A design tool to estimate maximum acceptable manual arm forces for above-shoulder work.

There is a need for design criteria for above-shoulder work to prevent shoulder fatigue and supraspinatus injuries. A tool is developed to estimate maximum acceptable manual arm forces for above-shoulder work based on 25th % female strength with adjustments for supraspinatus tendon impingement and shoulder fatigue. The tool equations are presented along with tables of maximum acceptable manual arm forces in 77 locations in the 3 D space above the shoulder that accommodates a 50th % female reach. The largest acceptable anterior force, 140.3 N, occurs at shoulder height, 0.5 m anterior to the shoulder. The largest acceptable superior force, 84.4 N, occurs at shoulder height, 0.1 m anterior and 0.2 m medial to the shoulder. The new tool provides design criteria for arm exertions at a higher level of detail than prior ergonomic tools, making it useful for engineers. Based on sensitivity analyses, the tool is robust to parameter assumptions. Practitioner summary: Above-shoulder work is associated with increased risk for shoulder fatigue and injuries. A new tool is developed that estimates maximum acceptable manual arm forces for work at or above shoulder height. The tool can be used to design acceptable above-shoulder work so that it can be accomplished by most workers. Abbreviations: AFF: arm force field; AP: anterior/posterior; DC: duty cycle; GH: glenohumeral angle; HT: humerothoracic angle; LM: lateral/medial; MAE: maximum acceptable effort; MAF: maximum acceptable force; MAS: manual arm strength; MVC: maximum voluntary contraction; N: newton; OCRA: occupational repetitive action; R: reach distance; RMS: root means square; RULA: rapid upper limb assessment; SF: scale factor; SI: superior/inferior; ST: scapulothoracic angle; T: thoracicKEY POINTSA new design tool is introduced that estimates maximum acceptable hand forces for specific locations above the shoulder.This above-shoulder tool is based on a 50th percentile female anthropometry and 25th percentile female manual arm strength.These base strengths are multiplied by scaling factors that adjust for subacromial impingement and fatigue.The tool was shown to be robust based on sensitivity analysis.

Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears.

BACKGROUND: Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a deep learning-based model for automatically diagnosing supraspinatus tears (STs) using shoulder MRI and validated its feasibility in clinical practice. MATERIALS AND METHODS: A total of 701 shoulder MRI data (2804 images) were retrospectively collected for model training and internal test. An additional 69 shoulder MRIs (276 images) were collected from patients who underwent shoulder arthroplasty and constituted the surgery test set for clinical validation. Two advanced convolutional neural networks (CNN) based on Xception were trained and optimized to detect STs. The diagnostic performance of the CNN was evaluated according to its sensitivity, specificity, precision, accuracy, and F1 score. Subgroup analyses were performed to verify its robustness, and we also compared the CNN's performance with that of 4 radiologists and 4 orthopedic surgeons on the surgery and internal test sets. RESULTS: Optimal diagnostic performance was achieved on the 2D model, from which F1-scores of 0.824 and 0.75, and areas under the ROC curves of 0.921 (95% confidence interval, 0.841-1.000) and 0.882 (0.817-0.947) were observed on the surgery and internal test sets. For the subgroup analysis, the 2D CNN model demonstrated a sensitivity of 0.33-1.000 and 0.625-1.000 for different degrees of tears on the surgery and internal test sets, and there was no significant performance difference between 1.5 and 3.0 T data. Compared with eight clinicians, the 2D CNN model exhibited better diagnostic performance than the junior clinicians and was equivalent to senior clinicians. CONCLUSIONS: The proposed 2D CNN model realized the adequate and efficient automatic diagnoses of STs, which achieved a comparable performance of junior musculoskeletal radiologists and orthopedic surgeons. It might be conducive to assisting poor-experienced radiologists, especially in community scenarios lacking consulting experts.

Risk Factors for Supraspinatus Tears: A Meta-analysis of Observational Studies.

BACKGROUND: The pathogenesis of rotator cuff tears remains unclear, and there is a lack of high-quality evidence-based research on the risk factors for supraspinatus tears. PURPOSE: To explore 10 potential risk factors for supraspinatus muscle tears. STUDY DESIGN: Systematic review; Level of evidence, 3. METHODS: This review was conducted according to the MOOSE (Meta-analysis Of Observational Studies in Epidemiology) guidelines. PubMed, Embase, and Web of Science were searched for cohort, case-control and cross-sectional studies published before January 2021 on supraspinatus tears. The following potential risk factors were analyzed: age, body mass index, male sex, female sex, arm dominance, diabetes mellitus, smoking, hypertension, thyroid disease, and the critical shoulder angle (CSA). Risk ratios (RRs) or weighted mean differences (WMDs) of related risk were calculated. The Egger test was used to evaluate publication bias. RESULTS: A total of 9 articles from 8 countries were included; among the 3240 patients, 687 were included in the supraspinatus tear group, and 2553 were included in the nonsupraspinatus tear group. The meta-analysis showed that older age (WMD, 3.36 [95% confidence interval (CI), 0.53-6.20]; P = .02), male sex (RR, 0.87 [95% CI, 0.78-0.97]; P = .01), smoking (RR, 2.21 [95% CI, 1.56-3.14]; P < .00001), diabetes (RR, 1.67 [95% CI, 1.03-2.70]; P = .04), hypertension (RR, 1.51 [95% CI, 1.16-1.97]; P = .002), and the CSA (WMD, 2.25 [95% CI, 1.39-3.12]; P < .00001) were risk factors for supraspinatus tears. CONCLUSION: Older age, male sex, smoking, diabetes, hypertension, and a higher CSA were found to be risk factors for supraspinatus tears in this meta-analysis review. Identifying risk factors for supraspinatus tears early can help clinicians identify these high-risk patients and choose appropriate treatments.

Associations between shoulder symptoms and concomitant pathology in patients with traumatic supraspinatus tears.

BACKGROUND: The association between concomitant pathologic characteristics and preoperative symptoms in patients identified as eligible for surgical rotator cuff repair has been sparsely evaluated. The purpose was to explore the associations between preoperative shoulder symptoms and additional structural pathology or injuries identified during surgery in patients with traumatic supraspinatus tears. METHODS: This was a cross-sectional study including patients with traumatic supraspinatus tears. Preoperatively, patients reported pain and disability using a numeric pain rating scale from 0 to 10 and the Western Ontario Rotator Cuff Index. During surgery, the presence of prespecified structural injuries and pathologies including a full-thickness or partial supraspinatus tear, infraspinatus tear, subscapularis tear, hooked acromion, acromioclavicular joint osteoarthritis, biceps tendon pathology, labral tear, and cartilage lesion was recorded. Linear regression and analysis of covariance were used to assess associations. RESULTS: A total of 87 patients (52 male patients, 60%) were included (mean age, 60 years; standard deviation, 9.2 years). Of these patients, 69 (79%) had a full-thickness supraspinatus tear and 18 (21%) had a partial-thickness tear. Concomitant structural pathology was found in 79 patients (91%). No association was found between the number of structural shoulder pathologies and preoperative numeric pain rating scale or Western Ontario Rotator Cuff Index score, and no particular concomitant pathology was associated with worse patient-reported symptoms. CONCLUSION: Pathology of the infraspinatus and subscapularis and other structural joint pathologies concomitant with supraspinatus tears were not correlated with preoperative self-reported pain and disability in patients scheduled to undergo rotator cuff surgery, suggesting that concomitant pathology adds little to the symptoms in patients with a traumatic supraspinatus tear.