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Diastolic dysfunction is condition of a stiff ventricle and a function of aging. It causes significant cardiovascular mortality and morbidity, and in fact, three million Americans are currently suffering from this condition. To date, all the pharmacological clinical trials have been negative. The lack of success in attenuating/ameliorating diastolic dysfunction stems from lack of duplication of myriads of clinical manifestation in pre-clinical settings. Here we report, a novel genetically engineered mice which may represents a preclinical model of human diastolic dysfunction to some extent. Topoisomerase 2 beta (Top2b) is an important enzyme in transcriptional activation of some inducible genes through transient double-stranded DNA breakage events around promoter regions. We created a conditional, tissue-specific, inducible Top2b knockout mice in the heart. Serendipitously, echocardiographic parameters and more invasive analysis of left ventricular function with pressure–volume loops show features of diastolic dysfunction. This was also confirmed histologically. At the cellular level, the Top2b knockdown showed morphological changes and molecular signaling akin to human diastolic dysfunction. Reverse phase protein analysis showed activation of p53 and inhibition of, Akt, as the possible mediators of diastolic dysfunction. Finally, activation of p53 and inhibition of Akt were confirmed in myocardial biopsy samples obtained from human diastolic dysfunctional hearts. Thus, we report for the first time, a Top2b downregulated preclinical mice model for diastolic dysfunction which demonstrates that Akt and p53 are the possible mediators of the pathology, hence representing novel and viable targets for future therapeutic interventions in diastolic dysfunction.

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.