Transcranial Ultrasonographic Image Analysis System for Decision Support in Parkinson Disease

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Abstract

Objectives

Transcranial ultrasonography (US) is a relatively new neuroimaging modality proposed for early diagnostics of Parkinson disease (PD). The main limitation of transcranial US image-based diagnostics is a high degree of subjectivity caused by low quality of the transcranial images. The article presents a developed image analysis system and evaluates the potential of automated image analysis on transcranial US.

Methods

The system consists of algorithms for the segmentation and assessment of informative brain regions (midbrain and substantia nigra) and a decision support subsystem, which is equipped with 64 classification algorithms. Transcranial US images of 191 participants (118 patients with a clinical PD diagnosis and 73 healthy control participants) were analyzed.

Results

The diagnostic sensitivity and specificity achieved by the proposed system were 85% and 75%, respectively.

Conclusions

Digital transcranial US image analysis is challenging, and the application of a such system as the sole instrument for decisions in clinical practice remains inconclusive. However, the proposed system could be used as a supplementary tool for automated assessment of US parameters for decision support in PD diagnostics and to reduce observer variability.

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