A special case of hypertrophic cardiomyopathy with a differential diagnosis of isolated cardiac amyloidosis or junctophilin type 2 associated cardiomyopathy

Differential diagnosis between hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) is mandatory since the prognosis is very different, but not always possible as both diseases present with increased myocardial thickness and mass. Despite better knowledge of the pathophysiology of both HCM and CA, and new developments in diagnosis, many patients with cardiac involvement in systemic amyloidosis are still only diagnosed in an advanced stage. Improvements in non-invasive diagnostic methods such as ultrasound techniques and cardiac magnetic resonance imaging will eventually obviate the need for invasive studies in order to prove amyloid cardiomyopathy. Nevertheless, today, an endomyocardial biopsy still remains the golden standard. We present an 86-year-old man, diagnosed with hypertrophic cardiomyopathy, in whom echocardiography and cardiac magnetic resonance imaging strongly suggested amyloidosis to be the underlying cause. Interestingly, a new variant of the junctophilin 2 (JPH2) gene, related to hypertrophic cardiomyopathies, was found in our patient.

Artificial Intelligence Will Transform Cardiac Imaging-Opportunities and Challenges

Artificial intelligence (AI) using machine learning techniques will change healthcare as we know it. While healthcare AI applications are currently trailing behind popular AI applications, such as personalized web-based advertising, the pace of research and deployment is picking up and about to become disruptive. Overcoming challenges such as patient and public support, transparency over the legal basis for healthcare data use, privacy preservation, technical challenges related to accessing large-scale data from healthcare systems not designed for Big Data analysis, and deployment of AI in routine clinical practice will be crucial. Cardiac imaging and imaging of other body parts is likely to be at the frontier for the development of applications as pattern recognition and machine learning are a significant strength of AI with practical links to image processing. Many opportunities in cardiac imaging exist where AI will impact patients, medical staff, hospitals, commissioners and thus, the entire healthcare system. This perspective article will outline our vision for AI in cardiac imaging with examples of potential applications, challenges and some lessons learnt in recent years.