Basic Military Training Injury Risk Identification using Sparta AI (IRISai-BMT)

Objective: Sparta Science, partnered with the 37th Training Wing and AFLCMC/WNU, intends to reduce the 31% of Basic Military Training (BMT) trainees who experience musculoskeletal injury (MSKI) and 20% of injured trainees who do not graduate BMT. Approximately 70% of these MSKI are overuse injuries (O-MSKI) that are preventable. Medical attrition from BMT costs the DAF up to $46 million annually. By providing healthcare providers scalable, machine-learning enhanced, objective metrics indicating trainee risk for O-MSKI, providers can prophylactically intervene to reduce the risk of training injuries, improve on-time BMT graduation rates, and enhance DAF mission readiness. R&D Effort: Within the first week of each BMT class, Sparta will collect de-identified feedback and movement signatures from all trainees using its Sparta Movement Health Platform software, with follow-up scans at BMT mid and end and if an injury occurs. Through R&D using ML model engineering, the collected force plate data and survey responses will enable the “training” and testing of ML models on the specific aspects of BMT O-MSKI, resulting in an artificial intelligence (AI) and ML-enabled software to aid in early identification of injury risks allowing healthcare professionals to intervene before injury impacts training.