Ewha Med J.  2025 Jan;48(1):e6. 10.12771/emj.2025.e6.

Application of deep learning for diagnosis of shoulder diseases in older adults: a narrative review

• Rhee SM

• KMID: 2567553
• DOI: http://doi.org/10.12771/emj.2025.e6

Abstract

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

Keyword

Aged; Computer neural networks; Deep learning; Rotator cuff injuries; Shoulder pain

Figure

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  Fig. 1. Segmentation results corresponding to the rotator cuff tear site. (A) Original MRI images displaying the presence of a rotator cuff tear. (B) The red region represents the area manually labeled by shoulder specialists, while the blue region indicates the area segmented by the proposed deep learning model. Adapted from Lee et al. [18] with CC-BY.

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  Fig. 2. A company utilizes machine learning-based pose estimation technology to measure a patient's range of motion, analyze the patient's current condition based on the results, and assign the most suitable rehabilitation exercises. This figure is used with permission from Itphy, Inc.

Reference

References

1. Gumina S, Kim H, Jung Y, Song HS. Rotator cuff degeneration and healing after rotator cuff repair. Clin Shoulder Elb. 2023; 26(3):323–329. DOI: 10.5397/cise.2023.00430. PMID: 37607856. PMCID: PMC10497920.

2. Lucas J, van Doorn P, Hegedus E, Lewis J, van der Windt D. A systematic review of the global prevalence and incidence of shoulder pain. BMC Musculoskelet Disord. 2022; 23(1):1073. DOI: 10.1186/s12891-022-05973-8. PMID: 36476476. PMCID: PMC9730650.

3. Hodgetts CJ, Leboeuf-Yde C, Beynon A, Walker BF. Shoulder pain prevalence by age and within occupational groups: a systematic review. Arch Physiother. 2021; 11(1):24. DOI: 10.1186/s40945-021-00119-w. PMID: 34736540. PMCID: PMC8567712.

4. Kim YT, Kim TY, Lee JB, Hwang JT. Glenohumeral versus subacromial steroid injections for impingement syndrome with mild stiffness: a randomized controlled trial. Clin Shoulder Elb. 2023; 26(4):390–396. DOI: 10.5397/cise.2023.00346. PMID: 37798841. PMCID: PMC10698124.

5. Mardani-Kivi M, Hashemi-Motlagh K, Darabipour Z. Arthroscopic release in adhesive capsulitis of the shoulder: a retrospective study with 2 to 6 years of follow-up. Clin Shoulder Elb. 2021; 24(3):172–177. DOI: 10.5397/cise.2021.00311. PMID: 34488298. PMCID: PMC8423526.

6. Hones KM, Hao KA, Buchanan TR, Trammell AP, Wright JO, Wright TW, et al. Does preoperative forward elevation weakness affect clinical outcomes in anatomic or reverse total shoulder arthroplasty patients with glenohumeral osteoarthritis and intact rotator cuff? Clin Shoulder Elb. 2024; 27(3):316–326. DOI: 10.5397/cise.2024.00262. PMID: 39138944. PMCID: PMC11393438.

7. Rhee SM, Youn SM, Kim CH, Chang GW, Kim SY, Ham HJ, et al. Rotator cuff repairs with all-suture tape anchors: no difference in outcomes between with or without all-suture tape anchors. Knee Surg Sports Traumatol Arthrosc. 2023; 31(9):4060–4067. DOI: 10.1007/s00167-023-07454-4. PMID: 37226010.

8. Ko YW, Park JH, Youn SM, Rhee YG, Rhee SM. Effects of comorbidities on the outcomes of manipulation under anesthesia for primary stiff shoulder. J Shoulder Elbow Surg. 2021; 30(8):E482–E492. DOI: 10.1016/j.jse.2020.11.007. PMID: 33359399.

9. Malavolta EA, Assunção JH, Gracitelli MEC, Yen TK, Bordalo-Rodrigues M, Ferreira Neto AA. Accuracy of magnetic resonance imaging (MRI) for subscapularis tear: a systematic review and meta-analysis of diagnostic studies. Arch Orthop Trauma Surg. 2019; 139(5):659–667. DOI: 10.1007/s00402-018-3095-6. PMID: 30539284.

10. Elrahim RMA, Embaby EA, Ali MF, Kamel RM. Inter-rater and intra-rater reliability of Kinovea software for measurement of shoulder range of motion. Bull Fac Phys Ther. 2016; 21(2):80–87. DOI: 10.4103/1110-6611.196778.

11. Grauhan NF, Niehues SM, Gaudin RA, Keller S, Vahldiek JL, Adams LC, et al. Deep learning for accurately recognizing common causes of shoulder pain on radiographs. Skeletal Radiol. 2022; 51(2):355–362. DOI: 10.1007/s00256-021-03740-9. PMID: 33611622. PMCID: PMC8692302.

12. Ro K, Kim JY, Park H, Cho BH, Kim IY, Shim SB, et al. Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI. Sci Rep. 2021; 11(1):15065. DOI: 10.1038/s41598-021-93026-w. PMID: 34301978. PMCID: PMC8302634.

13. Shariatnia MM, Ramazanian T, Sanchez-Sotelo J, Maradit Kremers H. Deep learning model for measurement of shoulder critical angle and acromion index on shoulder radiographs. JSES Rev Rep Tech. 2022; 2(3):297–301. DOI: 10.1016/j.xrrt.2022.03.002. PMID: 37588867. PMCID: PMC10426517.

14. Taghizadeh E, Truffer O, Becce F, Eminian S, Gidoin S, Terrier A, et al. Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets. Eur Radiol. 2021; 31(1):181–190. DOI: 10.1007/s00330-020-07070-7. PMID: 32696257. PMCID: PMC7755645.

15. Alzubaidi L, Al-Dulaimi K, Salhi A, Alammar Z, Fadhel MA, Albahri AS, et al. Comprehensive review of deep learning in orthopaedics: applications, challenges, trustworthiness, and fusion. Artif Intell Med. 2024; 155:102935. DOI: 10.1016/j.artmed.2024.102935. PMID: 39079201.

16. Goutallier D, Postel JM, Bernageau J, Lavau L, Voisin MC. Fatty muscle degeneration in cuff ruptures: pre- and postoperative evaluation by CT scan. Clin Orthop Relat Res. 1994; 304:78–83. DOI: 10.1097/00003086-199407000-00014.

17. Jeong JY, Chung PK, Lee SM, Yoo JC. Supraspinatus muscle occupation ratio predicts rotator cuff reparability. J Shoulder Elbow Surg. 2017; 26(6):960–966. DOI: 10.1016/j.jse.2016.11.001. PMID: 28153683.

18. Lee SH, Lee J, Oh KS, Yoon JP, Seo A, Jeong Y, et al. Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm. PLoS One. 2023; 18(5):e0284111. DOI: 10.1371/journal.pone.0284111. PMID: 37200275. PMCID: PMC10194959.

19. Hashimoto E, Maki S, Ochiai N, Ise S, Inagaki K, Hiraoka Y, et al. Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning. J Shoulder Elbow Surg. 2024; 33(8):1733–1739. DOI: 10.1016/j.jse.2023.12.009. PMID: 38311106.

20. Magnéli M, Ling P, Gislén J, Fagrell J, Demir Y, Arverud ED, et al. Deep learning classification of shoulder fractures on plain radiographs of the humerus, scapula and clavicle. PLoS One. 2023; 18(8):e0289808. DOI: 10.1371/journal.pone.0289808. PMID: 37647274. PMCID: PMC10468075.

21. Li G, Wu N, Zhang J, Song Y, Ye T, Zhang Y, et al. Proximal humeral bone density assessment and prediction analysis using machine learning techniques: An innovative approach in medical research. Heliyon. 2024; 10(15):e35451. DOI: 10.1016/j.heliyon.2024.e35451. PMID: 39166094. PMCID: PMC11334883.

22. Tingart MJ, Zurakowski D, Warner JJP, Apreleva M, von Stechow D. The cortical thickness of the proximal humeral diaphysis predicts bone mineral density of the proximal humerus. J Bone Joint Surg Br. 2003; 85(4):611–617. DOI: 10.1302/0301-620X.85B4.12843. PMID: 12793573.

23. van den Hoorn W, Lavaill M, Cutbush K, Gupta A, Kerr G. Comparison of shoulder range of motion quantified with mobile phone video-based skeletal tracking and 3D motion capture: preliminary study. Sensors. 2024; 24(2):534. DOI: 10.3390/s24020534. PMID: 38257626. PMCID: PMC10818695.

24. Pereira B, Cunha B, Viana P, Lopes M, Melo ASC, Sousa ASP. A machine learning app for monitoring physical therapy at home. Sensors. 2024; 24(1):158. DOI: 10.3390/s24010158. PMID: 38203019. PMCID: PMC10781250.

25. Sun K, Xiao B, Liu D, Wang J. Deep high-tesolution representation learning for human pose estimation. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019. Jun. 15-20. Long Beach, CA. Piscataway (NJ): IEEE;2019. p. p. 5693–5703. DOI: 10.1109/CVPR.2019.00584.

26. Takigami S, Inui A, Mifune Y, Nishimoto H, Yamaura K, Kato T, et al. Estimation of shoulder joint rotation angle using tablet device and pose estimation artificial intelligence model. Sensors. 2024; 24(9):2912. DOI: 10.3390/s24092912. PMID: 38733018. PMCID: PMC11086391.

27. Ramkumar PN, Haeberle HS, Navarro SM, Sultan AA, Mont MA, Ricchetti ET, et al. Mobile technology and telemedicine for shoulder range of motion: validation of a motion-based machine-learning software development kit. J Shoulder Elbow Surg. 2018; 27(7):1198–1204. DOI: 10.1016/j.jse.2018.01.013. PMID: 29525490.