CHARACTERIZATION OF STROKE LESION USING FRACTAL ANALYSIS

Objective: The characterization of stroke lesions is a challenging research issue due to the wide variability in the structure of lesion patterns. The objective of this research work is to characterize the stroke lesion structures using fractal analysis.Methods: To characterize the complex nature of the lesion structures, fractal box counting analysis is presented in this work. Three parameters from fractal dimension (FD) are considered to characterize the nature of the normal and abnormal brain tissues.Results: The experimental results are presented for 15 different datasets. Three different parameters namely FD average, FD deviation, and FDlacunarity are extracted to quantify the properties of the stroke lesion. The observations indicate that there is a significant proportion of separationof feature values between the normal and abnormal brain tissues.Conclusion: This work presents an efficient scheme for characterizing the stroke lesions using fractal parameters. It could be further enhanced by incorporating features extracted from other non-linear techniques.Â

Improved Stroke Detection at Early Stages Using Haar Wavelets and Laplacian Pyramid

Stroke merupakan pembunuh nomor tiga di dunia, namun hanya sedikit metode tentang deteksi dini. Oleh karena itu dibutuhkan metode untuk mendeteksi hal tersebut. Penelitian ini mengusulkan sebuah metode gabungan untuk mendeteksi dua jenis stroke secara simultan. Haar wavelets untuk mendeteksi stroke hemoragik dan Laplacian pyramid untuk mendeteksi stroke iskemik. Tahapan dalam penelitian ini terdiri dari pra proses tahap 1 dan 2, Haar wavelets, Laplacian pyramid, dan perbaikan kualitas citra. Pra proses adalah menghilangkan bagian tulang tengkorak, reduksi derau, perbaikan kontras, dan menghilangkan bagian selain citra otak. Kemudian dilakukan perbaikan citra. Selanjutnya Haar wavelet digunakan untuk ekstraksi daerah hemoragik sedangkan Laplacian pyramid untuk ekstraksi daerah iskemik. Tahapan terakhir adalah menghitung fitur Grey Level Cooccurrence Matrix (GLCM) sebagai fitur untuk proses klasifikasi. Hasil visualisasi diproses lanjut untuk ekstrasi fitur menggunakan GLCM dengan 12 fitur dan kemudian GLCM dengan 4 fitur. Untuk proses klasifikasi digunakan SVM dan KNN, sedangkan pengukuran performa menggunakan akurasi. Jumlah data hemoragik dan iskemik adalah 45 citra yang dibagi menjadi 2 bagian, 28 citra untuk pengujian dan 17 citra untuk pelatihan. Hasil akhir menunjukkan akurasi tertinggi yang dicapai menggunakan SVM adalah 82% dan KNN adalah 88%

Automated quantification of stroke damage on brain computed tomography scans: e-ASPECTS

Emergency radiological diagnosis of acute ischaemic stroke requires the accurate detection and appropriate interpretation of relevant imaging findings. Non-contrast computed tomography (CT) provides fast and low-cost assessment of the early signs of ischaemia and is the most widely used diagnostic modality for acute stroke. The Alberta Stroke Program Early CT Score (ASPECTS) is a quantitative and clinically validated method to measure the extent of ischaemic signs on brain CT scans. The CE-marked electronic-ASPECTS (e-ASPECTS) software automates the ASPECTS score. Anglia Ruskin Clinical Trials Unit (ARCTU) independently carried out a clinical investigation of the e-ASPECTS software, an automated scoring system which can be integrated into the diagnostic pathway of an acute ischaemic stroke patient, thereby assisting the physician with expert interpretation of the brain CT scan. Here we describe a literature review of the clinical importance of reliable assessment of early ischaemic signs on plain CT scans, and of technologies automating these processed scoring systems in ischaemic stroke on CT scans focusing on the e-ASPECTS software. To be suitable for critical appraisal in this evaluation, the published studies needed a sample size of a minimum of 10 cases. All randomised studies were screened and data deemed relevant to demonstration of performance of ASPECTS were appraised. The literature review focused on three domains: i) interpretation of brain CT scans of stroke patients, ii) the application of the ASPECTS score in ischaemic stroke, and iii) automation of brain CT analysis. Finally, the appraised references are discussed in the context of the clinical impact of e-ASPECTS and the expected performance, which will be independently evaluated by a non-inferiority study conducted by the ARCTU

CNN and Rf based Early Detection of Brain Stroke Using Bio-Electrical Signals

The brain is a vital component of the body that is in control of involuntary and voluntary movements such as walking,   memory, and vision. Nowadays, some of the most prevalent brain disorders include Alzheimer's disease, brain tumors, and epilepsy (paralysis or stroke).  As a result, stroke has become a significant global health concern, with high rates of mortality and disability. Importantly, approximately two-thirds of all strokes occur in developing countries, highlighting the significant burden of this condition in these regions. Therefore, emphasizing the timely detection and appropriate treatment of brain tumors is crucial. Given the high potential for mortality or severe disability associated with stroke disease, prioritizing active primary prevention and early identification of prognostic symptoms is of paramount importance. Ischemic stroke and hemorrhagic stroke are the two primary classifications for stroke diseases. Each type calls for specific emergency treatments, such as the administration of thrombolytics or coagulants, tailored to their respective underlying mechanisms. However, to effectively manage stroke, it is crucial to promptly identify the precursor symptoms in real-time, as they can vary among individuals. Timely professional treatment within the appropriate treatment window is essential and should be provided by a medical institution. In contrast, prior research has primarily centered around the formulation of acute treatment strategies or clinical guidelines subsequent to the occurrence of a stroke, rather than giving sufficient attention to the early identification of prognostic symptoms. Specifically, recent research has extensively utilized image analysis techniques, such as computed tomography (CT) or magnetic resonance imaging (MRI), as a primary approach for detecting and predicting prognostic symptoms in stroke patients. Traditional methodologies not only encounter difficulties in achieving early real-time diagnosis but also exhibit limitations in terms of prolonged testing duration and high testing costs. In this study, we introduce a novel system that employs machine learning techniques to predict and semantically interpret prognostic symptoms of stroke. Our approach utilizes real-time measurement of multi-modal bio-signals, namely electrocardiogram (ECG) and photoplethysmography (PPG), with a specific focus on the elderly population. To facilitate real-time prediction of stroke disease during walking, we have developed a stroke disease prediction system that incorporates a hybrid ensemble architecture. This architecture synergistically combines Convolutional Neural Network (CNN) and Random Forest (RF) models, enabling accurate and timely prognostication of stroke disease. The suggested method prioritises the convenience of use of bio-signal sensors for the elderly by collecting bio-signals from three electrodes placed on the index finger. These signals include ECG and PPG, and they are obtained while the participants walk. The CNN-RF model delivers satisfactory prediction accuracy when using raw ECG and PPG data. F1-Score, Sensitivity, Specificity, and Accuracy were the performance parameters used to evaluated the model's performance

Automated detection of parenchymal changes of ischemic stroke in non-contrast computer tomography: a fuzzy approach

The detection of ischemic changes is a primary task in the interpretation of brain Computer Tomography (CT) of patients suffering from neurological disorders. Although CT can easily show these lesions, their interpretation may be difficult when the lesion is not easily recognizable. The gold standard for the detection of acute stroke is highly variable and depends on the experience of physicians. This research proposes a new method of automatic detection of parenchymal changes of ischemic stroke in Non-Contrast CT. The method identifies non-pathological cases (94 cases, 40 training, 54 test) based on the analysis of cerebral symmetry. Parenchymal changes in cases with abnormalities (20 cases) are detected by means of a contralateral analysis of brain regions. In order to facilitate the evaluation of abnormal regions, non-pathological tissues in Hounsfield Units were characterized using fuzzy logic techniques. Cases of non-pathological and stroke patients were used to discard/confirm abnormality with a sensitivity (TPR) of 91% and specificity (SPC) of 100%. Abnormal regions were evaluated and the presence of parenchymal changes was detected with a TPR of 96% and SPC of 100%. The presence of parenchymal changes of ischemic stroke was detected by the identification of tissues using fuzzy logic techniques. Because of abnormal regions are identified, the expert can prioritize the examination to a previously delimited region, decreasing the diagnostic time. The identification of tissues allows a better visualization of the region to be evaluated, helping to discard or confirm a stroke.Peer ReviewedPostprint (author's final draft

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

The adoption of AI in clinical practice : exploring neuroradiologists’ perceptions and perspectives

As the world population ages, the prevalence of chronic health conditions increases, and healthcare faces an ever-growing demand for services with progressively higher costs. This is particularly concerning for non-communicable diseases such as neurological disorders, which are a known burden of mortality, morbidity and disability. In neuroradiology, as in other medical fields, Artificial Intelligence (AI) has the potential to unlock cost reduction while simultaneously improving the efficacy of health services. However, despite the existence of numerous AI applications in healthcare, its adoption by healthcare institutions is still in its infancy and heavily dependent on health professionals’ acceptance and expectations towards AI. In this dissertation, the perceptions and perspectives of neuroradiologists towards the adoption of AI in clinical practice are explored. An online survey conducted collected responses from 184 neuroradiologists and showed that the use of AI is still low and that AI-specific knowledge is limited. Despite showing an overall positive attitude towards the use of AI, neuroradiologists are primarily concerned about technological malfunctions and lack of regulation. Results show a positive association between AI knowledge and a positive attitude towards its use. On the other hand, a negative association was found between AI knowledge and fear towards it. No significant relationship was found between age and AI use. Reassurance through providing explanation and validation of new technologies, suitable working conditions, and the creation of a robust legal framework are possibilities in the making to raise trust, provide encouragement, and establish AI readiness.Com o envelhecimento populacional, as doenças crónicas aumentam e a saúde enfrenta uma procura cada vez maior pelos seus serviços, com custos progressivamente mais elevados. Isto é particularmente preocupante nas doenças não infeciosas, como as doenças neurológicas, que contribuem decisivamente para a mortalidade, morbilidade e incapacidade. Na neurorradiologia, como em outras áreas médicas, a Inteligência Artificial (IA) tem o potencial de reduzir custos e melhorar a eficácia dos serviços de saúde. Contudo, apesar das inúmeras aplicações de IA, a sua adoção pelas instituições de saúde é baixa e depende fortemente da aceitação e expectativas dos profissionais de saúde. Nesta dissertação, exploramos as perceções e perspetivas dos neurorradiologistas em relação à adoção de IA na prática clínica. Um questionário online realizado a 184 neurorradiologistas mostrou que o uso de IA é baixo e que o conhecimento de IA é limitado. Apesar de haver uma atitude geral positiva em relação ao uso de IA, os neurorradiologistas estão preocupados com problemas tecnológicos e a falta de regulamentação, entre outros. Os resultados indicam uma associação positiva entre o conhecimento de IA e uma atitude positiva em relação ao seu uso. Por outro lado, foi encontrada uma associação negativa entre o conhecimento de IA e o medo de IA. Nenhuma relação significativa foi encontrada entre a idade e o uso de IA. Assegurar a explicação e validação de novas tecnologias, criar condições de trabalho adequadas e desenvolver uma estrutura legal robusta são requisitos em desenvolvimento para assegurar a diligência no uso de IA