Cardiac fibers may be represented by a network of interconnected cables for simulating electrical propagation. The lack of automatic cable mesh generation tool has hampered this modeling approach. We aim to provide and evaluate an algorithmic solution to this problem. We developed an open-source C++/Python package for the construction of a monolayer interconnected cable model from a triangulated surface with fiber orientation, targeting a given longitudinal and transverse space step. The workflow of the algorithm starts with the generation of evenly spaced streamlines aligned with fiber orientation. Another set of streamlines, orthogonal to the fibers, is used to specify lateral connections. The intersection between the two sets of streamlines gives the vertices of the cable mesh, determines its connectivity, and defines a polygonal tessellation of the surface that can be triangulated. Finite differences can then be applied to solve a reaction-diffusion equation on the cable mesh. The approach was validated in increasingly complex configurations and up to near-cellular resolutions (20 to 200μm). Fiber orientation noise, singularities and abrupt changes in orientation reduced the local coupling by altering the microstructure of the tissue. The pipeline for mesh generation was tested using a publicly available cohort of 98 patient-specific geometries. The stability limit of the numerical scheme was assessed by spectral analysis of the diffusion matrix and was compared to triangular meshes and cartesian grids. This physiologically based mesh generation tool may be used as a building block for the construction of multilayer three-dimensional models of the atria for the simulation of discrete propagation.

The field of early biomarker discovery is characterized by a lack of consensus on the choice of statistical methodology, which may impede later progress towards clinically useful biomarkers. The Receiver-Operator Characteristic (ROC) is a de facto standard for determining the performance of In Vitro Diagnostic (IVD) devices. In this study, we aimed to systematically identify and mitigate prevalent pitfalls in biomarker discovery efforts and propose a best-practice guideline based on a ROC analysis framework. By maintaining a careful alignment to the study objectives through a sample procurement plan, study size determination and data analysis by the ROC framework, we formulated a biomarker discovery protocol. We performed Monte Carlo simulations to inform the investigator on the suitable number of study participants, the statistical power and sample bin allocation strategy. The main concept is illustrated using proteomic data of newly diagnosed cancer cases and concurrent external controls. The work demonstrates a regulatory-adherent pipeline to achieve an effect superior to the current best biomarker used as a predicate medical device. In our proof-of-concept ROC-based analysis in samples from a publicly available dataset, we detected statistically significant composite biomarkers, of which we validated a subset in an independent dataset acquired using the same proteomic analysis method. Intriguingly, commonly used feature selection methods do not identify the same composite biomarkers from the same data, and their selections show limited overlap with the ROC-based analysis. The proposed approach can facilitate translation of scientific discoveries into regulatory approved biomarker tests fit for use in clinical medicine.

The fetal abdomen is crucial in prenatal screening, offering key insights into fetal growth and congenital anomalies. However, segmenting internal abdominal structures in ultrasound (US) remains challenging due to anatomical variability, overlapping organs, and low contrast. While CNN-based models have shown strong performance in fetal head analysis, most existing methods focus on biometric measurements (e.g., head or abdominal circumference), leaving internal abdominal organ segmentation largely underexplored. Recently, foundation models like the Segment Anything Model (SAM) have emerged as flexible alternatives, enabling zero- or few-shot segmentation. Yet, their performance on fetal US remains poorly understood, and the need for adaptation is still an open question. We compare two segmentation strategies: (1) task-specific CNNs, including UNet, Attention UNet, nnUNet, DeepLabv3+, and their focal-loss variants; and (2) adapted SAM-based models. The latter includes zero-shot variants (SAMPoint, SAMBBox), pre-trained models (SAM Med2DBBox, MedSAMBBox), and adapted configurations, including lightweight fine-tuned models (SAM-LoRA, MedSAM-FrozenEncoder) and SAM Med2D variants with adapted layers. Experiments are conducted on a curated dataset of fetal abdominal US images with manual segmentations of the liver, stomach, artery, and umbilical vein. Performance is evaluated using Dice Similarity Coefficient, Intersection over Union, and precision. Statistical significance is assessed via pairwise Friedman chi-square tests. Zero-shot SAM variants performed poorly, particularly on small or low-contrast structures. In contrast, adapted SAM models consistently outperformed CNNs, reaching DSC scores up to 0.90 (liver) and 0.80 (artery). Prompt-based interaction enables semi-automated, human-in-the-loop workflows, supporting clinical applicability.