Springbok Secures $1.7M NIH Grant for Advanced Rotator Cuff Analysis

Springbok Analytics, a firm specializing in life sciences muscle analytics, has been awarded a $1.7 million Phase II Small Business Innovation Research (SBIR) grant by the National Institutes of Health (NIH). The funding will be utilized to refine a machine learning algorithm designed to autonomously identify shoulder muscle issues. Collaborating with academic and medical institutions such as the University of Virginia, the San Antonio Orthopedic Group, and the University of Wisconsin, Springbok aims to develop an AI-powered system that offers a precise and unbiased method for assessing rotator cuff (RC) muscles, thereby enhancing the prediction of surgical results.

The current surgical evaluation standard, the Goutallier classification system, faces limitations due to its qualitative nature and lack of predictive capabilities for surgical outcomes. In contrast, quantitative 3D measurements of muscle atrophy and fatty replacement have been found to be superior indicators of how surgeries will fare. However, these methods have not been widely adopted due to time-consuming processing and the need for specialized MRI techniques. Springbok's innovative approach using AI and advanced analysis methods, as highlighted in studies published in Radiology: Artificial Intelligence and Scientific Reports, addresses these challenges.

Lara Riem, Springbok's Director of AI and Data Science, expressed enthusiasm for the commencement of Phase II, building upon the successful development and validation of their deep-learning algorithm. This algorithm automates the quantification of rotator cuff muscle and fatty infiltration from clinical scans. During Phase I, they established a comprehensive digital database of both healthy and pathological rotator cuff scans, devising a novel method to handle variability in scan coverage. This innovation has led to the creation of key muscle metrics that can be accurately and automatically derived from MR images.

Springbok's proprietary muscle analytics technology generates a detailed analysis of the rotator cuff from standard MRIs, enhancing the clinical workflow. The 3D volumetric measurement it provides offers a thorough and sensitive evaluation of RC conditions. The result is a clear, objective, and actionable report that benefits both healthcare providers and patients, streamlining the surgical decision-making process.

Silvia Blemker, Springbok's Co-Founder and Chief Science Officer, emphasized the importance of a 3D understanding of a patient's rotator cuff musculature and the objective measurement of muscle atrophy and fat infiltration for proper treatment planning. She noted that Phase I demonstrated the practicality and value of their new analysis within clinical settings. The next phase aims to further develop an automatic segmentation and analysis technology for the rotator cuff, with the potential to benefit millions and improve upon current subjective scoring methods.

Upon completing Phase II, Springbok intends to seek FDA 510(k) market clearance, with ambitions to significantly boost the precision of shoulder pathology assessments and the diagnosis and treatment of shoulder injuries. This advancement could lead to improved outcomes for costly orthopedic procedures and possibly the prevention of unnecessary surgeries. Springbok Analytics is determined to set a new standard for understanding human musculature and to enhance clinical diagnosis, injury management, and human performance through cutting-edge AI technologies.

Springbok Analytics is dedicated to improving health and performance outcomes through its technology, which analyzes MRI data to produce personalized 3D visualizations of muscle health. This technology not only enhances the assessment and monitoring of treatments but also adds value to research and diagnostics by providing a detailed view of musculoskeletal health, including precise measurements of individual muscle volume and quality, fat infiltration, asymmetries, and the condition of scar tissue, edema, and tendons.