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Risk Factor Analysis for Predicting the Onset of Rotator Cuff Calcific Tendinitis Based on Artificial Intelligence
Shengtao Dong1, Jie Li2, Haozong Zhao2
1Department of Spine Surgery, The Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China.
Computational Intelligence and Neuroscience
|April 22, 2022
Summary
Female gender, hyperlipidemia, diabetes mellitus, and hypothyroidism are key risk factors for rotator cuff calcific tendinitis (RCCT). Men with these conditions and women with diabetes mellitus require targeted medical attention for this common shoulder disorder.
Area of Science:
- Orthopedics
- Medical Diagnostics
- Epidemiology
Background:
- Symptomatic rotator cuff calcific tendinitis (RCCT) is a prevalent shoulder ailment.
- Limited understanding of RCCT onset necessitates thorough clinical investigation.
- Artificial intelligence aids in advancing clinical practice for musculoskeletal disorders.
Purpose of the Study:
- To identify independent risk factors associated with the onset of symptomatic RCCT.
- To explore gender-specific risk factors for symptomatic RCCT.
- To enhance early diagnosis and treatment strategies for RCCT.
Main Methods:
- Retrospective analysis of clinical data from 2008-2020 for RCCT patients.
- Standardized questionnaires and radiographs used for diagnosis confirmation.
- Stepwise logistic regression analysis applied to identify significant risk factors.
Main Results:
- Symptomatic RCCT diagnosed in 12.05% of shoulder discomfort cases (237/1967).
- Higher prevalence of female gender and comorbidities in the RCCT cohort.
- Independent risk factors identified: female gender, hyperlipidemia, diabetes mellitus, and hypothyroidism.
Conclusions:
- Female gender, hyperlipidemia, diabetes mellitus, and hypothyroidism are independent predictors of symptomatic RCCT.
- Men with hyperlipidemia, diabetes mellitus, and hypothyroidism face high risk; women with diabetes mellitus warrant increased medical attention.
- Individualized risk stratification and AI integration can improve prediction and early treatment of RCCT.
