Amanda Winstead, Freelance Writer08.30.23
Methods of diagnosis and monitoring of osteoporosis continue to develop. While there are some new approaches to the process being explored, many physicians continue using tools that have been standard since the 1980s. This isn’t necessarily a bad thing in itself, but it’s worth considering how the industry’s approach to diagnostic testing and the design of such devices can be more effective and what the future may be.
In this article, we’re going to explore a few of the issues surrounding quality control and device design for osteoporosis testing and monitoring. What are the potential solutions and how can manufacturers and orthopedic professionals provide patients with the best outcomes?
Some of the most pressing challenges revolve around the limitations of the tools being used. Often, the diagnosis process begins when patients present with early warning signs for osteoporosis, such as frequent bone fractures and gradual loss of height. From there, dual-energy X-ray absorptiometry (DXA) scanners are generally the standard tools professionals use for testing. When coupled with an accurate medical history, these devices can be effective. However, they can still present the potential for discomfort, inaccessibility, or inconvenience to patients.
DXA scanners tend to be large and immobile machines, which is problematic as they require patients to travel to facilities for their diagnoses and monitoring. This may be more difficult for those living with mobility challenges or living in rural communities. Not to mention that high-quality scanning with these devices relies on patients to lay still with their legs raised on a platform, which, again, can be difficult for some patients and can lead to quality control challenges.
Another key challenge is the accuracy and consistency of DXA scans. This isn’t a physical design problem, but rather an issue with consistency in testing and machine calibration. One study reported that there is a lack of common databases used among manufacturers upon which to base the bone mineral density (BMD) measurement results of scans. This means patients visiting different facilities for diagnosis and monitoring throughout their care may get inconsistent or inaccurate measurements that affect their care.
AI and ML can analyze huge volumes of both individual patient data and wider historical information quickly and relatively accurately. The results of traditional DXA machines or other diagnostic or monitoring processes on their own may be more time-consuming to interpret and can be influenced by subjective elements.
Clinicians could pair AI and ML software with X-ray and DXA tools, utilizing algorithms to swiftly and accurately identify patterns associated with osteoporosis. Importantly, when trained on the various calibration specifications of various DXA machines, the software can take this data into consideration to overcome the potential hurdles in consistency.
Professionals and companies should be cognizant of the ethics of using AI-powered tools, though. Among the risks of using these platforms is the potential for programming bias, which could produce discriminatory results. Not to mention that a lack of transparency in how some AI systems operate can present trust challenges. If doctors and manufacturers are to couple AI with DXA, it is vital to take steps to mitigate ethical problems, including having full visibility of programming processes and the data sets that have contributed to them.
A good example here is the OsteoBoost vibration belt, which received a Food and Drug Administration (FDA) breakthrough grant for its potential to “stop the progression of bone loss and prevent the onset of osteoporosis.” This type of device functions best when the frequency for use is individually calibrated to the patient’s needs.
The results of DXA (potentially in conjunction with AI) testing and monitoring can help to fine-tune the calibration of such devices. Regular and accurate monitoring of patients not only tracks the efficacy of treatments and preventions, but it also provides further data to keep optimizing the tools for patients, creating a positive treatment and testing cycle.
Therefore, it can be wise for monitoring and testing tool manufacturers, creators of early intervention devices, and orthopedic professionals to collaborate closely. Each party in this equation has insights to offer one another to boost the efficacy of diagnosis, treatment and ongoing care. By committing to sharing data and working together on innovations, care teams may see higher chances of positive patient outcomes.
There are still a lot of challenges to address, though. Some of the most important advances in this area are likely to be those that make testing more portable and agile, which aids accessibility. The more manufacturers and physicians can collaborate on establishing the mobility and accuracy of testing devices, the greater impact it’s likely to have on patient well-being.
Amanda Winstead is a writer from the Portland area with a background in communications and a passion for telling stories. Along with writing she enjoys traveling, reading, working out, and going to concerts. If you want to follow her writing journey, or even just say hi you can find her on Twitter.
In this article, we’re going to explore a few of the issues surrounding quality control and device design for osteoporosis testing and monitoring. What are the potential solutions and how can manufacturers and orthopedic professionals provide patients with the best outcomes?
Recognizing the Challenges
Osteoporosis testing has serious implications for patients’ quality of life. Therefore, it's imperative for those working in orthopedics and manufacturers of related equipment to identify areas for improvement. To proceed effectively, start by getting an understanding of what the current challenges with testing and monitoring are at the moment.Some of the most pressing challenges revolve around the limitations of the tools being used. Often, the diagnosis process begins when patients present with early warning signs for osteoporosis, such as frequent bone fractures and gradual loss of height. From there, dual-energy X-ray absorptiometry (DXA) scanners are generally the standard tools professionals use for testing. When coupled with an accurate medical history, these devices can be effective. However, they can still present the potential for discomfort, inaccessibility, or inconvenience to patients.
DXA scanners tend to be large and immobile machines, which is problematic as they require patients to travel to facilities for their diagnoses and monitoring. This may be more difficult for those living with mobility challenges or living in rural communities. Not to mention that high-quality scanning with these devices relies on patients to lay still with their legs raised on a platform, which, again, can be difficult for some patients and can lead to quality control challenges.
Another key challenge is the accuracy and consistency of DXA scans. This isn’t a physical design problem, but rather an issue with consistency in testing and machine calibration. One study reported that there is a lack of common databases used among manufacturers upon which to base the bone mineral density (BMD) measurement results of scans. This means patients visiting different facilities for diagnosis and monitoring throughout their care may get inconsistent or inaccurate measurements that affect their care.
Adopting Machine Learning and AIs
In recent years there have been advancements that may help to support more accurate diagnostic and monitoring procedures. Doctors and technicians are, of course, essential to effective and contextually relevant testing. However, artificial intelligence (AI) and machine learning (ML) tools can also be powerful components.AI and ML can analyze huge volumes of both individual patient data and wider historical information quickly and relatively accurately. The results of traditional DXA machines or other diagnostic or monitoring processes on their own may be more time-consuming to interpret and can be influenced by subjective elements.
Clinicians could pair AI and ML software with X-ray and DXA tools, utilizing algorithms to swiftly and accurately identify patterns associated with osteoporosis. Importantly, when trained on the various calibration specifications of various DXA machines, the software can take this data into consideration to overcome the potential hurdles in consistency.
Professionals and companies should be cognizant of the ethics of using AI-powered tools, though. Among the risks of using these platforms is the potential for programming bias, which could produce discriminatory results. Not to mention that a lack of transparency in how some AI systems operate can present trust challenges. If doctors and manufacturers are to couple AI with DXA, it is vital to take steps to mitigate ethical problems, including having full visibility of programming processes and the data sets that have contributed to them.
Utilizing Early Intervention
It’s worth considering how prevention and early intervention tools can minimize the long-term effects of osteoporosis. Importantly, they can be part of a holistically beneficial approach to ongoing testing and monitoring processes.A good example here is the OsteoBoost vibration belt, which received a Food and Drug Administration (FDA) breakthrough grant for its potential to “stop the progression of bone loss and prevent the onset of osteoporosis.” This type of device functions best when the frequency for use is individually calibrated to the patient’s needs.
The results of DXA (potentially in conjunction with AI) testing and monitoring can help to fine-tune the calibration of such devices. Regular and accurate monitoring of patients not only tracks the efficacy of treatments and preventions, but it also provides further data to keep optimizing the tools for patients, creating a positive treatment and testing cycle.
Therefore, it can be wise for monitoring and testing tool manufacturers, creators of early intervention devices, and orthopedic professionals to collaborate closely. Each party in this equation has insights to offer one another to boost the efficacy of diagnosis, treatment and ongoing care. By committing to sharing data and working together on innovations, care teams may see higher chances of positive patient outcomes.
Conclusion
DXA tools are still a standard method for the diagnosis and monitoring of osteoporosis. However, these devices have challenges surrounding consistent measurement protocols among different manufacturers, and accessibility. One of the solutions to consistency and accuracy problems is to couple DXA machines with AI and ML software. The design of early intervention devices can also benefit from sharing of data between DXA manufacturers and physicians, resulting in more holistically beneficial treatment and prevention for patients.There are still a lot of challenges to address, though. Some of the most important advances in this area are likely to be those that make testing more portable and agile, which aids accessibility. The more manufacturers and physicians can collaborate on establishing the mobility and accuracy of testing devices, the greater impact it’s likely to have on patient well-being.
Amanda Winstead is a writer from the Portland area with a background in communications and a passion for telling stories. Along with writing she enjoys traveling, reading, working out, and going to concerts. If you want to follow her writing journey, or even just say hi you can find her on Twitter.
