Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning.

OBJECTIVES: To evaluate the long-term prognostic value of coronary CT angiography (cCTA)-derived plaque measures and clinical parameters on major adverse cardiac events (MACE) using machine learning (ML). METHODS: Datasets of 361 patients (61.9 ± 10.3 years, 65% male) with suspected coronary artery disease (CAD) who underwent cCTA were retrospectively analyzed. MACE was recorded. cCTA-derived adverse plaque features and conventional CT risk scores together with cardiovascular risk factors were provided to a ML model to predict MACE. A boosted ensemble algorithm (RUSBoost) utilizing decision trees as weak learners with repeated nested cross-validation to train and validate the model was used. Performance of the ML model was calculated using the area under the curve (AUC). RESULTS: MACE was observed in 31 patients (8.6%) after a median follow-up of 5.4 years. Discriminatory power was significantly higher for the ML model (AUC 0.96 [95%CI 0.93-0.98]) compared with conventional CT risk scores including Agatston calcium score (AUC 0.84 [95%CI 0.80-0.87]), segment involvement score (AUC 0.88 [95%CI 0.84-0.91]), and segment stenosis score (AUC 0.89 [95%CI 0.86-0.92], all p < 0.05). Similar results were shown for adverse plaque measures (AUCs 0.72-0.82, all p < 0.05) and clinical parameters including the Framingham risk score (AUCs 0.71-0.76, all p < 0.05). The ML model yielded significantly higher diagnostic performance compared with logistic regression analysis (AUC 0.96 vs. 0.92, p = 0.024). CONCLUSION: Integration of a ML model improves the long-term prediction of MACE when compared with conventional CT risk scores, adverse plaque measures, and clinical information. ML algorithms may improve the integration of patient's information to enhance risk stratification. KEY POINTS: • A machine learning (ML) model portends high discriminatory power to predict major adverse cardiac events (MACE). • ML-based risk stratification shows superior diagnostic performance for MACE prediction over coronary CT angiography (cCTA)-derived risk scores or clinical parameters alone. • A ML model outperforms conventional logistic regression analysis for the prediction of MACE.

Machine Learning and Coronary Artery Calcium Scoring.

PURPOSE OF REVIEW: To summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact. RECENT FINDINGS: Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.

Machine Learning and Deep Neural Networks Applications in Coronary Flow Assessment: The Case of Computed Tomography Fractional Flow Reserve.

Coronary computed tomography angiography (cCTA) is a reliable and clinically proven method for the evaluation of coronary artery disease. cCTA data sets can be used to derive fractional flow reserve (FFR) as CT-FFR. This method has respectable results when compared in previous trials to invasive FFR, with the aim of detecting lesion-specific ischemia. Results from previous studies have shown many benefits, including improved therapeutic guidance to efficiently justify the management of patients with suspected coronary artery disease and enhanced outcomes and reduced health care costs. More recently, a technical approach to the calculation of CT-FFR using an artificial intelligence deep machine learning (ML) algorithm has been introduced. ML algorithms provide information in a more objective, reproducible, and rational manner and with improved diagnostic accuracy in comparison to cCTA. This review gives an overview of the technical background, clinical validation, and implementation of ML applications in CT-FFR.

How the Workload and Outcome of Imaging Examinations Changed During the COVID-19 Pandemic Lockdown.

BACKGROUND: On March 9th, 2020, the Italian government decided to go into lockdown due to the COVID-19 pandemic, which led to changes in the workflow of radiological examinations. AIMS: Aim of the study is to illustrate how the workload and outcome of radiological exams changed in a community hospital during the pandemic. METHODS AND MATERIAL: The exams performed in the radiology department from March 9th to March 29th, 2020 were retrospectively reviewed and compared to the exams conducted during the same time-period in 2019. Only exams coming from the emergency department (ED) were included. Two radiologists defined the cases as positive or negative findings, based on independent blind readings of the imaging studies. Categorical measurements are presented as frequency and percentages, and p-values are calculated using the Chi-squared test. RESULTS AND CONCLUSIONS: There was a significant reduction in the amount of exams performed in 2020: there were 143 (93|65% male, 60.7±21.5 years) patients who underwent radiological examinations from the ED vs. 485 (255|53% male, 51.2±24.8 years) in 2019. Furthermore, the total number of ED exams dropped from 699 (2019) to 215 (2020). However, the percentage of patients with a positive result was significantly higher in 2020 (69|48%) compared to 2019 (151|31%) (p<.001). The reduction of emergency radiological examinations might be a result of the movement restrictions enforced during the lockdown, and possible fear of the hospital as a contagious place. This translated to a relative increase of positive cases as only patients with very serious conditions were accessing the ED.