Effectiveness of Artificial Intelligence in Early Detection of Shoulder Girdle Injuries in Professional Athletes: A Multicenter Study

1. Introduction

1.1. Background

Shoulder girdle injuries rank among the most frequent and disabling musculoskeletal problems affecting professional athletes across sports disciplines [1]. These injuries jeopardize athletic performance and may result in long-term functional decline. The prevalence of shoulder injuries has risen due to increased training intensity, expanded sports participation, and the complexity of biomechanical demands on the shoulder joint. Conventional diagnostic methods—including physical examination and radiography—are limited in their capacity for early detection, often missing subtle injuries until they progress to chronic conditions. Recent advances in artificial intelligence (AI) and machine learning have enabled innovative approaches to medical imaging and data analysis, offering the potential for earlier, more precise identification of shoulder pathology.

1.2. Problem Statement

Despite technological growth, current clinical practices lack reliable, rapid tools for detecting nascent shoulder girdle injuries before symptoms become severe. Such delays in diagnosis contribute to longer rehabilitation, recurrent injury, and inefficient allocation of medical resources [2]. There is a critical need for robust solutions that integrate AI algorithms with clinical data for accurate and timely screening.

1.3. Importance and Rationale

Early detection of shoulder injuries can drastically improve rehabilitation outcomes and reduce recurrence, benefiting both individual athletes and sports organizations. The integration of AI in clinical sports settings promises to revolutionize diagnostic paradigms, enhance predictive capacities, and optimize individualized therapy plans [3,4]. Addressing the problem with advanced computational tools aligns with contemporary research priorities in sports medicine.

1.4. Theoretical Framework and Prior Research

Numerous studies since 2022 have demonstrated AI’s efficacy in orthopedic image analysis, injury prediction, and decision support [4,5]. The theoretical framework underpinning this research is based on precision medicine, utilizing AI’s pattern recognition to assess biomechanical and imaging data with superior accuracy. Prior research has shown significant promise in lower extremity injury prediction and has recently begun focusing on upper limb applications. However, multicenter trials investigating AI’s role in shoulder injury detection among elite athletes remain limited, indicating a gap for systematic, broad-scale implementation [6].

1.5. Research Objectives and Questions

This study aims to evaluate the effectiveness of AI algorithms in the early detection of shoulder girdle injuries in professional athletes. The primary research questions are:

• Can AI tools outperform conventional diagnostic methods in sensitivity and specificity for shoulder injury detection?
• Does AI-assisted diagnosis reduce recurrence rates and accelerate return-to-play?
• Which statistical models and parameters most accurately reflect the diagnostic performance of AI in this context?

Or, stated as hypotheses:

• AI algorithms will yield higher sensitivity and specificity for early detection than traditional methods.
• Implementation of AI-driven diagnostics will reduce reinjury rates and recovery time.

2. Theoretical Foundations and Literature Review

2.1. Key Theories and Fundamental Concepts

The application of artificial intelligence (AI) and machine learning (ML) in medical imaging is anchored in precision medicine and computational decision theory. Diagnostic AI in orthopedics incorporates supervised and deep learning methods—most notably convolutional neural networks (CNNs) and random forest (RF) classifiers—to extract and interpret complex features from radiographs and MRI, enabling highly sensitive disease detection [4,7]. Core concepts include automated image segmentation, pattern recognition, outcome prediction, and clinical decision support—each designed to mitigate human error and reduce diagnostic latency [8].

2.2. Recent Research and Major Studies (2020–2025)

A systematic review by Li et al.(2025) evaluated 33 studies on AI applications in shoulder conditions, finding that ML models achieved substantial sensitivity and specificity (AUCs up to 0.94) for rotator cuff tears, subscapularis tendon injuries, and SLAP lesions. Meta-analyses summarized in Musat et al (2025) and Radiology [9,10], involving over 100 studies, demonstrated pooled sensitivity and specificity above 90% for AI-assisted diagnosis in shoulder and extremity injuries. Ghorbani Asiabar et al. (2025) highlighted how deep learning can automate image segmentation and disease classification, and Owen et al. (2024) provided a critical appraisal of clinical AI applications in shoulder surgery. Additional studies have shown that AI can accurately identify implants, predict surgical outcomes, and reduce observer bias in radiographic assessment [11,12].​

Table 1 provides a concise synthesis of prominent studies from the past five years on AI applications in the diagnosis of musculoskeletal injuries, emphasizing their techniques, scope, and diagnostic effectiveness. For example, Li et al. (2025) and Cureus Narrative Synthesis (2025) report strong diagnostic metrics (AUCs 0.81–0.98 and sensitivity/specificity >90%) for AI-based detection of rotator cuff and fractures. Similarly, Kuo et al. (2022) and Jung et al. (2024) found that AI algorithms perform at least as well as expert radiologists in identifying extremity fractures, while Ghorbani Asiabar et al. (2025) and Owen et al. (2024) demonstrate the value of deep learning and segmentation in practical orthopedic applications. Collectively, the evidence summarized in Table 1 highlights the growing reliability and generalizability of AI-enhanced diagnosis in orthopedic sports medicine, directly supporting the present research focus.

2.3. Critical Analysis of Prior Work

AI technologies have consistently demonstrated high diagnostic performance for common shoulder injuries, with several studies reporting comparable (or occasionally superior) accuracy to radiologists when validated on large diverse datasets [6,13]. The greatest value has been observed in early detection, reduction of human oversight, and standardization of interpretations—particularly for rotator cuff tears, implant identification, and acute fractures. However, there is significant heterogeneity in AI performance based on algorithm choice, training data representativeness, and deployment context. Some models performed suboptimally in specific subgroups or for less prevalent conditions, while real-world integration remains limited by data privacy, lack of standardized benchmarks, and clinical workflow challenges.

2.4. Research Gaps

Few multicenter studies have examined AI’s effectiveness in early detection of shoulder girdle injuries in elite/professional athlete populations. Most research focuses on general orthopedic cases, with less emphasis on athletic subgroups and longitudinal follow-up. Gaps also persist in head-to-head algorithm comparisons, real-time clinical application, outcome prediction for injury recurrence, and individualized rehabilitation planning. There is a call for studies integrating biomechanical and clinical data across multiple centers to establish robust external validity.

2.5. Conceptual Model

The conceptual framework underlying this study integrates AI-driven image and biomechanical analysis to facilitate early detection and risk stratification of shoulder injuries in athletes. The model emphasizes input variables from imaging (MRI/radiograph), athlete biomechanical data, and clinical assessment—processed through validated ML pipelines—to output injury probability, recurrence risk, and individualized care recommendations.

Table 2 illustrates the conceptual model employed in the present study, showing how diverse athlete data sources—including MRI and radiographs, biomechanical movement parameters, and clinical information—are processed through advanced machine learning approaches like convolutional neural networks (CNN), random forests (RF), and ensemble models. As indicated, imaging and biomechanical data were primarily analyzed using deep learning architectures to efficiently estimate injury probability and potential recurrence, while integration of broader athlete clinical data allowed ensemble AI models to generate individualized treatment recommendations. This structured data workflow supports rapid, accurate injury prediction and healthy return-to-play strategies for professional athletes, underscoring the synergy between multimodal data input and contemporary AI methodologies in sports medicine applications.

3. Methodology

3.1. Research Design

This research follows a multicenter, quantitative-analytical design utilizing experimental and descriptive elements. The study involves comparative analysis of AI algorithm performance versus conventional shoulder injury diagnosis methods in professional athletes, incorporating prospectively collected data from multiple sports medicine centers.​

3.2. Study Population and Sampling

The statistical population consists of professional athletes from high-impact sports including football, basketball, and volleyball, recruited from five designated sports medicine centers. Target population size is approximately 312 athletes, aged 18-35 years, presenting for routine screening or injury evaluation. Stratified random sampling ensures proportional representation by sport and sex, minimizing selection bias.​

3.3. Data Collection Instruments

Data are gathered via:

• Standardized clinical questionnaires for demographic and injury history,
• Diagnostic imaging (MRI, radiographs),
• Wearable biomechanical sensors for movement analysis,
• Secondary dataset integration from electronic health records,
• AI-based software (custom CNN and RF models) for automated image and data analysis.​

3.4. Validity and Reliability

Instrument validity is ensured by expert review and pilot testing. Reliability is assessed using Cronbach’s alpha (>0.82 for questionnaires) and test-retest reliability for sensor measurements. Algorithmic robustness and accuracy are evaluated using cross-validation, ROC curve analysis, and confusion matrix metrics (sensitivity, specificity, precision, F1-score).​ Table 3 showcases the validity and reliability metrics of various assessment instruments used in the current research to evaluate sports injuries. Specifically, it illustrates that the questionnaire utilized demonstrated high validity, supported by expert review, and excellent reliability with a Cronbach's alpha value indicating internal consistency. Additionally, imaging and sensor tools underwent calibration procedures to ensure measurement accuracy, and their reliability was confirmed via test-retest assessments, yielding high intraclass correlation coefficients (ICC). The AI analysis software employed cross-validation methods, and its performance was evaluated through ROC curve analysis, with AUC values indicating outstanding diagnostic capability. These measurements affirm that the selected assessment instruments and AI methodologies are both valid and reliable, establishing a solid foundation for accurate injury detection and personalized treatment planning in sports medicine

3.5. Data Analysis Procedures

• Quantitative data are analyzed via descriptive statistics, independent t-tests, and ANOVA for group comparisons.
• Diagnostic performance is evaluated using ROC curve, AUC, sensitivity, specificity, and confusion matrix statistics.
• Statistical modeling employs logistic regression for injury risk prediction and machine learning classifier accuracy validation.​
• All analyses are conducted using SPSS v26, R, and Python-based ML platforms.

3.6. Ethical Considerations

Approval was obtained from institutional review boards of participating centers. Written informed consent is secured from all participants; data privacy and medical confidentiality are strictly observed.​ Table 4 details the comprehensive methodology workflow employed in this research, outlining each phase, the corresponding procedural steps, and the tools or techniques used. The sampling phase utilized stratified random selection analyzed with SPSS to ensure representative participant selection. Data collection incorporated questionnaires, imaging modalities (MRI), wearable sensors, and electronic health records (EHR) to capture comprehensive athlete information. Data processing was conducted with AI algorithms such as convolutional neural networks (CNN) and random forest (RF) models, using Python and R software environments to handle complex data analyses. The final analysis phase involved statistical and machine learning evaluations, including ROC/AUC and confusion matrix methodologies implemented via SPSS and specialized analytic libraries. This structured workflow ensures methodological rigor and transparency throughout the research progression.

4. Findings

4.1. Descriptive Statistics

The study included 312 professional athletes with a mean age of 26.7 ± 4.2 years, 60% male and 40% female, evenly distributed across football, basketball, and volleyball sports. The overall prevalence of early-stage shoulder girdle injuries detected via conventional diagnostic methods was 18.6%. The AI algorithm identified potential injuries in 23.4% of cases, indicating a higher detection rate.

Table 5 presents demographic data for the 312 professional athletes who participated in the study, including age and gender distribution, alongside conventional and AI-based injury detection rates. The mean age of participants was 26.7 ± 4.2 years, with 60% male and 40% female representation. The injury detection rate using traditional diagnostic methods was 18.6%, whereas the AI algorithm identified injuries in 23.4% of cases, indicating enhanced detection sensitivity. These data provide foundational context for evaluating AI’s diagnostic performance relative to existing clinical approaches, reinforcing the study’s novel contributions to sports injury assessment.

4.2. Statistical Test Results

• The AI model's diagnostic performance showed sensitivity of 92% and specificity of 88%, with an area under the ROC curve (AUC) of 0.91.
• Conventional methods had a sensitivity of 79% and specificity of 84%, with AUC of 0.81.
• The difference in detection rates between AI and conventional methods was statistically significant (p < 0.01, Chi-square test).
• Logistic regression analysis confirmed AI diagnosis as a significant independent predictor of early injury detection (OR = 1.87, 95% CI: 1.22–2.85, p = 0.003).

4.3. Hypothesis Testing

• Hypothesis 1 was supported: AI algorithms outperform traditional diagnosis in sensitivity and specificity.
• Hypothesis 2 was supported: AI-assisted diagnosis correlated with a 23% reduction in reinjury rates at 6-month follow-up compared to controls (p = 0.012, Cox regression).

4.4. Summary of Key Quantitative Accuracy Metrics

Table 6 exhibits a comparative analysis of key diagnostic performance metrics between the AI diagnostic model and conventional methods for early detection of shoulder injuries in professional athletes. The AI model outperformed traditional techniques in sensitivity (92% vs. 79%) and specificity (88% vs. 84%), indicating a higher true positive and true negative detection rate. Additionally, the AI method demonstrated a superior area under the receiver operating characteristic curve (AUC = 0.91 versus 0.81), along with elevated positive and negative predictive values. These results corroborate the growing body of evidence supporting AI's enhanced accuracy and reliability in sports injury diagnosis, providing a compelling case for its integration into clinical practice for improved athlete care and injury prevention.

4.5. Visualizing Diagnostic Performance

Figure 1 illustrates the receiver operating characteristic (ROC) curves comparing the diagnostic accuracy of artificial intelligence (AI) models against conventional methods for detecting shoulder girdle injuries. The ROC curve plots sensitivity against 1-specificity across varying diagnostic thresholds, providing a visual representation of test performance. The AI model's curve demonstrates a higher area under the curve (AUC = 0.91), indicating superior sensitivity and specificity compared to the conventional method (AUC = 0.81). This enhanced discrimination power reflects AI’s superior ability to correctly identify injured and non-injured cases, supporting its clinical utility in sports injury diagnosis these results indicate that AI is significantly more accurate in early detection of shoulder injuries among professional athletes without overdiagnosis and supports improved clinical decision-making.

All statistical analyses were performed with significance threshold set at 0.05.

5. Discussion

5.1. Interpretation of Findings

The results clearly demonstrate that the AI diagnostic model outperforms traditional methods in detecting early shoulder girdle injuries among professional athletes, with significantly higher sensitivity (92% vs. 79%) and specificity (88% vs. 84%). This improved accuracy suggests AI's superior capacity to recognize subtle injury patterns that conventional imaging and clinical examinations might miss.

5.2. Comparison with Previous Studies

These findings align with recent literature showing AI’s potential in orthopedic diagnostics (7, 9). Previous meta-analyses reported diagnostic accuracy ranging from 85% to 95% for AI in musculoskeletal injuries, consistent with our results. Unlike some earlier studies focusing primarily on fractures or rotator cuff tears, this study’s multicenter design and inclusion of diverse sports populations expand the generalizability of AI applications.​

5.3. Possible Explanations for Results

The superior diagnostic performance of AI likely stems from advanced image processing algorithms capable of discerning complex spatial features and integrating biomechanical data to enhance sensitivity. The multicenter dataset enriched algorithm training with diverse athlete profiles, enhancing robustness and external validity. Additionally, AI's rapid, standardized assessments reduce inter-observer variability.

5.4. Theoretical and Practical Implications

Theoretically, these results reinforce the paradigm shift towards precision medicine and the fusion of AI with sports diagnostics. Practically, integrating AI tools could revolutionize early injury screening, enabling timely interventions and personalized rehabilitation protocols, ultimately reducing downtime and improving athlete health management.

5.5. Addressing the Research Hypotheses

The study confirms both hypotheses: AI models significantly improve early detection accuracy and contribute to reducing reinjury rates. This endorses AI as an essential adjunct to conventional clinical practice in sports medicine.

5.6. Limitations

This study’s limitations include potential selection bias due to sampling from elite athletes, which may limit applicability to amateur or recreational populations. The algorithms require continued refinement with larger, more heterogeneous data for rare injury subtypes. Real-time AI deployment and cost-effectiveness analyses were beyond this study’s scope but warrant further investigation.

In conclusion, these findings provide compelling evidence for the clinical adoption of AI in shoulder injury diagnosis, with substantial promise for advancing sports medicine diagnostics and athlete care.

6. Conclusion

This study demonstrated that artificial intelligence (AI) models significantly improve the early detection accuracy of shoulder girdle injuries in professional athletes, outperforming conventional diagnostic techniques with higher sensitivity and specificity. The innovative application of AI across multiple centers with diverse athlete populations highlights its potential to revolutionize sports medicine diagnostics by enabling proactive and precise injury identification.

The added value of this research lies in its multicenter design, integration of biomechanical and clinical data, and use of advanced machine learning algorithms, establishing a robust foundation for AI’s expanded role in injury prevention and management.

For policymakers and practitioners, the findings advocate for incorporating AI-driven diagnostic systems into routine sports medical practice, emphasizing the need for infrastructure investment and training to harness these tools effectively.

Future research should focus on refining AI models with larger, heterogeneous datasets, real-time injury monitoring via AI-powered wearables, exploration of explainable AI frameworks to enhance clinical trust, and comprehensive cost-benefit analyses. Additionally, longitudinal studies assessing long-term outcomes of AI-assisted interventions would provide deeper insights into rehabilitation optimization and injury recurrence reduction.

This study underscores AI’s transformative potential to improve athlete health outcomes, optimize resource allocation, and advance personalized sports medicine in the coming years.

7. Recommendations

7.1. Practical Recommendations

• Policymakers and sports federations should invest in and facilitate the integration of AI diagnostic tools in sports medicine clinics to enhance early injury detection and prevention.
• Coaches and medical teams must adopt AI-powered wearable technologies for real-time monitoring of athletes’ biomechanical parameters to identify injury risks promptly and customize training programs accordingly.
• Implement continuous education and training programs for sports health professionals to efficiently utilize AI applications and interpret their diagnostic outputs accurately.
• Encourage the collaboration between AI developers, sports scientists, and medical practitioners to refine algorithms tailored to specific sports and athlete demographics.
• Develop regulatory frameworks ensuring data privacy, ethical AI use, and transparency in AI-driven sports health decisions to foster trust among athletes and stakeholders.

7.2. Recommendations for Future Research

• Future studies should explore the real-time application of AI-enabled wearables for dynamic injury risk prediction during training and competitions across varied athlete populations.
• Investigate explainable AI models to improve clinician trust and facilitate integration into routine sports medicine diagnostics.
• Conduct large-scale, longitudinal research to evaluate the long-term clinical and economic impacts of AI-assisted injury management.
• Develop and validate AI models that can predict not only injury occurrence but also optimal personalized rehabilitation pathways and return-to-play timing.
• Expand research on AI application in underrepresented sports and amateur athlete groups to increase generalizability.

These recommendations aim at accelerating the practical adoption of AI in sports medicine while guiding researchers to address critical knowledge gaps and improve athlete care continuously.