Machine learning based model to diagnose obstructive coronary artery disease using calcium scoring, PET imaging, and clinical data

Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends

Myocardial perfusion imaging (MPI), using either single photon emission computed tomography (SPECT) or positron emission tomography (PET), is one of the most commonly ordered cardiac imaging tests, with prominent clinical roles for disease diagnosis and risk prediction. Artificial intelligence (AI) could potentially play a role in many steps along the typical MPI workflow, from image acquisition through to clinical reporting and risk estimation. AI can be utilized to improve image quality, reducing radiation exposure and image acquisition times. Once images are acquired, AI can help optimize motion correction and image registration during image reconstruction or provide direct image attenuation correction. Utilizing these image sets, AI can segment a number of anatomic features from associated computed tomographic imaging or even generate synthetic attenuation imaging. Lastly, AI may play an important role in disease diagnosis or risk prediction by combining the large number of potentially important clinical, stress, and imaging-related variables. This review will focus on the most recent developments in the field, providing clinicians and researchers with a timely update on the field. Additionally, it will discuss future trends including applications of AI during multiple points of the typical MPI workflow to maximize clinical utility and methods to maximize the information that can be obtained from hybrid imaging.

Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review

Nuclear medicine and molecular imaging plays a significant role in the detection and management of cardiovascular disease (CVD). With recent advancements in computer power and the availability of digital archives, artificial intelligence (AI) is rapidly gaining traction in the field of medical imaging, including nuclear medicine and molecular imaging. However, the complex and time-consuming workflow and interpretation involved in nuclear medicine and molecular imaging, limit their extensive utilization in clinical practice. To address this challenge, AI has emerged as a fundamental tool for enhancing the role of nuclear medicine and molecular imaging. It has shown promising applications in various crucial aspects of nuclear cardiology, such as optimizing imaging protocols, facilitating data processing, aiding in CVD diagnosis, risk classification and prognosis. In this review paper, we will introduce the key concepts of AI and provide an overview of its current progress in the field of nuclear cardiology. In addition, we will discuss future perspectives for AI in this domain.

Basing on the machine learning model to analyse the coronary calcification score and the coronary flow reserve score to evaluate the degree of coronary artery stenosis

AIM

To obtain the coronary artery calcium score (CACS) for each branch in coronary artery computed tomography angiography (CCTA) examination combined with the flow fraction reserve (FFR) of each branch in the coronary artery detected by CT and apply a machine learning model (ML) to analyse and predict the severity of coronary artery stenosis.

METHODS

All patients who underwent coronary computed tomography angiography (CCTA) from January 2019 to April 2022 in the HOSPITAL (T.C.M) AFFILIATED TO SOUTHWEST MEDICAL UNIVERSITY) were retrospectively screened, and their sex, age, characteristics of lipid-containing lesions, coronary calcium score (CACS) and CT-FFR values were collected. Five machine learning models, random forest (RF), k-nearest neighbour algorithm (KNN), kernel logistic regression, support vector machine (SVM) and radial basis function neural network (RBFNN), were used as predictive models to evaluate the severity of coronary stenosis.

RESULTS

Among the five machine learning models, the SVM model achieved the best prediction performance, and the prediction accuracy of mild stenosis was up to 90%. Second, age and male sex were important influencing factors of increasing CACS and decreasing CT-FFR. Moreover, the critical CACS value of myocardial ischemia >200.70 was calculated.

CONCLUSION

Through computer machine learning model analysis, we prove the importance of CACS and FFR in predicting coronary stenosis, especially the prominent vector machine model, which promotes the application of artificial intelligence computer learning methods in the field of medical analysis.