Development of Murine Anterior Interbody and Posterolateral Spinal Fusion Techniques.

BACKGROUND: Multiple animal models have previously been utilized to investigate anterior fusion techniques, but a mouse model has yet to be developed. The purpose of this study was to develop murine anterior interbody and posterolateral fusion techniques. METHODS: Mice underwent either anterior interbody or posterolateral spinal fusion. A protocol was developed for both procedures, including a description of the relevant anatomy. Samples were subjected to micro-computed tomography to assess fusion success and underwent biomechanical testing with use of 4-point bending. Lastly, samples were fixed and embedded for histologic evaluation. RESULTS: Surgical techniques for anterior interbody and posterolateral fusion were developed. The fusion rate was 83.3% in the anterior interbody model and 100% in the posterolateral model. Compared with a control, the posterolateral model exhibited a greater elastic modulus. Histologic analysis demonstrated endochondral ossification between bridging segments, further confirming the fusion efficacy in both models. CONCLUSIONS: The murine anterior interbody and posterolateral fusion models are efficacious and provide an ideal platform for studying the molecular and cellular mechanisms mediating spinal fusion. CLINICAL RELEVANCE: Given the extensive genetic tools available in murine disease models, use of fusion models such as ours can enable determination of the underlying genetic pathways involved in spinal fusion.

Pitch-classifier model for professional pitchers utilizing 3D motion capture and machine learning algorithms.

Introduction: A pitcher's ability to achieve pitch location precision after a complex series of motions is of paramount importance. Kinematics have been used in analyzing performance benefits like ball velocity, as well as injury risk profile; however, prior utilization of such data for pitch location metrics is limited. Objective: To develop a pitch classifier model utilizing machine learning algorithms to explore the potential relationships between kinematic variables and a pitcher's ability to throw a strike or ball. Methods: This was a descriptive laboratory study involving professional baseball pitchers (n = 318) performing pitching tests under the setting of 3D motion-capture (480 Hz). Main outcome measures included accuracy, sensitivity, specificity, F1 score, positive predictive value (PPV), and negative predictive value (NPV) of the random forest model. Results: The optimized random forest model resulted in an accuracy of 70.0 %, sensitivity of 70.3 %, specificity of 48.5 %, F1 equal to 80.6 %, PPV of 94.3 %, and a NPV of 11.6 %. Classification accuracy for predicting strikes and balls achieved an area under the curve of 0.67. Kinematics that derived the highest % increase in mean square error included: trunk flexion excursion(4.06 %), pelvis obliquity at foot contact(4.03 %), and trunk rotation at hand separation(3.94 %). Pitchers who threw strikes had significantly less trunk rotation at hand separation(p = 0.004) and less trunk flexion at ball release(p = 0.003) compared to balls. The positive predictive value for determining a strike was within an acceptable range, while the negative predictive value suggests if a pitch was determined as a ball, the model was not adequate in its prediction. Conclusions: Kinematic measures of pelvis and trunk were crucial determinants for the pitch classifier sequence, suggesting pitcher kinematics at the proximal body segments may be useful in determining final pitch location.