According to WHO’s latest reports, with approximately 17.9 million deaths every year, cardiovascular diseases (CVDs) are the leading cause of death in adults. This disease group affects the heart and blood vessels and causes coronary heart disease, cerebrovascular disease, and rheumatic heart disease. Transformative AI technologies have been applied in cardiovascular in many areas, such as disease prediction, cardiac imaging analysis, personalized medicine, new drug targets, and intelligent robots.

Treatments in cardiology have been significantly impacted by Artificial Intelligence and Big Data. It has helped find appropriate treatment and development of new personalized medicine, unlike the “one size fits all” drugs identified from broad clinical trials. One example is how the AI technology of Corti.ai is saving many lives from heart attacks. To predict these cardiac arrest cases in real-time, the company integrates sophisticated voice and pattern recognition technology. This is possible because AI searches for cardiac arrest signs, including verbal and non-verbal evidence, such as tone of voice and breathing patterns, based on data from millions of previous calls. While technology like Alexa is built for user prompts and short sentences, the Corti.ai platform offers a solution that can work even in a challenging acoustic environment.

Cardiotrack is a startup that offers AI-enabled cardiac care solutions that aim to reduce the cost of diagnosis significantly. It provides quality diagnostics at primary health centers through 3 platforms. It consists of Healthcare IoT System, a device that collects data from all the 12-lead MHealth Software, enabling the recorded ECG to be seen by a patient, and Cloud Services that deal with data storage and AI analytics.

Carmat aims to create revolutionary artificial organs, and this mission has started with an artificial heart. Centered on AI and cloud computing technology, VoxelCloud offers automated medical image processing services and diagnostic assistance. To identify and diagnose many diseases, VoxelCloud creates computer-aided detection systems that can also handle cardiovascular diseases along with other ailments. Their technologies are focused on cutting-edge computer vision, deep learning, and technology for artificial intelligence. Cardiologs offers a web solution to streamline the process on a wide scale by implementing the latest Artificial Intelligence research, allowing the highest diagnostic yield for the least physician effort.

Mayo Clinic is collaborating with a young U.K startup- Ultromics– to gain insights on Covid’s effect on the heart using AI tech and automated echocardiogram assessment and quantification. This study will look at echocardiograms (cardiac ultrasounds) of 500 Covid-19 positive men and women between 18 and 89 retrospectively to examine the images for patterns and details of patients who have had a clinically indicated cardiac ultrasound in three months. Automated cardiac measurements, ejection fraction, and Global Longitudinal Strain will be the primary goal.

Caption Health enables non-specialists to perform cardiac ultrasounds using AI and machine learning technologies. This has increased access to safe and effective cardiac diagnostics that can be life-saving for patients. It makes the cardiac diagnosis easy as the software is compatible with different ultrasound machines and easy to learn. CathVision ApS is a startup that is developing a novel AI therapeutic platform for cardiac electrophysiology procedures. It enables cardiologists to succeed in managing several different cardiac arrhythmias. Tricog is a healthcare analytics firm that uses Artificial Intelligence and Machine Learning to help screen and diagnose acute and chronic heart diseases. In over 2,500 hospitals, clinics, and diagnostic centers, Tricog’s Insta ECG program has been deployed to help identify and treat patients with severe cardiac diseases, including heart attacks.

Future

AI tech is significantly influencing the Clinical Decision Support System, which helps the cardiologists in a significant way. Image-based diagnosis and algorithms/models of Machine learning (ML) has improved the chances of detecting complex cardiac diseases such as coronary artery disease (CAD) and heart failure. Using the superior performance of AI tech, faster and more precise diagnostic decision making can help deliver better cardiac care.

Despite the promising initial outcome, the clinical application of such approaches in real-world cardiovascular diseases is elusive due to many factors such as lack of standardization of technical parameters. The availability of only a small data set to train ML classifiers is also a significant issue as this would lead to overfitting and low generalizability of the classification models when applied to a different population.

There has been a radical shift from the traditional” cardiac treatment methods to the techniques in AI that deals with large amounts of data. The main difference is that earlier studies in cardiology depended on statistical methods while Ai techniques apply machine learning methods to identify patterns from the clinical data/medical images and perform prediction. In the future, ML algorithms combined with data from clinical parameters, genetics, protein metabolism, phenomapping of cardiovascular diseases, and imaging data from echocardiograms and MRI/CT can develop a personalized treatment protocol for each patient.