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Radiomics-Based AI Model to Assist Clinicians in Intracranial Hemorrhage Diagnosis: External Validation Study.

Salita Angkurawaranon1,2, Natipat Jitmahawong1, Kittisak Unsrisong1

  • 1Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.

JMIR Formative Research
|December 11, 2025
PubMed
Summary

An artificial intelligence (AI) model using radiomics aids in identifying causes of spontaneous intracerebral hemorrhage (ICH). While its standalone accuracy decreased on external data, AI assistance significantly improved diagnostic performance for all medical professionals.

Keywords:
AIAI-assisted decision-makingartificial intelligencebrain CT scansbrain computed tomography scansexternal validationradiomicsspontaneous intracranial hemorrhages

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Early identification of intracerebral hemorrhage (ICH) etiology is crucial for treatment planning.
  • A radiomics-based artificial intelligence (AI) model for classifying ICH causes from CT scans was previously developed.

Purpose of the Study:

  • To externally validate a radiomics-based AI model for classifying spontaneous ICH.
  • To assess the AI model's utility in improving diagnostic performance of clinicians.

Main Methods:

  • External validation of an AI model using 69 CT scans from a separate cohort.
  • Classification of nontraumatic ICHs into primary, tumorous, and vascular malformation-related causes.
  • Assessment of diagnostic performance (accuracy, sensitivity, specificity, PPV) of clinicians before and after AI model assistance.

Main Results:

  • The AI model achieved an accuracy of 0.65 in classifying ICH causes.
  • AI model assistance improved diagnostic accuracy across all reader groups (nonradiologists, radiologists, trainees, specialists).
  • Accuracy gains ranged from 0.04 to 0.07, with specialists showing the highest accuracy with AI support.

Conclusions:

  • The radiomics-based AI model's accuracy decreased on an external dataset.
  • AI model assistance significantly enhanced diagnostic performance for all clinician groups, improving consistency.