Using a 6-OHDA rat model, the study correlates histology with transcranial sonography (TCS) features and applies cascaded super-resolution (WDSR + interpolation) and a ResNet18 classifier to improve TCS image quality (PSNR=30.67, SSIM=0.94) and stage early PD with 89% accuracy.

By creating a pathology-anchored, AI-enhanced noninvasive imaging biomarker for early PD staging, this approach could improve patient stratification and trial enrollment—boosting translational and diagnostic value even though it does not directly probe molecular therapeutic mechanisms.

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.