Medical imaging AI and the digitalisation of cardiology

Digital_cardiology_blog

Thanks to enhanced computing power, ongoing demands on the workforce and a proliferation of available digital data, there has been an explosion in the adoption of AI-based technologies in recent years. This widespread adoption of AI has also found its way to the healthcare sector. Here, there is already a wealth of big data available in electronic patient records. AI opens the door to new opportunities for data mining and predictive modelling. 

AI has the ability to mimic human intelligence, in turn reducing the burden on clinicians. It can also process the huge volume of data; something that would be otherwise beyond our capacity. 

Like many other specialities in the field of medicine, cardiology is becoming increasingly digitalised. As a result, it is also enjoying many of the benefits that AI can bring to the discipline.  

Developments in AI can help cardiologists in numerous ways, including:  

  • Supporting diagnosis and treatment planning  
  • Organising data into usable frameworks to facilitate wide-scale collaboration 
  • Identifying patterns in image analysis  
  • Assisting with making reliable predictions based on computer algorithms.  

By providing cardiologists with computer-aided diagnostic tools, clinical decision support, quantitative analysis tools and computer-aided detection algorithms, AI can not only relieve the burden on clinicians on a day-to-day basis, but can also significantly improve accuracy.  

Sophisticated AI algorithms are now being used extensively to leverage current diagnostic modalities and support clinicians in making diagnoses. Consequently, AI can provide cardiologists with valuable, evidence-based second opinions to support their professional diagnoses. It can also capture information from existing data sources that the human eye may otherwise be unable to detect. In other words, by using AI, data from a CT, EKG, MRI or other imaging software can be extracted faster than ever before. What’s more, the required input from humans has never been lower.

The emerging field of radiomics 

Radiomics uses AI to analyse information from large data sets. It uses data characterisation algorithms to uncover novel disease characteristics (or those that the human eye cannot detect owing to resolution limitations).  

AI and deep learning techniques can support – and enhance – a variety of processes related to cardiac imaging including segmentation, identification, classification of images, lesion detection and classification of tissues from histological images. 

Within cardiology departments, this can make significant differences to workflow, diagnostics, imaging analysis and ultimately, patient outcomes by guiding treatment and therapy in the most appropriate way.  

Recent studies have revealed that in some cases, machine-learning models even perform better than humans when identifying conditions. These include long QT syndrome and atrial fibrillation, calculating coronary calcium, and estimating physiologic age from an ECG. Furthermore, using data from CT scans, AI models can predict future risk of cardiovascular disease with greater accuracy than existing predictive models. 

Advanced AI functionality is already being integrated into dashboards to support cardiologists in some settings. However, full approval and adoption on a broader scale is yet to occur.

The amount of data being generated in the field of cardiovascular medicine is in danger of overwhelming us entirely. This could make it almost impossible to keep abreast of the latest developments in the field. However, deep learning algorithms can help us to access this wealth of clinical data, maximise performance using computer-aided diagnostics, imaging and modelling tools and ultimately, improve experience and outcomes for both clinicians and patients.