AI-informed retinal biomarkers predict 10-year risk of onset of multiple hematological malignancies.

Background: Early detection of hematological malignancies improves long-term survival but remains a critical challenge due to heterogeneity in clinical presentation. Chronic inflammation is a key driver in hematologic cancers and is known to induce compensatory microvascular changes. High-resolution, non-invasive retinal imaging can allow the quantification of microvascular changes for the early detection of hematological malignancies.

Fundus Photograph-Derived Computational Features Predict Risk of Cardiovascular Events in the Chronic Renal Insufficiency Cohort Clinical Observational Study.

Background: Patients with CKD face an elevated but variable risk of cardiovascular (CV) disease. Retinal imaging in CKD provides a non-invasive opportunity for CV risk stratification through microvascular analysis. The objective of this study was to evaluate retinal vascular features extracted via computer vision and machine learning approaches for CV risk and their added value over established risk calculators in CKD patients.

Association of patient-level characteristics with long-term outcomes after Fontan palliation: Rationale, design, and baseline characteristics of the Pediatric Cardiac Care Consortium Fontan cohort study.

Background: The Fontan operation is used to palliate single ventricle congenital heart defects (CHD) but poses significant morbidity and mortality risks. We present the design, planned analyses, and rationale for a long-term Fontan cohort study aiming to examine the association of patient characteristics at the time of Fontan with post-Fontan morbidity and mortality.

Methods And Results: We used the Pediatric Cardiac Care Consortium (PCCC), a US-based, multicenter registry of pediatric cardiac surgeries to identify patients who underwent the Fontan procedure for single ventricle CHD between 1 and 21 years of age.

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

Background: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death.

Methods: DeHFt comprises two steps.

Normalization of breast MRIs using cycle-consistent generative adversarial networks.

Objectives: Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used to complement ultrasound examinations and x-ray mammography for early detection and diagnosis of breast cancer. However, images generated by various MRI scanners (e.g.