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Related Experiment Video

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Evaluating Deep Learning-Based Commercial Software for Detecting Ischemic Lesions on DWI in Stroke Patients.

Ceren Alis1, Elvin Ay2, Gencer Genc1

  • 1Department of Neurology, Sisli Hamidiye Etfal Training and Research Hospital, 34396 Istanbul, Turkey.

Diagnostics (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study found that artificial intelligence (AI) software for detecting ischemic lesions on diffusion-weighted imaging (DWI) has high patient-level sensitivity but struggles with smaller lesions. Clinicians should be aware of these AI limitations in acute stroke care.

Keywords:
artificial intelligencedeep learningdiffusion-weighted imagingischemic strokelesion detectionmagnetic resonance imaging

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Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Deep learning advancements enable automated ischemic lesion detection software.
  • Real-world performance of AI for diffusion-weighted imaging (DWI) needs further exploration.
  • CE-marked AI software (version 1.0) for ischemic lesion detection was evaluated.

Purpose of the Study:

  • Evaluate the diagnostic performance of a commercial AI software for detecting ischemic lesions on DWI.
  • Assess AI software sensitivity in relation to lesion characteristics.
  • Determine AI software's lesion and patient-level sensitivity.

Main Methods:

  • Retrospective analysis of 235 patients with confirmed ischemic stroke who underwent DWI.
  • Comparison of AI software performance against expert neurologist interpretations (reference standard).
  • Analysis of lesion characteristics: size, ADC values, slice coverage, and location.

Main Results:

  • The AI software achieved 83.51% lesion-level and 95.31% patient-level sensitivity.
  • Undetected lesions were smaller, covered fewer slices, and had higher ADC values.
  • Detection rates were not significantly affected by anatomical location, vascular territory, or time from symptom onset.

Conclusions:

  • The AI software demonstrates strong overall patient-level sensitivity for ischemic stroke detection.
  • Limitations exist in detecting smaller, less conspicuous ischemic lesions.
  • Optimization of deep learning algorithms and clinician awareness of AI limitations are crucial for acute stroke care.