Machine Learning and Statistical Prediction of Pitching Arm Kinetics

Neck range of motion prognostic factors in association with shoulder and elbow injuries in professional baseball pitchers

BACKGROUND

The authors observed an association between cervical spine mobility and arm injury risk in baseball players; however, there is a need to assess the generalizability of cervical measurement data. Assessing the downstream associations of cervical dysfunction on shoulder and elbow injuries can inform clinical interventions to help reduce future arm injuries. The purpose of this study was to assess the generalizability of neck range of motion measures as arm injury prognostic factors in professional baseball pitchers.

METHODS

A prospective cohort of professional baseball pitchers in one Major League Baseball Organization was studied. Pitchers underwent preseason neck range of motion including cervical flexion, extension, rotation, lateral flexion, and the flexion-rotation test, and were followed for the season. The outcome was the occurrence of a shoulder or elbow injury. A Cox proportional hazards analysis was performed and reported as hazard ratios with 95% confidence intervals (95% CIs).

RESULTS

A total of 88 pitchers were included (age: 24.2 [2.4] years; left-handed: 21 [23%]; fastball velocity: 92.3 [1.8]), with 15,942 athlete exposure days collected over the season. Pitcher neck range of motion was assessed (flexion: 64° [10°]; extension: 69° [11°]; difference in lateral flexion: -1° [7°]; difference in neck rotation: -2° [9°]; difference in cervical flexion-rotation test: -1° [7°]). A total of 20 arm injuries (shoulder: 9 [10%]; elbow: 11 [13%]; combined rate: 1.3 [95% CI: 0.7, 1.7] per 1000 exposure days) were suffered by pitchers during the season. For every degree increase in the difference in dominant (rotating to dominant shoulder) vs. nondominant (rotating to nondominant shoulder) neck rotation, there was a 4-fold increase in arm injury hazard (hazard ratio: 4.0 [95% CI: 1.1, 13.9], P = .031). No other neck measurements demonstrated prognostic value.

CONCLUSIONS

A deficit in dominant vs. nondominant neck rotation was prognostic for a pitching arm injury. However, the cervical rotation test did not have prognostic value in this sample. Further research is required to assess the generalizability and scalability of neck range of motion assessment in relation to baseball shoulder and elbow injuries across different competition levels.

Collegiate baseball pitchers demonstrate a relationship between ball velocity and elbow varus torque, both within and across pitchers

High elbow varus torque during baseball pitching has been identified as a potential cause of ulnar collateral ligament injury in baseball pitchers. In general, elbow varus torque increases as ball velocity increases across pitchers. However, studies incorporating within-subject analyses report that not all professional pitchers have a positive relationship between elbow varus torque and ball velocity (T-V relationship). It remains unknown whether collegiate pitchers show the same trend as professionals in their T-V relationships. The current study investigated the T-V relationship of collegiate pitchers focusing on both across and within pitchers. Division 1 collegiate pitchers (n = 81) were assessed for elbow torque and ball velocity during pitching. Both across- and within-pitcher T-V relationships were significant (p < 0.05) using linear regression. However, more variance in elbow varus torque was explained using the within-pitcher relationship (R2 = 0.29) than the across-pitcher relationship (R2 = 0.05). Of the 81 pitchers, nearly half (n = 39) had significant T-V relationships, while the other half (n = 42) did not. Our findings indicate that the T-V relationship should be assessed on an individual basis as T-V is pitcher-specific.

A Comparison of Throwing Arm Kinetics and Ball Velocity in High School Pitchers With Overall Fast and Overall Slow Cumulative Joint and Segment Velocities

BACKGROUND

Individual maximum joint and segment angular velocities have shown positive associations with throwing arm kinetics and ball velocity in baseball pitchers.

PURPOSE

To observe how cumulative maximum joint and segment angular velocities, irrespective of sequence, affect ball velocity and throwing arm kinetics in high school pitchers.

STUDY DESIGN

Descriptive laboratory study.

METHODS

High school (n = 55) pitchers threw 8 to 12 fastball pitches while being evaluated with 3-dimensional motion capture (480 Hz). Maximum joint and segment angular velocities (lead knee extension, pelvis rotation, trunk rotation, shoulder internal rotation, and forearm pronation) were calculated for each pitcher. Pitchers were classified as overall fast, overall slow, or high velocity for each joint or segment velocity subcategory, or as population, with any pitcher eligible to be included in multiple subcategories. Kinematic and kinetic parameters were compared among the various subgroups using t tests with post hoc regressions and multivariable regression models created to predict throwing arm kinetics and ball velocity, respectively.

RESULTS

The lead knee extension and pelvis rotation velocity subgroups achieved significantly higher normalized elbow varus torque (P = .016) and elbow flexion torque (P = .018) compared with population, with equivalent ball velocity (P = .118). For every 1-SD increase in maximum pelvis rotation velocity (87 deg/s), the normalized elbow distractive force increased by 4.7% body weight (BW) (B = 0.054; β = 0.290; P = .013). The overall fast group was older (mean ± standard deviation, 16.9 ± 1.4 vs 15.4 ± 0.9 years; P = .007), had 8.9-mph faster ball velocity (32.7 ± 3.1 vs 28.7 ± 2.3 m/s; P = .002), and had significantly higher shoulder internal rotation torque (63.1 ± 17.4 vs 43.6 ± 12.0 Nm; P = .005), elbow varus torque (61.8 ± 16.4 vs 41.6 ± 11.4 Nm; P = .002), and elbow flexion torque (46.4 ± 12.0 vs 29.5 ± 6.8 Nm; P < .001) compared with the overall slow group. A multiregression model for ball velocity based on maximum joint and segment angular velocities and anthropometrics predicted 53.0% of variance.

CONCLUSION

High school pitchers with higher maximum joint and segment velocities, irrespective of sequence, demonstrated older age and faster ball velocity at the cost of increased throwing shoulder and elbow kinetics.

CLINICAL RELEVANCE

Pitchers and coaching staff should consider this trade-off between faster ball velocity and increasing throwing arm kinetics, an established risk factor for elbow injury.

Artificial intelligence in shoulder and elbow surgery: overview of current and future applications

Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery.

Assessment of Pulmonary Arteries Hemodynamics and Its Relationship With Cardiac Remodeling and Myocardial Fibrosis in Athletes With Four-Dimensional Flow MRI

BACKGROUND

Exercise-induced cardiac remodeling (CR) and myocardial fibrosis (MF) can increase cardiovascular risk in athletes. Early detection of pulmonary arterial hemodynamics parameters among athletes may be beneficial in optimizing the frequency of clinical follow-ups.

PURPOSE

To analyze the hemodynamics of pulmonary arteries and its relationship with CR and MF in athletes using four-dimensional (4D) flow MRI.

STUDY TYPE

Prospective.

POPULATION

One hundred twenty-one athletes (median age, 24 years; mean exercise per week 10 hours, for mean of 5 years) and twenty-one sedentary healthy controls (median age, 25 years; exercise per week <3 hours, irregular pattern).

FIELD STRENGTH/SEQUENCE

True fast imaging with steady state free precession, time-resolved 3D Cartesian phase-contrast, and phase sensitive inversion recovery late gadolinium enhancement sequences at 3.0 T.

ASSESSMENT

CR was defined as any cardiac parameters exceeding the 99th percentile upper reference limits, encompassing ventricular function, bi-atrium and bi-ventricle diameters, and ventricular wall thickness. MF was visually evaluated by three independent radiologists. 4D flow parameters were assessed in the main, right, and left pulmonary arteries (MPA, RPA, and LPA, respectively) and compared between different groups. Four machine learning (ML) models were developed to differentiate between athletes with and without CR and/or MF.

STATISTICAL TESTS

Univariate analysis was used to compare groups. Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the ML models.

RESULTS

Athletes had significantly higher WSSmax in the MPA, RPA, and LPA than controls. Athletes with CR and/or MF (N = 30) had significantly lower RPmax from MPA to RPA than those without (N = 91). Among the ML models, the gradient boosting machine model had the highest performance, with an AUC of 0.90.

CONCLUSION

The pulmonary arterial hemodynamics parameters could differentiate CR and/or MF in athletes, which may be potential to assist in optimizing frequency of follow-up.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery

In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These "markerless" systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.

Real-time sports injury monitoring system based on the deep learning algorithm

In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detection is performed to capture human movements, followed by human joint detection. Polynomial fitting analysis is used to extract joint motion patterns, and the average of training data is calculated as a reference point. The raw data is then normalized to adjust position and direction, and dimensionality reduction is achieved through singular value decomposition to enhance processing efficiency and model training speed. A support vector machine classifier is used to classify and identify the processed data. The experimental section monitors sports injuries and investigates the accuracy of the system's monitoring. Compared to mainstream models such as Random Forest and Naive Bayes, the SVM utilized demonstrates good performance in accuracy, sensitivity, and specificity, reaching 94.2%, 92.5%, and 96.0% respectively.

An Overview of Machine Learning Applications in Sports Injury Prediction

Use injuries, i.e., injuries caused by repetitive strain on the body, represent a serious problem in athletics that has traditionally relied on historic datasets and human experience for prevention. Existing methodologies have been frustratingly slow at developing higher precision prevention practices. Technological advancements have permitted the emergence of artificial intelligence and machine learning (ML) as promising toolsets to enhance both injury mitigation and rehabilitation protocols. This article provides a comprehensive overview of recent advances in ML techniques as they have been applied to sports injury prediction and prevention. A comprehensive literature review was conducted searching PubMed/Medline, Institute of Electrical and Electronics Engineers (IEEE)/Institute of Engineering and Technology (IET), and ScienceDirect. Ovid Discovery and Google Scholar were used to provide additional aggregate results and a grey literature search. A focus was placed on papers published from 2017 to 2022. Algorithms of interest were limited to K-Nearest Neighbor (KNN), K-means, decision tree, random forest, gradient boosting and AdaBoost, and neural networks. A total of 42 original research papers were included, and their results were summarized. We conclude that given the current lack of open source, uniform data sets, as well as a reliance on dated regression models, no strong conclusions about the real-world efficacy of ML as it applies to sports injury prediction can be made. However, it is suggested that addressing these two issues will allow powerful, novel ML architectures to be deployed, thus rapidly advancing the state of this field, and providing validated clinical tools.

Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review

Background

Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature.

Methods

A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff). All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both).

Results

A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation.

Conclusion

Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications.

Kinematic and kinetic comparison between preprofessional pitchers from the Dominican Republic and the United States

Introduction

Pitching biomechanical efficiency is defined as the association between pitch velocity and arm kinetics. Pitching mechanics inefficiency, an increase in arm kinetics without the resultant increase in pitch velocity, can lead to increased arm strain, increasing arm injury risk. The purpose of this study was to compare arm kinetics, elbow varus torque and shoulder force, in preprofessional United States (US) and Dominican Republic (DR) pitchers. Kinematics that are known to influence elbow varus torque and shoulder force as well as a representative of pitch velocity (hand velocity) were also compared.

Methods

A retrospective review was performed on baseball pitchers from the DR and US who participated in biomechanical evaluations conducted by the University biomechanics laboratory personnel. Three-dimensional biomechanical analyses were performed on US (n = 37) and DR (n = 37) baseball pitchers. Potential differences between US and DR pitchers were assessed through analysis of covariance with 95% confidence intervals [95% confidence Interval (CI)].

Results

Preprofessional DR pitchers experienced increased elbow varus torque compared with their US counterparts [DR: 7.5 (1.1); US: 5.9 (1.1) %BWxH; Beta: -2.0 (95% CI: -2.7, -1.2) %BWxH], despite throwing fastballs with slower hand velocity [DR: 3,967.1 (939.4); US: 5,109.1 (613.8) °/s; Beta: 1,129.5 (95% CI: 677.5, 1,581.4) °/s]. DR and US pitchers demonstrated similar shoulder force [DR: 136.8 (23.8); US: 155.0 (25.7); Beta: 0.4 (95% CI: -1.2, 19.7) %BW].

Discussion

Increased elbow varus torque although decreased hand velocity suggests inefficient pitching mechanics among DR pitchers. Inefficient pitching mechanics and increased elbow torque should be considered when developing training programs and pitching plans for professional pitchers from the Dominican Republic.

Supervised machine learning and associated algorithms: applications in orthopedic surgery

Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.

Initial kinematic chain injuries increase hazard of subsequent arm injuries in professional baseball pitchers

BACKGROUND

Currently, there are few studies that have evaluated the relationship between a lower extremity or trunk injury (kinematic chain) and subsequent arm injury. The purpose of this study was (1) to investigate the relationship between initial kinematic chain (lower extremity or trunk) injury and subsequent arm injury; and (2) to investigate the relationship between initial shoulder or elbow injury and subsequent arm injury.

METHODS

A 7-year prospective injury risk study was conducted with Minor League Baseball pitchers. Pitches, pitching appearances, athlete exposures (AEs), and arm injuries (≥1-day time loss) were documented throughout the season. Cox survival analyses with 95% confidence intervals (95% CIs) were performed. Confounders controlled for included age, body mass index, arm dominance, pitching role, previous arm injury, number of pitching appearances, and seasonal pitch load.

RESULTS

A total of 297 pitchers participated (total player days = 85,270). Arm injury incidence was 11.4 arm injuries/10,000 AEs, and kinematic chain incidence was 5.2 injuries/10,000 AEs. Pitchers who sustained a kinematic chain injury demonstrated a greater hazard (2.6 [95% CI: 1.2, 5.6], P = .019) of sustaining an arm injury. Pitchers who sustained an initial shoulder injury demonstrated a greater hazard (9.3 [95% CI: 1.1, 83], P = .047) of sustaining a subsequent shoulder or elbow injury compared with pitchers who sustained an initial elbow injury.

CONCLUSIONS

Pitchers who sustained an initial lower extremity or trunk injury demonstrated an increased subsequent arm injury hazard compared with pitchers who did not. Pitchers who sustained an initial shoulder injury demonstrated a greater hazard of sustaining a subsequent arm injury compared with pitchers who sustained an initial elbow injury. However, this secondary analysis should be interpreted with caution. Clinicians should monitor risk with workload accumulation, which may be related to pitching compensatory strategies in a fatigued state. Pitchers who sustain a shoulder injury should be evaluated and perform both shoulder and elbow rehabilitation strategies before return to sport.