machine learning in innovations in stroke identification
ABSTARCT : Cerebrovascular diseases such as stroke are among the most common causes of death and disability worldwide and are preventable and treatable.
Early detection of strokes and their rapid intervention play an important role in reducing the burden of disease and improving clinical outcomes.
In recent years, machine learning methods have attracted a lot of attention as they can be used to detect strokes.
The aim of this study is to identify reliable methods, algorithms, and features that help medical professionals make informed decisions about stroke treatment and prevention.
This involves evaluating the patient’s symptoms, medical history (including risk factors like hypertension, diabetes, etc.), and conducting a physical examination.
Symptoms commonly associated with stroke include sudden numbness or weakness in the face, arm, or leg, especially on one side of the body, sudden confusion, trouble speaking or understanding speech, sudden trouble seeing in one or both eyes, sudden trouble walking, dizziness, loss of balance, or coordination.
This is often the first-line imaging technique used in the emergency setting due to its speed and accessibility. It can detect hemorrhagic strokes (bleeding in the brain) quickly.
Machine learning models require large, high-quality datasets for training. Neuroimaging data, especially from diverse populations and different imaging modalities (e.g., CT, MRI)
Deep learning models, which are often used in neuroimaging analysis, are known for their "black box" nature, making it difficult to understand how they arrive at their conclusions.
This lack of interpretability can be a barrier in clinical settings where transparency and justification of decisions are crucial.
Models trained on specific datasets may not generalize well to new populations, different hospitals, or varied imaging protocols.
Gather large and diverse datasets of neuroimaging data, including CT and MRI scans, annotated with stroke outcomes.
Ensure data quality through standardized protocols for image acquisition and preprocessing techniques to enhance the reliability and consistency of input data.
Utilize deep learning techniques to automatically extract relevant features from neuroimages, such as lesion location, size, and intensity patterns.
Incorporate clinical data (e.g., patient demographics, medical history, symptom onset time) to improve model performance and diagnostic accuracy.
Allows remote neurologists to evaluate patients quickly via video conferencing, enabling prompt administration of clot-busting medications like tissue plasminogen activator (tPA).
Algorithms can analyze brain imaging (such as CT scans) to quickly detect signs of stroke, helping clinicians prioritize urgent cases.
Devices like mobile stroke units equipped with CT scanners can diagnose strokes on-site (e.g., at a patient's home), reducing treatment delays.
Emerging biomarkers in blood tests can indicate stroke risk or confirm a stroke has occurred, aiding in rapid diagnosis.
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