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Related Experiment Video

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Design and optimization of an automatic deep learning-based cerebral reperfusion scoring (TICI) using thrombus

Arthur Folcher1, Jérémy Piters1, Daphné Wallach2

  • 1Neuroradiology Department, Brest University Hospital, Brest, France.

Journal of Neuroradiology = Journal De Neuroradiologie
|June 28, 2025
PubMed
Summary

An AI model accurately classifies mechanical thrombectomy outcomes into two Thrombolysis in Cerebral Infarction (TICI) groups. However, its performance in distinguishing three TICI grades remains insufficient for clinical use.

Keywords:
Artificial intelligenceDeep learningStrokeTICIThrombectomyThrombus

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • The Thrombolysis in Cerebral Infarction (TICI) scale is crucial for assessing mechanical thrombectomy outcomes.
  • Significant variability exists in TICI scoring, necessitating improved assessment methods.

Purpose of the Study:

  • To develop and optimize an artificial intelligence (AI)-based classification model for digital subtraction angiography (DSA) TICI scoring.
  • To evaluate the AI model's performance in classifying TICI scores into two and three distinct groups.

Main Methods:

  • A convolutional neural network (CNN) was trained on a monocentric DSA dataset of thrombectomies.
  • The model classified TICI scores into two groups (TICI 0-2a vs. 2b-3) and three groups (TICI 0-2a vs. 2b vs. 2c-3).
  • Thrombus positions were introduced manually and via an automated detection module to assess their impact on performance.

Main Results:

  • The AI model achieved high specificity (0.97 ± 0.01) and sensitivity (0.86 ± 0.01) for the two-class TICI classification.
  • The three-class classification model demonstrated insufficient performance, with F1 scores for TICI 2b around 0.50-0.55.
  • Automatic thrombus detection did not significantly improve the performance of the three-class model.

Conclusions:

  • The AI model offers a reproducible method for two-class TICI scoring in DSA.
  • The current AI model's performance is inadequate for distinguishing three TICI grades in clinical practice.
  • Automated thrombus detection did not enhance the AI model's accuracy for multi-class TICI assessment.