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Deep learning-based automatic ASPECTS calculation can improve diagnosis efficiency in patients with acute ischemic

Jianyong Wei1,2, Kai Shang3, Xiaoer Wei3

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, China.

European Radiology
|July 26, 2024
PubMed
Summary

A new deep learning system accurately and rapidly assesses the Alberta Stroke Program Early CT Score (ASPECTS) for acute ischemic stroke. This automated tool significantly reduces diagnosis time and improves workflow efficiency for clinicians.

Keywords:
Acute ischemic strokeArtificial intelligenceConvolutional neural networkDiagnostic efficiencyNon-contrast computed tomography

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Area of Science:

  • Neurology
  • Radiology
  • Artificial Intelligence

Background:

  • The Alberta Stroke Program Early CT Score (ASPECTS) is crucial for evaluating ischemic changes in acute ischemic stroke using non-contrast computed tomography (NCCT).
  • Current ASPECTS interpretation relies heavily on expert experience, leading to inter-reader variability and potential delays in treatment.
  • There is a need for a standardized, objective, and efficient method for ASPECTS assessment in clinical practice.

Purpose of the Study:

  • To develop and validate a deep learning (DL) based system for automated ASPECTS assessment in acute ischemic stroke.
  • To evaluate the clinical applicability and efficiency of the automated DL system compared to expert interpretations.

Main Methods:

  • A DL system was trained and validated on 1987 NCCT scans from four centers.
  • The system's performance was assessed against physician consensus (reference standard) using metrics like AUC and ICC.
  • Prospective validation was conducted on 13,399 patients in real-world clinical settings.

Main Results:

  • The DL system achieved an AUC of 84.97% and an ICC of 0.84 on the test cohort.
  • Diagnostic sensitivity was 94.61% for ASPECTS ≥ 6, with substantial agreement (ICC=0.65) with experts.
  • The system reduced diagnosis time by 74.8% (from 130.6s to 33.3s) and was utilized in 94.0% of prospective cases, with 96% of physicians noting improved efficiency.

Conclusions:

  • The developed DL-based system accurately and rapidly determines ASPECTS, potentially streamlining clinical workflows for early stroke intervention.
  • The automated system demonstrates non-inferiority to expert ratings, improving evaluation consistency and significantly reducing processing time.
  • While effective, physician validation remains essential for clinical application of the automated ASPECTS system.