Left ventricular hypertrophy is an independent predictor of coronary artery disease, stroke, and heart failure. Our aim was to detect LVH cardiac magnetic resonance (CMR) scans with automatic methods. We developed an ensemble model based on a three-dimensional version of ResNet. The input of the network included short-axis and long-axis images. We also introduced a standardization methodology to unify the input images for noise reduction. The output of the network is the decision whether the patient has hypertrophy or not. We included 428 patients (mean age: 49 ± 18 years, 262 males) with LVH (346 hypertrophic cardiomyopathy, 45 cardiac amyloidosis, 11 Anderson–Fabry disease, 16 endomyocardial fibrosis, 10 aortic stenosis). Our control group consisted of 234 healthy subjects (mean age: 35 ± 15 years; 126 males) without any known cardiovascular diseases. The developed machine-learning-based model achieved a 92% F1-score and 97% recall on the hold-out dataset, which is comparable to the medical experts. Experiments showed that the standardization method was able to significantly boost the performance of the algorithm. The algorithm could improve the diagnostic accuracy, and it could open a new door to AI applications in CMR.

Nephroprotective imaging in peripheral arterial disease (PAD) is often crucial. We compared the diagnostic performance of non-contrast Quiescent-interval single-shot magnetic resonance angiography (QISS MRA) and carbon-dioxide digital subtraction angiography (CO2 DSA) in chronic lower extremity PAD patients. A 19-segment lower extremity arterial model was used to assess the degree of stenosis (none, 70%) and the image quality (5-point Likert scale: 1-non-diagnostic, 5-excellent image quality). Intra-class correlation coefficient (ICC) was calculated for inter-rater reliability. Diagnostic accuracy and interpretability were evaluated using CO2 DSA as a reference standard. 523 segments were evaluated in 28 patients (11 male, mean age: 71 ± 9 years). Median and interquartile range of subjective image quality parameters for QISS MRA were significantly better compared to CO2 DSA for all regions: (aortoiliac: 4 [4–5] vs. 3 [3–4]; femoropopliteal: 4 [4–5] vs. 4 [3–4]; tibioperoneal: 4 [3–5] vs. 3 [2–3]; all regions: 4 [4–5] vs. 3 [3–4], all p < 0.001). QISS MRA out-performed CO2 DSA regarding interpretability (98.3% vs. 86.0%, p < 0.001). Diagnostic accuracy parameters of QISS MRA for the detection of obstructive luminal stenosis (70%<) as compared to CO2 DSA were as follows: sensitivity 82.6%, specificity 96.9%, positive predictive value 89.1%, negative predictive value 94.8%. Regarding the degree of stenosis, interobserver variability for all regions was 0.97 for QISS MRA and 0.82 for CO2 DSA. QISS MRA proved to be superior to CO2 DSA regarding subjective image quality and interpretability for the imaging of chronic lower extremity PAD.

Background: Lower limb arterial calcification (LLAC) is a robust imaging biomarker of peripheral artery disease (PAD) severity. Vitamin K antagonists are presumed to accelerate cardiovascular calcification. Direct oral anticoagulants (DOACs) may influence vascular calcification differently, but lower limb data are limited. Methods: We performed a single-center retrospective cross-sectional study comparing LLAC on clinically acquired non-contrast CT between DOAC users and controls without anticoagulation. Patients were propensity score-matched 1:1 (48 DOAC vs. 48 control; n = 96) using baseline clinical covariates. Associations between LLAC scores and perioperative or cardiovascular events were assessed. Segment-specific LLAC was quantified on non-contrast CT and normalized for arterial segment length. A prespecified exposure–duration sensitivity analysis compared the outcomes in patients with ≥5 years of continuous DOAC therapy (n = 22) versus matched controls. Results: In the matched cohort, total LLAC scores did not differ significantly between DOAC and control groups (infrarenal aorta: median 7596.0 vs. 8637.0 (p = 0.487), iliac segment: median 5689.5 vs. 5193.5 (p = 0.602). However, in patients with ≥5 years of DOAC use, LLAC scores were significantly lower in proximal segments: infrarenal aorta median 5593.5 vs. 11,185.0 (p = 0.001997) and iliac arteries 5624.5 vs. 11,501.0 (p = 0.001867)). Higher LLAC was associated with major adverse cardiovascular events (such as myocardial infarction, stroke, or significant bleeding) in controls (p = 0.0023) but not in DOAC-treated patients. Conclusions: In this propensity-matched, cross-sectional CT study, long-term DOAC exposure was associated with lower proximal LLAC scores in a small duration-defined subgroup, while the primary matched analysis showed no overall difference in total LLAC scores. Because baseline (pre-DOAC) imaging was unavailable and residual confounding/survivor bias is possible, these findings should be considered hypothesis-generating and require prospective validation. The cohort reflected a mixed lower-extremity vascular population rather than exclusively classic chronic atherosclerotic PAD, which may limit biological interpretation and generalizability.