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Building AI-Ready Datasets for Dural-Based Pathologies: A Systematic Approach to Data Curation, Annotation

Shweta Kedia1, Harsh Deora2, Sarvesh Goyal1

  • 1Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India.

Neurology India
|January 9, 2026
PubMed
Summary
This summary is machine-generated.

Creating high-quality datasets for Artificial Intelligence (AI) in neurosurgery is challenging due to data variability and privacy concerns. This study proposes solutions to build an AI-ready dataset for dural-based lesions, improving diagnostic accuracy.

Keywords:
Annotationartificial intelligencedataset curationdural-based pathologiesfederated learningmeningiomaneurosurgery

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Area of Science:

  • Neurosurgery
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Artificial Intelligence (AI) integration in neurosurgery promises enhanced diagnostics and surgical precision.
  • High-quality, standardized datasets are crucial for AI development but are scarce, especially in under-resourced settings.

Purpose of the Study:

  • Identify key challenges in creating AI-ready datasets for dural-based lesions.
  • Propose practical solutions to overcome these barriers in dataset development.

Main Methods:

  • Utilized histopathology slides from 122 patients across multiple institutions over 1 year.
  • Data underwent anonymization, curation, and annotation by a multidisciplinary team.
  • Implemented standardized imaging protocols, AI-assisted annotation, automated quality control, and federated learning.

Main Results:

  • Encountered challenges: imaging variability, data gaps, manual annotation labor, and interobserver inconsistencies.
  • Addressed data security and privacy concerns through secure transfer protocols and deidentification.
  • Developed solutions including standardized protocols, AI-assisted annotation, automated QC, and federated learning.

Conclusions:

  • Established a structured, high-quality dataset for AI applications in neurosurgery.
  • This dataset will facilitate robust AI model development for improved diagnostic and therapeutic decision-making.
  • Contributes to advancing AI-driven healthcare solutions in neurosurgery, particularly in India.