Rawal A, Eckers F, Lee OS, Hochreiter B, Wang KK, Ek ET. Current evidence regarding shoulder instability in the paediatric and adolescent population. J Clin Med. 2024;13(3):724.
Article PubMed PubMed Central Google Scholar
Seyres M, Postans N, Freeman R, Pandyan A, Chadwick EK, Philp F. Children and adolescents with all forms of shoulder instability demonstrate differences in their movement and muscle activity patterns when compared to age-and sex-matched controls. J Shoulder Elbow Surg. 2024;33:e478.
Entezari V, Lazarus MD. Shoulder instability. In: The foundations of shoulder and elbow surgery. CRC Press; 2024. p. 191–214.
Linscheid LJ, DeShazo SJ, Pescatore SM, Somerson JS. Superior labrum anterior to posterior (SLAP) repair is associated with increased rate of subsequent rotator cuff diagnoses and revision surgery: a propensity-matched comparison. J Shoulder Elbow Surg. 2024;33:1821.
Alpantaki K, McLaughlin D, Karagogeos D, Hadjipavlou A, Kontakis G. Sympathetic and sensory neural elements in the tendon of the long head of the biceps. JBJS. 2005;87(7):1580–3.
Cooper DE, Arnoczky S, Obrien S, Warren R, Dicarlo E, Allen A. Anatomy, histology, and vascularity of the glenoid labrum. An anatomical study. JBJS. 1992;74(1):46–52.
Arkenbosch JH, van Ruler O, de Vries AC, van der Woude CJ, Dwarkasing RS. The role of MRI in perianal fistulizing disease: diagnostic imaging and classification systems to monitor disease activity. Abdominal Radiol. 2024;50:589.
Sugawara K, Takaya E, Inamori R, Konaka Y, Sato J, Shiratori Y, Hario F, Kobayashi T, Ueda T, Okamoto Y (2025) Breast cancer classification based on breast tissue structures using the Jigsaw puzzle task in self-supervised learning. Radiol Phys Technol, 1–10
Mori K, Negishi T (2025) Development of an image quality evaluation system for bedside chest X-ray images using scatter correction processing. Radiol Phys Technol, 1–9
Ieko Y, Kadoya N, Tanaka S, Kikuchi K, Yamamoto T, Ariga H, Jingu K (2025) Radiomics and dosiomics approaches to estimate lung function after stereotactic body radiation therapy in patients with lung tumors. Radiol Phys Technol, 1–11
Nakagawa S, Miyati T, Ohno N, Oda Y, Kashiwagi H, Kobayashi S (2025) Evaluation of gravity effect on liver and spleen volumes using multiposture MRI. Radiol Phys Technol, 1–4
Ni M, Gao L, Chen W, Zhao Q, Zhao Y, Jiang C, Yuan H. Preliminary exploration of deep learning-assisted recognition of superior labrum anterior and posterior lesions in shoulder MR arthrography. Int Orthop. 2024;48(1):183–91.
Kim M, Park HM, Kim JY, Kim SH, Hoeke S, De Neve W, MRI-based diagnosis of rotator cuff tears using deep learning and weighted linear combinations. In: Machine learning for healthcare conference, 2020. PMLR, pp 292–308
Key S, Demir S, Gurger M, Yilmaz E, Barua PD, Dogan S, Tuncer T, Arunkumar N, Tan R-S, Acharya UR. ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images. Med Eng Phys. 2022;110: 103864.
Kang Y, Choi D, Lee KJ, Oh JH, Kim BR, Ahn JM. Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning. Eur Radiol. 2021;31(12):9408–17.
Shim E, Kim JY, Yoon JP, Ki S-Y, Lho T, Kim Y, Chung SW. Automated rotator cuff tear classification using 3D convolutional neural network. Sci Rep. 2020;10(1):15632.
Article CAS PubMed PubMed Central Google Scholar
Yıldız F, Bilsel K, Pulatkan A, Uzer G, Aralaşmak A, Atay M. Reliability of magnetic resonance imaging versus arthroscopy for the diagnosis and classification of superior glenoid labrum anterior to posterior lesions. Arch Orthop Trauma Surg. 2017;137:241–7.
Gunay C, Kavak M (2021) Comparison of SLAP Lesions on MRI and Arthroscopy. Osmangazi Tıp Dergisi
Clymer DR, Long J, Latona C, Akhavan S, LeDuc P, Cagan J. Applying machine learning methods toward classification based on small datasets: application to shoulder labral tears. J Eng Sci Med Diagnostics Ther. 2020;3(1): 011004.
He K, Zhang X, Ren S, Sun J, 2016 Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Tan M, Le Q, 2019 Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105–6114
Woo S, Debnath S, Hu R, Chen X, Liu Z, Kweon IS, Xie S, Convnext v2: Co-designing and scaling convnets with masked autoencoders. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023. pp 16133–16142
Yacouby R, Axman D Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models. In: Proceedings of the first workshop on evaluation and comparison of NLP systems, 2020. pp 79–91
Goutte C, Gaussier EA. probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: European conference on information retrieval. Springer; 2005. p. 345–59.
Tuncer T, Dogan S, Özyurt F, Belhaouari SB, Bensmail H. Novel multi center and threshold ternary pattern based method for disease detection method using voice. IEEE Access. 2020;8:84532–40.
Vapnik V. The support vector method of function estimation. In: Nonlinear modeling. Springer; 1998. p. 55–85.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, 2017. pp 618–626
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. pp 4510–4520
Redmon J, Farhadi A, YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017. pp 7263–7271
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1097–105.
Zhang X, Zhou X, Lin M, Sun J, Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. pp 6848–6856
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ, Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017. pp 4700–4708
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z, Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. pp 2818–2826
Szegedy C, Ioffe S, Vanhoucke V, Alemi A Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2017, Vol 1.
Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2013) Intriguing properties of neural networks. arXiv preprint arXiv:13126199
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