Built over strong clinical research foundation

While the DEXA scan is the common "gold standard" for bone health, it is often inaccurate because it uses a 2D image that can be misled by arthritis or calcium in your arteries. CT uses the 3D details to look inside your bone with much higher precision. Research shows that CT is nearly 3 times more effective at predicting your actual risk of a future fracture than a DEXA scan.

A CT scan is a very reliable way to find and measure fatty liver, correctly identifying moderate-to-severe cases in about 82% of patients. Unlike an ultrasound, which depends on a doctor's visual assessment, a CT provides a precise numerical score to determine the exact level of fat buildup. It is especially helpful for patients with higher body weight, as it can see through extra tissue that often makes ultrasound images blurry or hard to read.

CT is widely regarded as the gold standard for measuring visceral fat because it can directly visualize and precisely quantify fat around the internal organs. Unlike indirect measures (BMI, waist), CT provides an objective, anatomical measurement of visceral fat area/volume. That’s why CT-based visceral fat quantification is commonly used as the reference method in research and clinical validation.

AI-powered body composition analysis transcends the limitations of BMI by providing precise, opportunistic measurements of skeletal muscle and adipose tissue from routine clinical CT scans. These automated tools identify "hidden" sarcopenia and myosteatosis, offering critical prognostic insights into patient survival and surgical outcomes that are often overlooked in standard radiological reports. By integrating directly into clinical workflows, this technology enables scalable, data-driven risk stratification across various medical specialties.

Automated Abdominal Aortic Calcification (AAC) scoring transforms routine CT scans into a powerful longitudinal health monitor, with severe scores (>= 6 on the AAC-24 scale) correlating to a 2.87-fold increase in major cardiovascular events. Beyond vascular health, these AI-quantified metrics serve as a systemic "biological clock," where every unit increase in calcium burden signals a 7% higher risk of long-term mortality. By integrating these automated metrics into clinical workflows, providers can transition from reactive imaging to proactive, data-driven risk management for heart disease, stroke, and frailty.

Incidental coronary artery calcium (CAC) is found in up to 53% of non-cardiac chest CTs, yet it is mentioned in the clinical impression only 4% of the time. Implementing real-time AI notification (as seen in the NOTIFY-PICTURE trial) bridges this gap, driving a 7.5x increase in statin prescriptions (47% vs. 6%) and reclassifying up to 55% of intermediate-risk patients into more accurate treatment categories. With severe CAC conferring a 4-fold higher risk of mortality, automating this "opportunistic" finding transforms every chest CT into a life-saving triage tool that prevents 1 major event for every 12 patients treated.

Automated AAA screening bridges a critical diagnostic gap where up to 69% of incidentally imaged aneurysms go unreported, potentially preventing the 80% mortality rate associated with acute rupture. By identifying these "silent" expansions at an early stage, AI-driven workflows enable a 48% reduction in AAA-specific mortality through timely elective intervention. This opportunistic "backstop" transforms every abdominal scan into a life-saving screening event, ensuring that high-risk patients are moved into surveillance long before a crisis occurs.