Future Perspectives for Automated Neurodegenerative Disorders Diagnosis: Challenges and Possible Research Directions

Kumar, Attuluri Vamsi; Kumar, Sandeep; Garg, Vivek Kumar; Goel, Neelam; Hoang, Vinh Truong

doi:10.1007/978-981-99-2154-6_15

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2024

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Cited by 2 publications

References 34 publications

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AI Enhancing Digital Communication in Neurodegenerative Disease Treatment

Thapliyal,

Thapliyal

2024

Studies in Computational Intelligence

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AI Enhancing Digital Communication in Neurodegenerative Disease Treatment

Thapliyal,

Thapliyal

2024

Studies in Computational Intelligence

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Detecting Parkinson’s disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

Jovanovic,

Damaševičius,

Matic

et al. 2024

PeerJ Computer Science

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Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson’s disease. Sensor data collected from wearable gyroscopes located at the sole of the patient’s shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson’s as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen’s Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

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Future Perspectives for Automated Neurodegenerative Disorders Diagnosis: Challenges and Possible Research Directions

Cited by 2 publications

References 34 publications

AI Enhancing Digital Communication in Neurodegenerative Disease Treatment

AI Enhancing Digital Communication in Neurodegenerative Disease Treatment

Detecting Parkinson’s disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

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