Stepwise ablation achieving sinus rhythm in patients with chronic AF has a significant impact on LA electrical activity but is associated with recovery of LA function.

We present the case of a caseous calcification of the mitral annulus, responsible for two strokes, in a 72-year-old female patient. The brain computed tomography (CT) scan confirmed the presence of a calcific embolus. The echocardiography showed a liquidy, pseudotumoral mass combined with numerous calcifications located in the posterior part of the mitral annulus and extending toward the inferior surface of the left ventricle. During surgery, we found a direct communication between the caseous necrosis and the lumen of the left ventricle at the level of its inferior wall. We performed a valve repair procedure and excision of the caseous necrosis, combined with injection of bioglue into the cavity, to avoid recurrence. Six months after the procedure, the patient was in good health, and had no recurrence of stroke with a satisfactory echocardiography. This is the first description of spontaneous fistulization of a caseous necrosis in the lumen of the left ventricle, explaining a new mechanism for cerebral embolism during the course of calcifying diseases of the mitral annulus.

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.