The heart must go on: The spectrum of cardiological diseases ranges from arrhythmias to valve defects to heart attacks. However, the diagnostic means and the forms of therapy with intervention, surgery and medication are just as diverse. Read more about this affair of the heart here.
A simple blood test that can very accurately predict the chance of survival with good recovery could be of great significance for patients in intensive care after a cardiac arrest.
A new study shows that complications after stent surgery are more common in individuals with diabetes. The results emphasise the importance of tailoring treatment strategies for this patient group.
Pacemakers have a range of different functions, not all of which are needed for every patient – switching off unnecessary features could help extend the battery life of the devices by several years.
In emergency settings, AI to analyze ECG data can improve detection of severe heart attacks, including those with unconventional symptoms, or atypical ECG patterns, and reduced false positives.
A new approach to PET imaging offers a promising way for physicians to promptly identify patients who are at risk for poor functional recovery after a heart attack, according to new research.
Cardiac imaging is evolving, and new techniques continue to uncover the secrets of the heart for cardiologists who know how to use them. At the ESC 2025 Congress in Madrid, four experts explored cutting-edge developments across different modalities. Ranging from AI-assisted ultrasound image acquisition and accelerated MRI protocols to advanced prognostic tools for CT and nuclear imaging, these…
Dizziness upon standing up or during exertion: Postural orthostatic tachycardia syndrome (POTS) affects almost a third of patients with severe long Covid, a new study finds.
A new study demonstrates drones can deliver life-saving AEDs in just minutes from an emergency call – potentially doubling survival rates in out-of-hospital cardiac arrests.
Systematic screening and support for mental health conditions such as depression, anxiety, or PTSD should become normalised in cardiovascular care, cardiologists urge in a new Consensus Statement.
Traditionally, measuring heart rate requires some sort of wearable device. Now, new research shows how the signal from a household WiFi device can be used for this crucial health monitoring.
Researchers have developed a machine learning algorithm that uses cardiac MRI images to help identify breast cancer patients who may be at risk of cardiotoxicity during cancer treatment. The research, led by cardiologist Dr Paaladinesh Thavendiranathan from Toronto General Hospital University Health Network, was presented at the European Society of Cardiology's Cardio-Oncology Conference in…
New AI-powered solutions for cardiac ultrasound: Italian medical imaging specialist Esaote will be present at the European Society of Cardiology (ESC) Congress 2025, which starts today (29th August) in Madrid.
An unexpected trigger for heart attack: Researchers show that dormant bacteria can contribute to atherosclerotic plaques and fatal myocardial infarction, effectively making it an infectious disease.
An artificial heart valve made from SEBS, a new type of highly durable and biocompatible plastic, could be a step closer to use in humans, following a successful long-term safety test in animals.
An association between specific electrical patterns and structural characteristics of heart scars after a heart attack could offer a new approach for more targeted and effective arrhythmia treatments.
A new AI tool has been shown to accurately predict who would go on to develop leaks in the heart valves – conditions known as regurgitant valvular heart diseases, at a very early stage from an ECG.
A neural network AI has been trained to detect cardiac amyloidosis from a single echocardiogram video of the heart's apical four-chamber view and differentiate it from similar heart conditions.
Analyzing heart imaging and a full spectrum of medical records, a new AI tool can reveal previously hidden information about a patient’s heart health, including predictions of sudden cardiac arrest.
An AI-assisted model can take low-quality MRI heart scans and turn them into high quality images, while reducing the time needed to scan the heart by about 90%.
