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Interpretation of SPECT wall motion with deep learning.

Yangmei Zhang1, Emma Bos2, Owen Clarkin3

  • 1School of Biomedical Engineering, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.

Journal of Nuclear Cardiology : Official Publication of the American Society of Nuclear Cardiology
|May 9, 2024
PubMed
Summary

Deep learning (DL) significantly improves single-photon emission computed tomography (SPECT) wall motion analysis. This AI approach offers higher accuracy than human readers and quantitative methods for SPECT interpretation.

Keywords:
Artificial intelligenceDeep learningEchocardiographyGated SPECT myocardial perfusion imagingRegional wall motion abnormality

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Single-photon emission computed tomography (SPECT) wall motion assessment faces limitations in temporal and spatial resolution.
  • Visual interpretation of SPECT wall motion is subjective and prone to errors.
  • Artificial intelligence (AI) presents a potential solution to enhance the accuracy of wall motion assessment.

Purpose of the Study:

  • To develop a novel deep learning (DL) workflow for interpreting SPECT wall motion.
  • To compare the diagnostic performance of DL with human readers and quantitative parameters.

Main Methods:

  • A total of 1038 patients undergoing rest electrocardiogram (ECG)-gated SPECT and echocardiography were included.
  • A DL model was trained using echocardiography as ground truth to predict abnormal wall motion and assess regional wall motion.
  • A 10-fold cross-validation was employed, and DL performance was compared against human readers and quantitative parameters.

Main Results:

  • The DL model demonstrated superior diagnostic performance compared to human readers and quantitative parameters, with higher area under the receiver operating characteristic curve (AUC) and accuracy (ACC).
  • DL model achieved an AUC of .82 (95% CI: .79-.85) and ACC of .88, outperforming human readers (AUC: .77; ACC: .82) and quantitative parameters (AUC: .74; ACC: .78).
  • The DL model provided results rapidly (within 30 seconds) via a user interface, enabling preliminary interpretations.

Conclusions:

  • Deep learning (DL) effectively enhances the interpretation of rest SPECT wall motion.
  • DL-based analysis surpasses the diagnostic capabilities of current human readers and quantitative parameter diagnoses for SPECT.
  • The developed DL workflow offers a more accurate and efficient method for SPECT wall motion interpretation.