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Automated deep learning pipeline for callosal angle quantification.

Siavash Shirzadeh Barough1, Murat Bilgel2, Catalina Ventura1

  • 1Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Fluids and Barriers of the CNS
|December 27, 2025
PubMed
Summary
This summary is machine-generated.

We developed an automated deep learning framework to measure the callosal angle (CA) for diagnosing normal pressure hydrocephalus (NPH). This tool offers a reliable and reproducible alternative to manual measurements, improving early NPH detection.

Keywords:
Callosal angleDeep learningNeuroimagingNormal pressure hydrocephalus

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Normal pressure hydrocephalus (NPH) is an underdiagnosed neurodegenerative disorder.
  • Manual analysis of imaging biomarkers like callosal angle (CA) is labor-intensive and subjective.
  • Automated analysis is needed to improve NPH diagnosis.

Purpose of the Study:

  • To develop a fully automated deep learning framework for measuring the callosal angle (CA) from T1 MPRAGE MRI scans.
  • To provide a robust and reproducible method for CA measurement, aiding NPH diagnosis.

Main Methods:

  • A deep learning framework integrating BrainSignsNET for landmark detection (AC, PC) and a UNet-based network for lateral ventricle segmentation.
  • Input: 3D MRI scans preprocessed and reoriented.
  • Output: Automated measurement of the callosal angle (CA).

Main Results:

  • The framework achieved high concordance with manual CA measurements (r=0.98, p<0.001).
  • Mean absolute error (MAE) was 3.26 degrees.
  • Performance was independent of patient age, gender, and Evans Index (EI), indicating broad applicability.

Conclusions:

  • The automated CA measurement framework is reliable and reproducible, surpassing manual methods and interobserver variability.
  • This tool has significant potential for enhancing early NPH detection and diagnosis in research and clinical settings.