Cardiac amyloidosis (CA) represents the accumulation and deposition of misfolded protein fibrils in the myocardium, resulting in progressive restrictive cardiomyopathy. Light chain (AL) and transthyretin (TTR) amyloidosis are the most common types of CA. While endomyocardial biopsy remains the gold standard for diagnosing cardiac amyloidosis, its invasive nature and associated risk of complications have led to increased reliance on clinical suspicion and noninvasive imaging modalities as alternative diagnostic tools. Over the last decade, radionuclide imaging studies have become a widely accepted tool in diagnosing ATTR CA, whereas its diagnostic utility in AL CA detection remains limited. Recent advances in PET-CT radiopharmaceuticals have further expanded the potential of nuclear imaging as a comprehensive tool for diagnosis, prognostication, and therapy monitoring. This literature review appraises the current applications of nuclear imaging in the clinical management of cardiac amyloidosis.
The SARS-CoV-2 pandemic challenged the authorities into taking measures that limit the spread of the virus in the community. TAMEC project (Advanced Community Approaches to Epidemic Management) developed a system that detects the contacts of patients infected with the SARS-CoV-2 virus and alerts them using mobile applications. It allows real-time analysis of confirmed patients and identified contacts, early detection of new outbreaks, and provides decision- making information to prevent viral transmission. The system uses its patent for homomorphic encryption, a method that allows to process and manipulate data in an encrypted format, without having access to the original data. Thus, the solution proposed in the TAMEC project fully respects the privacy rules of citizens imposed by the EU, using the concept of privacy preservation. Our solution offers the possibility to identify and validate a risk score that could become an extremely helpful tool in the stratification of COVID-19 patients. The TAMEC system is innovative in its simplicity and ability to facilitate the prevention of the spread of the SARS-CoV-2 virus in the community
Artificial Intelligence (AI) has become an important tool for computer-aided diagnosis of medical imaging. This review aims to provide an overview for clinicians, explaining the relevant aspects of artificial intelligence and machine learning (ML) and presenting up-to-date applications of AI and ML techniques to medical imaging methods such as angiography, magnetic resonance, and echocardiography. For each imaging method, we present the acquisition process, the types of diagnostic test interpretation, and the challenges related to them, as well as how AI/ML techniques, have improved the process of decision making. A summary of selected works applying AI/ML techniques to medical imaging is organized into a table, which highlights the scope of the study, the dataset used, the details of each approach as well as the measured results, including objectives and criteria. The overall benefits of AI in medical imaging are extracted based on the diverse applications and high evaluation scores. In the end, cardiologists should have an advanced understanding of using AI to integrate clinical data and making the final decision in diagnosis.
Background: Degenerative aortic stenosis (AS) and transthyretin cardiac amyloidosis (ATTR-CM), particularly wild-type ATTR (ATTRwt), are age-associated disorders that frequently converge in elderly patients. Their coexistence may obscure diagnosis, amplify heart-failure burden, and complicate risk stratification before and after aortic valve replacement. Methods: This narrative review was conducted using a structured literature search focused on ATTR-CM, AS, transcatheter aortic valve implantation/replacement (TAVI/TAVR), and nuclear cardiology. Priority was given to cohort studies, systematic reviews, consensus documents, and guideline statements. Main findings: Across observational cohorts and meta-analyses, ATTR-CM is consistently identified in a clinically meaningful minority of older patients with severe AS, especially among those referred for TAVI. Reported prevalence varies with age, referral pathway, diagnostic protocol, and whether equivocal grade 1 uptake is included, but most contemporary TAVI-oriented cohorts place definite ATTR-CM in the high single-digit to mid-teen percentage range. Clinical suspicion should increase in patients with disproportionate left-ventricular wall thickening, low-flow low-gradient AS, restrictive physiology, elevated cardiac biomarkers, conduction disease, atrial fibrillation, or extracardiac ATTR clues such as bilateral carpal tunnel syndrome. Conclusions: Bone-avid tracer scintigraphy with 99mTc-pyrophosphate, 99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid, or 99mTc-hydroxymethylene diphosphonate, interpreted with SPECT or SPECT/CT and combined with mandatory exclusion of a monoclonal protein, enables robust non-biopsy diagnosis of ATTR-CM. In severe AS, nuclear diagnosis should be embedded in a pragmatic, multidisciplinary pathway that identifies patients likely to benefit from valve intervention, ATTR-specific therapy, genetic testing, and tailored follow-up.
