Open-source, fully automated framework for end-to-end aortic analysis from CT angiography (CTA), built on top of TotalSegmentator baseline segmentations with additional refinement. AortaExplorer extracts established biomarkers such as diameters across anatomical segments defined by the European Society of Cardiology, and introduces new metrics including aortic tortuosity. Diameter measurements were validated against expert manual readings in more than 10,000 CTA scans from Danish population cohorts, and the tortuosity index's increase with age is consistent with prior literature; the tool reduces per-case analysis time from about 15 minutes to under 5 minutes.

Open, transparent deep-learning method for measuring left ventricular global longitudinal strain (GLS) from routine 2D echocardiography, built as an alternative to proprietary vendor strain software. Unity-GLS is a multi-image neural network (based on the HigherHRNet-W32 pose-estimation architecture) that identifies the mitral annulus, LV apex, and endocardial curve from a target frame plus six neighbouring frames, across apical 4-, 3-, and 2-chamber views. Validated against multi-expert (11-reader) consensus tracings from 100 echocardiograms in a UK-wide collaborative, Unity-GLS agreed with expert consensus as strongly as individual human experts and two proprietary vendor packages (correlation with consensus: 0.91 vs. 0.73-0.85 for other methods).

Explainable Inception-style 1D CNN for multi-label arrhythmia detection from 12-lead ECGs, integrating Grad-CAM visualization to highlight the waveform segments driving each prediction. Trained on MIMIC-IV-ECG and externally validated on PTB-XL across atrial fibrillation, sinus tachycardia, conduction disturbances (RBBB/LBBB/LAFB), long QT, Wolff-Parkinson-White pattern, and paced-rhythm detection, with all metrics exceeding 90% internally and strong generalization on external validation.

1D residual neural network that screens 10-second, 12-lead ECG tracings for six common abnormalities: first- and second-degree AV block patterns, right and left bundle branch block, sinus bradycardia, atrial fibrillation, and sinus tachycardia. Reported F1 scores above 80% and specificity over 99% when benchmarked against cardiology residents. Developed at Universidade Federal de Minas Gerais and trained on the large Brazilian CODE-15% ECG dataset.

1D residual neural network that predicts a patient's age directly from a 12-lead ECG; the gap between this predicted 'ECG age' and true chronological age is used as a biomarker of cardiovascular risk and mortality. Trained on the CODE-15% Brazilian ECG dataset, with the original R² of 0.71 later reproduced (R² = 0.70) in an independent German validation cohort. Developed by researchers at Uppsala University and UFMG.