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<p>Parkinson's Disease (PD) is a condition which reduces greatly the quality of life for patients. PD receives its diagnosis primarily through the observation of dopaminergic neuron damage within the substantia nigra that appears as motor conditions including tremors, postural instability and additional non motor manifestations like cognitive deterioration, depression and sleep problems. It is a major global public health issue as it generates substantial health expenses together with costs from long-term care and workforce reduction. A successful timely diagnosis of Parkinson's disease allows healthcare providers to deliver proper treatment that enhances patient results. Traditional diagnostic methods depend on clinical histories combined with clinical assessments yet this approach leads to misdiagnosis and delayed diagnosis mainly because symptoms are underdeveloped early in disease progression. The critical requirement for advanced diagnostic systems became vital for precise PD detection along with quicker diagnosis timing while providing necessary intervention guidance. This paper evaluates cutting-edge deep learning technologies developed from MRI data that enhance PD diagnosis precision, alongside their predictive role in transforming clinical patient care. The study aims to advance PD identification methods by enhancing the existing diagnostic techniques through its approach and try to overcome major challenges.</p>

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MRI-Driven Deep Learning in Parkinson’s Disease Diagnosis: Innovations, Challenges, and Future Directions

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Gaurav Dwivedi,
  • Rahul Vyas,
  • Anurag Tripathi,
  • Lokendra Kumar Tiwari

摘要

Parkinson's Disease (PD) is a condition which reduces greatly the quality of life for patients. PD receives its diagnosis primarily through the observation of dopaminergic neuron damage within the substantia nigra that appears as motor conditions including tremors, postural instability and additional non motor manifestations like cognitive deterioration, depression and sleep problems. It is a major global public health issue as it generates substantial health expenses together with costs from long-term care and workforce reduction. A successful timely diagnosis of Parkinson's disease allows healthcare providers to deliver proper treatment that enhances patient results. Traditional diagnostic methods depend on clinical histories combined with clinical assessments yet this approach leads to misdiagnosis and delayed diagnosis mainly because symptoms are underdeveloped early in disease progression. The critical requirement for advanced diagnostic systems became vital for precise PD detection along with quicker diagnosis timing while providing necessary intervention guidance. This paper evaluates cutting-edge deep learning technologies developed from MRI data that enhance PD diagnosis precision, alongside their predictive role in transforming clinical patient care. The study aims to advance PD identification methods by enhancing the existing diagnostic techniques through its approach and try to overcome major challenges.