Artificial intelligence-based automated left ventricular mass quantification from non-contrast cardiac CT scans: correlation with contrast CT and cardiac MRI.

Background: Non-contrast CT scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (MRI). We assessed the feasibility of LV mass estimation from standard, ECG-gated, non-contrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and cardiac MRI. Methods: We enrolled consecutive patients who underwent coronary CTA, which included non-contrast CT calcium scanning and contrast CTA, and cardiac MRI. The median interval between coronary CTA and MRI was 22 days (IQR: 3-76). We utilized an nn-Unet AI model that automatically segmented non-contrast CT structures. AI measurement of LV mass was compared to contrast CTA and MRI. Results: A total of 316 patients (Age: 57.1±16.7, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r=0.84, p<0.001), with no significant differences (136.5±55.3 vs. 139.6±56.9 g, p=0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to MRI, measured LV mass was higher with AI (136.5±55.3 vs. 127.1±53.1 g, p<0.001). There was an excellent correlation between AI and MRI (r=0.85, p<0.001), with a small bias (-9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and MRI, or AI and MRI. Conclusions: The AI-based automated estimation of LV mass from non-contrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and MRI.

Tendinopathy and tendon material response to load: What we can learn from small animal studies.

Tendinopathy is a debilitating disease that causes as much as 30% of all musculoskeletal consultations. Existing treatments for tendinopathy have variable efficacy, possibly due to incomplete characterization of the underlying pathophysiology. Mechanical load can have both beneficial and detrimental effects on tendon, as the overall tendon response depends on the degree, frequency, timing, and magnitude of the load. The clinical continuum model of tendinopathy offers insight into the late stages of tendinopathy, but it does not capture the subclinical tendinopathic changes that begin before pain or loss of function. Small animal models that use high tendon loading to mimic human tendinopathy may be able to fill this knowledge gap. The goal of this review is to summarize the insights from in-vivo animal studies of mechanically-induced tendinopathy and higher loading regimens into the mechanical, microstructural, and biological features that help characterize the continuum between normal tendon and tendinopathy. STATEMENT OF SIGNIFICANCE: This review summarizes the insights gained from in-vivo animal studies of mechanically-induced tendinopathy by evaluating the effect high loading regimens have on the mechanical, structural, and biological features of tendinopathy. A better understanding of the interplay between these realms could lead to improved patient management, especially in the presence of painful tendon.