trial lasted eight weeks, using six-minute walk testing, the modified Ashworth scale, and Fugl-Meyer assessment (lower extremity). The study found that the "mirror therapy" group had better results than the "control group" group, with P values of 0.023 and 0.00, respectively, for the outcome measures "MAS post-intervention" and "6MWT post-intervention." The "Mirror Therapy" group also improved more than the "Control Group" group in the outcome measure "FMA-LE Post-Intervention."This study concluded that there was a significant association between treadmill training and mirror therapy and lower limb functioning, dynamic balance, and gait in chronic stroke patients.&nbsp

ADL-BSDF: A Deep Learning Framework for Brain Stroke Detection from MRI Scans towards an Automated Clinical Decision Support System

Deep learning has emerged to be efficient Artificial Intelligence (AI) phenomena to solve problems in healthcare industry. Particularly Convolutional Neural Network (CNN) models have attracted researchers due to their efficiency in medical image analysis. According to World Health Organization (WHO), rapidly developing cerebral malfunction, brain stroke, is the second leading cause of death across the globe. Brain MRI scans, when analysed quantitatively, play vital role in diagnosis and treatment of stroke. There are many existing methods built on deep learning for stroke diagnosis. However, an automatic, reliable and faster method that not only helps in stroke diagnosis but also demarcate affected regions as part of Clinical Decision Support System (CDSS) is much desired. Towards this objective, we proposed an Automated Deep Learning based Brain Stroke Detection Framework (ADL-BSDF). It does not rely on expertise of healthcare professional in diagnosis and know the extent of damage enabling physician to make quick decisions. The framework is realized by two algorithms proposed. The first algorithm known as CNN-based Deep Learning for Brain Stroke Detection (CNNDL-BSD) focuses on accurate detection of stroke. The second algorithm, Deep Auto encoder for Stroke Severity Detection (DA-SSD), focuses on revealing extent of damage or severity of the stroke. The framework is evaluated against state of the art deep learning models such as EfficientNet, ResNet50 and VGG16

Analysing an Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques

A stroke is a medical condition characterized by the rupture of blood vessels within the brain which can lead to brain damage. Various symptoms may be exhibited when the brain's supply of blood and essential nutrients is disrupted. To forecast the possibility of brain stroke occurring at an early stage using Machine Learning (ML) and Deep Learning (DL) is the main objective of this study. Timely detection of the various warning signs of a stroke can significantly reduce its severity. This paper performed a comprehensive analysis of features to enhance stroke prediction effectiveness. A reliable dataset for stroke prediction is taken from the Kaggle website to gauge the effectiveness of the proposed algorithm. The dataset has a class imbalance problem which means the total number of negative samples is higher than the total number of positive samples. The results are reported based on a balanced dataset created using oversampling techniques. The proposed work used Smote and Adasyn to handle imbalanced problem for better evaluation metrics. Additionally, the hybrid Neural Network and Random Forest (NN-RF) utilizing the balanced dataset by Adasyn oversampling achieves the highest F1-score of 75% compared to the original unbalanced dataset and other benchmarking algorithms. The proposed algorithm with balanced data utilizing hybrid NN-RF achieves an accuracy of 84%. Advanced ML techniques coupled with thorough data analysis enhance stroke prediction. This study underscores the significance of data-driven methodologies, resulting in improved accuracy and comprehension of stroke risk factors. Applying these methodologies to medical fields can enhance patient care and public health outcomes. By integrating our discoveries, we can enhance the efficiency and effectiveness of the public health system

Point of view on outcome prediction models in post-stroke motor recovery

Stroke is a leading cause of disability worldwide which can cause significant and persistent upper limb (UL) impairment. It is difficult to predict UL motor recovery after stroke and to forecast the expected outcomes of rehabilitation interventions during the acute and subacute phases when using clinical data alone. Accurate prediction of response to treatment could allow for more timely and targeted interventions, thereby improving recovery, resource allocation, and reducing the economic impact of post-stroke disability. Initial motor impairment is currently the strongest predictor of post-stroke motor recovery. Despite significant progress, current prediction models could be refined with additional predictors, and an emphasis on the time dependency of patient-specific predictions of UL recovery profiles. In the current paper a panel of experts provide their opinion on additional predictors and aspects of the literature that can help advance stroke outcome prediction models. Potential strategies include close attention to post-stroke data collection timeframes and adoption of individual-computerized modeling methods connected to a patient’s health record. These models should account for the non-linear and the variable recovery pattern of spontaneous neurological recovery. Additionally, input data should be extended to include cognitive, genomic, sensory, neural injury, and function measures as additional predictors of recovery. The accuracy of prediction models may be further improved by including standardized measures of outcome. Finally, we consider the potential impact of refined prediction models on healthcare costs

Uso de Redes Neurais para a Predição de Diagnóstico de AVE: Uma Revisão Sistemática

Fundamentos: O Acidente Vascular Encefálico (AVE) é uma síndrome de déficit neurológico agudo atribuído à lesão vascular do Sistema Nervoso (SN). As técnicas de Inteligência Artificial (IA) na Medicina — como algoritmos de Redes Neurais Artificiais (RNAs) — têm ajudado na tomada de decisões clínicas voltadas para essa condição. Objetivo: o objetivo desta revisão será avaliar como as redes neurais artificiais estão sendo utilizadas para a predição de diagnóstico de AVE. Métodos: Trata-se de uma revisão sistemática de artigos indexados nas bases de dados PubMed, BVS, SciELO, Cochrane e SpringerLink, entre janeiro e fevereiro de 2022. Os critérios de inclusão e filtros para esse trabalho foram: artigos relacionados ao tema, estudos randomizados, coorte e ensaios clínicos, trabalhos em humanos, realizados nos últimos 5 anos, apenas nos idiomas Português, Inglês e Espanhol e com texto completo disponível gratuitamente. Os parâmetros de exclusão foram: artigos duplicados, fuga ao tema, artigos de revisão e trabalhos que não preenchiam todos os critérios de inclusão. Resultados: As RNAs estão sendo utilizadas, principalmente, para avaliação de áreas de lesões isquêmicas e hemorrágicas por métodos de segmentação e os exames mais utilizados para a modelagem dos programas têm sido Ressonância Magnética (RM) e Tomografia Computadorizada (TC). Além da TC e RM, a angiorressonância e angiotomografia também estão sendo utilizadas para o modelamento do algoritmo e são úteis por apresentarem maior sensibilidade para detecção de infartos. Conclusão: Algoritmos de segmentação e classificação aplicados nas RNAs fazem parte da medicina personalizada e servem de base para médicos na prática clínica

KLASIFIKASI CITRA STROKE MENGGUNAKAN AUGMENTASI DAN CONVOLUTIONAL NEURAL NETWORK EFFICIENTNET-B0

Stroke adalah gangguan fungsional otak secara tiba-tiba yang disebabkan oleh disfungsi otak klinis fokal dan global selama 24 jam bahkan lebih. Sebanyak 15 juta jiwa meninggal diakibatkan oleh stroke setiap tahunnya. Pasien yang terkena stroke harus segera ditangani sehingga bisa meminimalisir resiko kerusakan otak. Salah satu pendukung diagnosis stroke yaitu melalui analisa citra hasil pemindaian Computed Tomograpghy (CT-Scan). Seiring perkembangan zaman, teknologi pengolahan citra memungkinkan untuk mendeteksi pola stroke pada citra otak, dapat membantu dokter dan ahli radiologi dalam proses diagnosis dan pengobatan. Penelitian ini bertujuan untuk melakukan perbandingan tingkat akurasi menggunakan augmentasi dan tanpa augmentasi serta hyperparameter menggunakan Convolutional Neural Network dalam arsitektur EfficientNet-B0 untuk melakukan klasifikasi citra stroke iskemik, hemoragik, dan otak normal. Augmentasi data yang digunakan adalah dengan melakukan rotation, horizontal flip, dan pengaturan contrast pada data asli. Data uji disediakan sebanyak 20% dari porsi data asli dan augmentasi, dan 80% lainnya digunakan untuk proses training pencarian model optimal. Pencarian model berdasarkan komposisi data latih dan validasi dengan perbandingan 70:30, 80:20, dan 90:10. Hasil eksperimen menunjukkan performa yang terbaik diperoleh pada gabungan citra asli dan augmentasi, dengan akurasi dan F1- score berturut-turut sebesar 97%, 93%, dan 94% untuk data uji citra asli, citra augmentasi, dan citra gabungan. Penggabungan citra asli dan augmentasi untuk data training telah menunjukkan bahwa model cukup robust untuk dapat menghasilkan akurasi yang tinggi

Classification of stroke disease using machine learning algorithms

© 2019, Springer-Verlag London Ltd., part of Springer Nature. This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients’ symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69