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.

Prevalence and perceptions of e-cigarette use among medical students in a Saudi University.

Background And Aims: We hypothesized that the prevalence of electronic cigarette vaping among the medical student population is on the rise. Our aims were to assess the prevalence of electronic cigarette vaping among medical students in Saudi Arabia, to understand and analyze the reasons that led them to try it, and to investigate students' perceptions towards electronic cigarette vaping.

Methods: An anonymous, paper-based, cross-sectional questionnaire was distributed amongst 401 undergraduate medical students from years 1-5 at Alfaisal University in Riyadh, Saudi Arabia.