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A HOG feature extraction and CNN approach to Parkinson’s spiral drawing diagnosis

Tripathi et al. | Aug 09, 2024

A HOG feature extraction and CNN approach to Parkinson’s spiral drawing diagnosis

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.

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Diagnosing hypertrophic cardiomyopathy using machine learning models on CMRs and EKGs of the heart

Kolluri et al. | Jul 29, 2024

Diagnosing hypertrophic cardiomyopathy using machine learning models on CMRs and EKGs of the heart
Image credit: Jesse Orrico

Here seeking to develop a method to diagnose, hypertrophic cardiomyopathy which can cause sudden cardiac death, the authors investigated the use of a convolutional neural network (CNN) and long short-term memory (LSTM) models to classify cardiac magnetic resonance and heart electrocardiogram scans. They found that the CNN model had a higher accuracy and precision and better other qualities, suggesting that machine learning models could be valuable tools to assist physicians in the diagnosis of hypertrophic cardiomyopathy.

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