PRIME 2.0: An Update to The Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation Checklist.

The PRIME 2.0 checklist is an updated, domain-specific framework designed to standardize the development, evaluation, and reporting of artificial intelligence (AI) applications in cardiovascular imaging. This update specifically responds to rapid advances from traditional machine learning to deep learning, large language models, and multimodal generative AI.

Beyond Post hoc Explanations: A Comprehensive Framework for Accountable AI in Medical Imaging Through Transparency, Interpretability, and Explainability.

The integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic capabilities, yet the black-box nature of deep learning models poses significant challenges for clinical adoption. Current explainable AI (XAI) approaches, including SHAP, LIME, and Grad-CAM, predominantly focus on post hoc explanations that may inadvertently undermine clinical decision-making by providing misleading confidence in AI outputs. This paper presents a systematic review and meta-analysis of 67 studies (covering 23 radiology, 19 pathology, and 25 ophthalmology applications) evaluating XAI fidelity, stability, and performance trade-offs across medical imaging modalities.

Deep-learning derived detailed measures of body fat distribution and cardiovascular outcomes in non-dialysis dependent chronic kidney disease: The Multi-Ethnic Study of Atherosclerosis.

An obesity paradox, i.e., an association between high BMI and reduced all-cause mortality, has been reported in patients with non-dialysis-dependent chronic kidney disease (CKD).

Ultrasonic Texture Analysis for Predicting Acute Myocardial Infarction.

Background: Acute myocardial infarction (MI) alters cardiomyocyte geometry and architecture, leading to changes in the acoustic properties of the myocardium.

Objectives: This study examines ultrasomics-a novel cardiac ultrasound-based radiomics technique to extract high-throughput pixel-level information from images-for identifying ultrasonic texture and morphologic changes associated with infarcted myocardium.

Methods: The authors included 684 participants from multisource data: a) a retrospective single-center matched case-control dataset; b) a prospective multicenter matched clinical trial dataset; and c) an open-source international and multivendor dataset.

Use of CT-derived non-cardiovascular calcification marker for predicting cardiovascular events among diabetic older adults: the multi-ethnic study of atherosclerosis.

Objective: To evaluate the discriminative power of coronary artery calcium (CAC) score-based Cox models for predicting cardiovascular disease (CVD) in older adults with longstanding diabetes, a population at elevated CVD risk. We also aimed to determine whether adding computed tomography (CT)-derived costal cartilage calcification (CCC) improves risk prediction, given the potential limitation of CAC due to widespread soft tissue calcification.

Materials And Methods: We analyzed adults ≥ 65 years from the multi-ethnic study of atherosclerosis with longstanding diabetes mellitus (DM, ≥ 5 years, n = 231) and compared them to non-DM participants (n = 1148).

Diabetes and longitudinal changes in deep learning-derived measures of vertebral bone mineral density using conventional CT: the Multi-Ethnic Study of Atherosclerosis.

Objective: To investigate the longitudinal association between diabetes and changes in vertebral bone mineral density (BMD) derived from conventional chest CT and to evaluate whether kidney function (estimated glomerular filtration rate (eGFR)) modifies this relationship.

Materials And Methods: This longitudinal study included 1046 participants from the Multi-Ethnic Study of Atherosclerosis Lung Study with vertebral BMD measurements from chest CTs at Exam 5 (2010-2012) and Exam 6 (2016-2018). Diabetes was classified based on the American Diabetes Association criteria, and those with impaired fasting glucose (i.

Topological Data Analysis in the Assessment of Coronary Atherosclerosis: A Comprehensive Narrative Review.

Plaque build-up in the coronary arteries can restrict blood flow or become unstable and contribute to cardiovascular events. Evaluating these plaques is essential for cardiovascular risk stratification and determining the most appropriate treatment strategies. This review aimed to investigate the potential of topological data analysis (TDA), a novel technique, in assessing coronary atherosclerosis.

Mild to moderate COPD, vitamin D deficiency, and longitudinal bone loss: the Multi-ethnic Study of Atherosclerosis.

Objective: Despite the established association between chronic obstructive pulmonary disease (COPD) severity and risk of osteoporosis, even after accounting for the known shared confounding variables (e.g., age, smoking, history of exacerbations, steroid use), there is paucity of data on bone loss among mild to moderate COPD, which is more prevalent in the general population.

Quantifying the Unknowns of Plaque Morphology: The Role of Topological Uncertainty in Coronary Artery Disease.

This article aimed to explore topological uncertainty in medical imaging, particularly in assessing coronary artery calcification using artificial intelligence (AI). Topological uncertainty refers to ambiguities in spatial and structural characteristics of medical features, which can impact the interpretation of coronary plaques. The article discusses the challenges of integrating AI with topological considerations and the need for specialized methodologies beyond traditional performance metrics.

Bringing Precision to Pediatric Care: Explainable AI in Predicting No-Show Trends Before and During the COVID-19 Pandemic.

Patient no-shows significantly disrupt pediatric healthcare delivery, highlighting the necessity for precise predictive models, especially during the dynamic shifts caused by the SARS-CoV-2 pandemic. In outpatient settings, these no-shows result in medical resource underutilization, increased healthcare costs, reduced access to care, and decreased clinic efficiency and increased provider workload. The objective is to develop a predictive model for patient no-shows using data-driven techniques.

A Deep Learning Model for Three-Dimensional Determination of Whole Thoracic Vertebral Bone Mineral Density from Noncontrast Chest CT: The Multi-Ethnic Study of Atherosclerosis.

Background Recent studies have investigated how deep learning (DL) algorithms applied to CT using two-dimensional (2D) segmentation (sagittal or axial planes) can calculate bone mineral density (BMD) and predict osteoporosis-related outcomes. Purpose To determine whether TotalSegmentator, an nnU-net algorithm, can measure three-dimensional (3D) vertebral body BMD across consistently imaged thoracic levels (T1-T10) at any conventional, noncontrast chest CT examination. Materials and Methods This study is a secondary analysis of a multicenter ( = 6) prospective cohort, the Multi-Ethnic Study of Atherosclerosis (MESA).

Visualizing radiological data bias through persistence images.

Persistence images, derived from topological data analysis, emerge as a powerful tool for visualizing and mitigating biases in radiological data interpretation and AI model development. This technique transforms complex topological features into stable, interpretable representations, offering unique insights into medical imaging data structure. By providing intuitive visualizations, persistence images enable the identification of subtle structural differences and potential biases in data acquisition, interpretation, and AI model training.

Persistence barcodes: A novel approach to reducing bias in radiological analysis.

Persistence barcodes emerge as a promising tool in radiological analysis, offering a novel approach to reduce bias and uncover hidden patterns in medical imaging. By leveraging topological data analysis, this technique provides a robust, multi-scale perspective on image features, potentially overcoming limitations in traditional methods and Graph Neural Networks. While challenges in interpretation and implementation remain, persistence barcodes show significant potential for improving diagnostic accuracy, standardization, and ultimately, patient outcomes in the evolving field of radiology.

Persistence landscapes: Charting a path to unbiased radiological interpretation.

Persistence landscapes, a sophisticated tool from topological data analysis, offer a promising approach to address biases in radiological interpretation and AI model development. By transforming complex topological features into statistically analyzable functions, they enable robust comparisons between populations and datasets. Persistence landscapes excel in noise filtration, fusion bias mitigation, and enhancing machine learning models.

Mitigating bias in radiology: The promise of topological data analysis and simplicial complexes.

Topological Data Analysis (TDA) and simplicial complexes offer a novel approach to address biases in AI-assisted radiology. By capturing complex structures, n-way interactions, and geometric relationships in medical images, TDA enhances feature extraction, improves representation robustness, and increases interpretability. This mathematical framework has the potential to significantly improve the accuracy and fairness of radiological assessments, paving the way for more equitable patient care.

Diabetes mellitus disrupts lncRNA Malat1 regulation of cardiac mitochondrial genome-encoded protein expression.

Understanding the cellular mechanisms behind diabetes-related cardiomyopathy is crucial as it is a common and deadly complication of diabetes mellitus. Dysregulation of the mitochondrial genome has been linked to diabetic cardiomyopathy and can be ameliorated by altering microRNA (miRNA) availability in the mitochondrion. Long noncoding RNAs (lncRNAs) have been identified to downregulate miRNAs.

Cardiac ultrasomics for acute myocardial infarction risk stratification and prediction of all-cause mortality: a feasibility study.

Background: Current risk stratification tools for acute myocardial infarction (AMI) have limitations, particularly in predicting mortality. This study utilizes cardiac ultrasound radiomics (i.e.

Generative AI in oncological imaging: Revolutionizing cancer detection and diagnosis.

Generative AI is revolutionizing oncological imaging, enhancing cancer detection and diagnosis. This editorial explores its impact on expanding datasets, improving image quality, and enabling predictive oncology. We discuss ethical considerations and introduce a unique perspective on personalized cancer screening using AI-generated digital twins.

A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.

To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53).

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data.

Prediction of heart failure and all-cause mortality using cardiac ultrasomics in patients with breast cancer.

Breast cancer chemotherapy/immunotherapy can be associated with treatment-limiting cardiotoxicity. Radiomics techniques applied to ultrasound, known as ultrasomics, can be used in cardio-oncology to leverage echocardiography for added prognostic value. To utilize ultrasomics features collected prior to antineoplastic therapy to enhance prediction of mortality and heart failure (HF) in patients with breast cancer.