April 21, 2021Open Access

Clinical Application of Machine Learning-Based Artificial Intelligence in the Diagnosis, Prediction, and Classification of Cardiovascular Diseases

Q: What is the clinical evidence from this study?

Study design: Review. Population: Cardiovascular Diseases. Intervention: Machine Learning-Based Artificial Intelligence vs. Traditional clinical methods and prediction models.

Key Result

Machine learning-based artificial intelligence demonstrates strong potential to improve cardiovascular care by automating diagnostic image interpretation, enhancing prediction models, and refining disease classification.

Structured PICO

Population

A comprehensive review summarizing the applications of machine learning-based artificial intelligence in the diagnosis, prediction, and classification of cardiovascular diseases.

Intervention

Machine learning-based artificial intelligence

Machine learning and artificial intelligence offer promising applications in the diagnosis, prediction, and classification of cardiovascular diseases, though challenges remain in clinical implementation.

Limitations

Data availability limits the generalizability of machine learning algorithms to different populations.
Obtaining large quantities of high-quality labeled data for supervised learning is labor intensive.
The 'black box' property of deep learning models makes their inner mechanisms difficult to explain and limits clinical acceptance.

Abstract

With the rapid development of artificial intelligence (AI) and machine learning (ML), as well as the arrival of the big data era, technological innovations have occurred in the field of cardiovascular medicine. First, the diagnosis of cardiovascular diseases (CVDs) is highly dependent on assistive examinations, the interpretation of which is time consuming and often limited by the knowledge level and clinical experience of doctors; however, AI could be used to automatically interpret the images obtained in auxiliary examinations. Second, some of the predictions of the incidence and prognosis of CVDs are limited in clinical practice by the use of traditional prediction models, but there may be occasions when AI-based prediction models perform well by using ML algorithms. Third, AI has been used to assist precise classification of CVDs by integrating a variety of medical data from patients, which helps better characterize the subgroups of heterogeneous diseases. To help clinicians better understand the applications of AI in CVDs, this review summarizes studies relating to AI-based diagnosis, prediction, and classification of CVDs. Finally, we discuss the challenges of applying AI to cardiovascular medicine.

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