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<p>Delayed diagnosis of Parkinson’s disease (PD) due to undetectable early pathological changes remains a major clinical challenge limiting effective treatment. This study presents a novel diagnostic approach that integrates non-invasive imaging techniques with deep learning, facilitating accurate early diagnosis and staging of PD. A rat model of PD induced by 6-hydroxydopamine (6-OHDA) was established. Neuronal damage was quantitatively assessed through histological examination, while transcranial sonography (TCS) was employed to capture and analyze brain region images. This approach enabled the establishment of a quantitative relationship between TCS-derived imaging features and the extent of pathological injury. A deep learning framework based on TCS images was developed, integrating cascaded super-resolution reconstruction techniques (Wide Activation Super-Resolution Network (WDSR) with traditional interpolation methods) to enhance TCS image quality (PSNR = 30.67, SSIM = 0.94). Furthermore, the ResNet18 model was incorporated for disease staging of PD with 89% diagnostic accuracy. This advancement holds promise for enhancing early intervention and precision medicine strategies in PD management.</p>

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Pathology-Anchored Transcranial Sonography: A Cascaded Super-Resolution Deep Learning System for Early-Stage Parkinson’s Disease Grading

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月11日

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  • Yishen Zhao,
  • Weiguo Cui,
  • Shuang Liang,
  • Yu Gu,
  • Xue Bai,
  • Lu Zhang

摘要

Delayed diagnosis of Parkinson’s disease (PD) due to undetectable early pathological changes remains a major clinical challenge limiting effective treatment. This study presents a novel diagnostic approach that integrates non-invasive imaging techniques with deep learning, facilitating accurate early diagnosis and staging of PD. A rat model of PD induced by 6-hydroxydopamine (6-OHDA) was established. Neuronal damage was quantitatively assessed through histological examination, while transcranial sonography (TCS) was employed to capture and analyze brain region images. This approach enabled the establishment of a quantitative relationship between TCS-derived imaging features and the extent of pathological injury. A deep learning framework based on TCS images was developed, integrating cascaded super-resolution reconstruction techniques (Wide Activation Super-Resolution Network (WDSR) with traditional interpolation methods) to enhance TCS image quality (PSNR = 30.67, SSIM = 0.94). Furthermore, the ResNet18 model was incorporated for disease staging of PD with 89% diagnostic accuracy. This advancement holds promise for enhancing early intervention and precision medicine strategies in PD management.