AEIOU Scientific’s Cortical Bone Mechanics Technology™ is changing the game

CBMT reveals bone quality information that BMD cannot provide

We measure the kind of bone most likely to fracture with osteoporosis

Jeff Spitzner
William Timmons
Lyn Bowman, Engr
Chief Technology Officer
Anne Loucks, PhD
Chief, Skeletal Health Research
Brian Clark, PhD
Chief, Aging Research


Osteoporosis – or more correctly, bone loss – affects 54 million Americans and 325 million people worldwide. Over age 50, half of all women and one-quarter of all men will experience an osteoporotic fracture. Unfortunately, today there are some patients who receive treatment but do not need it, while many others are not receiving the preventive care they require. There is a critical need to better identify people who need treatment in order to reduce their risk of low-trauma fractures and substantially decrease costs and suffering.

After age 60, most bone loss is cortical (rather than trabecular), and most bone fractures occur at predominantly cortical bone site. Therefore, it is essential to be able to accurately measure or estimate the strength of cortical bone in order to determine who truly has a bone weakness problem that needs treatment.,



Measured by Quasistatic Mechanical Testing (QMT), bone stiffness accurately predicts cortical bone strength (R2>0.95), but QMT requires removal of a bone from the body. Reference Point Indentation (RPI) inserts a needle through the skin and drives it a short distance into the underlying cortical bone, but RPI measurements have shown little association with any mechanical property of bone (R2≤0.33). Mechanical Response Tissue Analysis (MRTA) analyzed vibrations to measure the stiffness of bones in vivo, but its accuracy (R2=0.62) and repeatability (CV=150%) were poor and the technology was abandoned.

In CBMT, Ohio University investigators have identified and corrected the sources of error in MRTA.  The resulting manually operated, proof-of-concept CBMT prototype was used to make non-invasive measurements of the stiffness and strength of cortical bone in 35 cadaveric human arms that were indistinguishable from QMT measurements of the excised bones (y = 1.00x, R2 = 0.99). Repeatability in healthy human subjects was excellent (CV<4%). We are now developing an automated prototype with simultaneous data acquisition and analysis as a scientific laboratory instrument.


A Critical Barrier to Progress

Osteoporosis is characterized by reduced bone strength, but no current medical device measures bone strength.  Instead, since 1993 physicians have been advised to diagnose osteoporosis by measuring BMD at sites of predominantly trabecular bone in the spine and hip. However, subsequent research has found that BMD does not predict fractures well.  In a study of 160,000 post-menopausal women, 11,397 (96%) of 11,806 women diagnosed with osteoporosis did not fracture, while 1757 (81%) of 2166 fractures occurred in those diagnosed without osteoporosis. Consequently, since 2011, many physicians decide who to treat by assessing clinical risk factors for fracture using the web-based questionnaire FRAX®. The FRAX® web page records 8000 assessments per day. Yet, a recent prospective study of post-menopausal women found that 74% of those identified by FRAX® as high risk did not fracture, while 81% of fractures occurred in women identified as low risk.  Combining BMD and FRAX® only reduced these errors to 69% and 62%.  As a result, preventive care is administered to millions of patients who do not fracture, and withheld from millions who do. These diagnostic errors drive healthcare costs

AEIOU Scientific is poised to overcome that barrier with new CBM technology that measures cortical bone strength more accurately. We are currently readying to market a CBMT scientific laboratory device for research use. In parallel, we are working to develop CBMT as a separate product for FDA approval as a medical device. (This device is not cleared by the FDA for distribution in the United States).

AEIOU Scientific is commercializing the patent-pending CBMT developed at Ohio University

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