How AI Is Bringing Musculoskeletal Imaging to New Frontiers

Advancements in MRI technology are enabling deeper insights into musculoskeletal structures than ever before possible. Moreover, MRI improves upon other imaging modalities by acquiring data on bones, muscle, tendons, fat, and edema with a single scan. While these data have great value in identifying and diagnosing a number of musculoskeletal abnormalities, only highly sub-specialized radiologists are able to interpret this data with high accuracy. These very real barriers - the significant amount of time required for manual analysis and years of training needed to attain proficiency - prevent clinicians from leveraging the full capabilities of musculoskeletal MRI analysis in any kind of scalable clinical workflow.

This challenge is exactly why AI-powered MRI analytical tools are revolutionizing the field, offering AI-based analytical algorithms that can give providers with a wide range of experience   objective, precise data expeditiously and at scale.

A recently published scoping review in Skeletal Radiology details how artificial intelligence and deep-learning algorithms are transforming musculoskeletal radiology, the numerous applications for these tools throughout the clinical workflow, and the areas where more studies are needed to expand their widespread adoption in clinical use.

We summarize key points from the article below; if you want the full review, you can find that here.

Going Beyond the Scan: How AI Can Help with Diagnosis

The use of MRI is a growing and incredibly effective method to diagnose musculoskeletal injuries. Many promising techniques developed to unlock the full potential of highly detailed MRI data are supported by scientific studies, centering on the binary classification of the presence or absence of a feature. Yet what is missing from some of these techniques is the ability to  precisely characterize a musculoskeletal abnormality. MRIs hold great value when they can account for additional factors such as tear location, configuration, size, and the state of an injured ligament, tendon, or meniscus, something that is critical for significant clinical decisions, including electing surgery and understanding outcomes.

For musculoskeletal MRI interpretation to add more value in clinical use, innovations for determining an abnormality’s presence or absence must be enhanced to include:

• Comprehensive abnormality characterizations and quantification.
• Patient-centric information and big data contrasts that can predict related injuries, enforce treatment selection, and deliver accurate prognoses.

With artificial intelligence, patient scans can be compared with vast knowledge bases of muscle segmentation, tissue connection, and disease markers. Thanks to deep-learning algorithms, AI-powered image analysis can "see and measure" what the human eye cannot - i.e. intricate connections (or lack thereof) among tissues and the probable causes of dysfunction.

Improving Patient Outcomes

Diagnosis is only part of the overall treatment plan — and misdiagnosis or underdiagnosis can not only derail progress but also exacerbate issues. Due to limitations in technology, it has not   always been possible or feasible to obtain a big picture, yet detailed data for an injured area and its surrounding structures.

Indeed, one limitation of traditional MRI-based analytical approaches is that they may not  identify or quantify critical subtleties about the tears in muscle tissue. Everything from tear length and location to overall tendon integrity and surrounding muscle volumes can impact patient outcomes.

By contrast, cutting-edge deep-learning MRI diagnostic tools are yielding far more accurate and detailed imaging results — faster and at scale. Efficiencies gained by AI-based muscle segmentation are enabling clinicians to incorporate advanced technology more into their clinical workflows.

Rotator Cuff Tear Repairs

One clinical area where AI-based tools can significantly impact patient outcomes is rotator cuff tear repairs. At a high level, the shoulder is the second most common site of joint pain and the  second most common referral for a joint MRI in many departments worldwide. Within that population, rotator cuff tears are the most common shoulder injury. MRI provides an excellent non-invasive assessment for these tears and can provide data on a number of characteristics known to influence the degree of success of surgery to relieve pain and retain or restore shoulder mobility.

When making the surgical decision, physicians will consider a number of factors, including two important characteristics from the MRI scan - the grade of muscle bulk atrophy and fatty infiltration. These two metrics are essential to the decision-making process as well as the prognosis.

This is because the preoperative degree of atrophy and fatty infiltration of the rotator cuff muscles are predictors that are inversely associated with the long-term functional outcome of rotator cuff repair. Furthermore, the progression of rotator cuff muscle atrophy and fatty infiltration after surgical repair correlates with poor functional outcomes.

However, the commonly used Goutallier classification scale is largely subjective, can vary between studies and the relevant measurements are time-consuming to obtain. This is where automated deep learning-based quantification can add significant clinical value through improved reproducibility and efficiency gains. In fact, a growing number of studies are indicating the powerful effects and value that using deep learning algorithms for diagnosing rotator cuff tears and segmenting rotator cuff muscles can provide. This is only poised to grow as well.

With a holistic and detailed analysis of the tear itself and its supporting structures, clinicians can make more objective decisions for surgical interventions and personalize treatment plans to improve individual patient outcomes.

In sum, machine-learning MRI algorithms can help PTs, rehab specialists, and physicians develop a patient-specific treatment plan, based on their medical history and unique musculature.

AI as a Tool for Clearer Tissue Imaging and Diagnosis

Beyond a specific use case like rotator cuff injury, AI-powered MRI analysis tools can greatly assist providers in a wide range of clinical decision-making instances. By minimizing the margin of error and generating advanced details about the affected musculoskeletal structure, these technologies allow them to create a truly personalized and progressive care plan. As importantly, the rapid analysis makes this process much more efficient and scalable than traditional MRI-based diagnostic approaches.

The more data these platforms analyze, the more the AI learns about human anatomy and how injuries, surgeries, and unique structures affect musculoskeletal performance. These data will become ever richer — and therefore invaluable clinically and for research purposes. With the ability to efficiently leverage deep MRI insights, human performance specialists across the global healthcare landscape can develop true precision programs to optimize muscle strength and joint integrity for maximum performance and reduction in the risk of injury.

Wrapping Up

As shown in Skeletal Radiology’s review of deep-learning imaging tools, artificial intelligence offers tremendous potential for the field of sports medicine. In addition to streamlining and improving current image-based diagnostics, AI will continually learn more about human physiology to give scientists, clinicians, and providers even more insight into the human body. The data produced will further improve our understanding of muscle segmentation, complementarity, and optimal function, even with the effects of aging and injury.

By leveraging these powerful data, we can help athletes perform at the most advanced levels as well as help them have longer and more successful careers thanks to early injury detection and greater injury management programs and protocols.