Machine learning for prognostic prediction in coronary artery disease with SPECT data: a systematic review and meta-analysis.

Background: Single-photon emission computed tomography (SPECT) analysis relies on qualitative visual assessment or semi-quantitative measures like total perfusion deficit that play a critical role in the non-invasive diagnosis of coronary artery disease by assessing regional blood flow abnormalities. Recently, machine learning (ML) -based analysis of SPECT images for coronary artery disease diagnosis has shown promise, with its utility in predicting long-term patient outcomes (prognosis) remaining an active area of investigation. In this review, we comprehensively examine the current landscape of ML-based analysis of SPECT imaging with an emphasis on prognostication of coronary artery disease.

Main body: Our systematic search yielded twelve retrospective studies, investigating SPECT-based ML models for prognostic prediction in coronary artery disease patients, with a total sample size of 73,023 individuals. Several of these studies demonstrate the superior prognostic capabilities of ML models over traditional logistic regression (LR) models and total perfusion deficit, especially when incorporating demographic data alongside SPECT imaging. Meta-analysis of 6 studies revealed promising performance of the included ML models, with sensitivity and specificity exceeding 65% for major adverse cardiovascular events and all-cause mortality. Notably, the integration of demographic information with SPECT imaging in ML frameworks shows statistically significant improvements in prognostic performance.

Conclusion: Our review suggests that ML models either independently or in combination with demographic data enhance prognostic prediction in coronary artery disease.