The Future of Joint Range of Motion Measurement

Accurate measurement of Joint Range of Motion (ROM) is critical for effective patient care, diagnosis, and treatment planning. Traditionally, doctors and physical therapists use goniometers and visual estimations, however they are subject to human error and inconsistency. With the rapid advancements in artificial intelligence (AI) and machine learning, we’re now witnessing a transformative shift in ROM measurement technology.

A patient’s range of motion is measured in-person using a goniometer. QuickPose uses machine learning to accurately measure angles between joints from video or image inputs.

Musculoskeletal Health is a major part of US Healthcare, and so is investment in AI technologies.

The global AI in healthcare market is projected to grow from USD 14.6 Billion in 2023 to USD 102.7 Billion by 2028.

The National Health Interview Survey (NHIS) reported that 126.6 million US Adults had a musculoskeletal disorder between 2013–2015. That’s 50% of adults in America, and with such significant numbers of people with musculoskeletal issues, it’s time to embrace the future of joint ROM measurement with AI pose estimation.

Reflex — Shoulder Mobility App is an app built using QuickPose that allows patients to track their shoulder range of motion throughout recovery.

Transforming Consultations and Patient Care

AI pose estimation is revolutionising consultations and patient care, allowing healthcare professionals to focus on patients rather than data collection. By streamlining ROM measurement, AI saves valuable time, which can be devoted to personalised patient interactions and treatment planning.

Adopting AI solutions that save time can significantly enhance overall efficiency and patient satisfaction in the healthcare industry. Especially as patients become more tech-savvy and want to take control of their own health metrics.

The Power of Shared Data and Learnings

Large-scale data collection enabled by AI pose estimation holds the key to better, data-driven decision-making in healthcare. With the ability to measure ROM in thousands of patients simultaneously, the wealth of data generated can inform optimised post-surgery exercises and recovery periods. Such data-driven insights can ultimately lead to improved patient outcomes and a more robust understanding of joint health.

Embracing the Future of Joint Range of Motion Measurement

Curious to explore more of what you can do with AI Pose Estimation? Take advantage of these options with QuickPose:

• Test the capabilities of QuickPose’s demo app with joint measurement by registering for our TestFlight here
• Clone our GitHub repo by visiting our GitHub Repository here

QuickPose: The AI-Powered Solution

QuickPose offers a seamless and accurate way to measure joint ROM. A production-ready iOS SDK, QuickPose is designed to integrate effortlessly with healthcare providers’ existing apps, making implementation a breeze. Its state-of-the-art AI algorithms boast an impressive accuracy rate of up to 5º, on par with traditional goniometers.

For healthcare providers, AI technology means being able to assess patients’ ROM during video calls, or develop apps to monitor progress and prescribe exercises. The ease and precision offered by QuickPose paves the way for a new era in patient care.

Conclusion

The future of joint ROM measurement is undeniably intertwined with AI pose estimation. QuickPose’s solutions offer a golden opportunity to enhance efficiency, accuracy, and patient care in the healthcare sector. Don’t miss the chance to be part of this transformation — test, explore, and consult with QuickPose to uncover the true potential of AI-driven ROM measurement.