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Detection of Elbow OCD in the Ultrasound Image by Artificial Intelligence Using YOLOv8

Inui, Atsuyuki Mifune, Yutaka Nishimoto, Hanako Mukohara, Shintaro Fukuda, Sumire Kato, Tatsuo Furukawa, Takahiro Tanaka, Shuya Kusunose, Masaya Takigami, Shunsaku Ehara, Yutaka Kuroda, Ryosuke 神戸大学

2023.07

概要

Background: Screening for elbow osteochondritis dissecans (OCD) using ultrasound (US) is essential for early detection and successful conservative treatment. The aim of the study is to determine the diagnostic accuracy of YOLOv8, a deep-learning-based artificial intelligence model, for US images of OCD or normal elbow-joint images. Methods: A total of 2430 images were used. Using the YOLOv8 model, image classification and object detection were performed to recognize OCD lesions or standard views of normal elbow joints. Results: In the binary classification of normal and OCD lesions, the values from the confusion matrix were the following: Accuracy = 0.998, Recall = 0.9975, Precision = 1.000, and F-measure = 0.9987. The mean average precision (mAP) comparing the bounding box detected by the trained model with the true-label bounding box was 0.994 in the YOLOv8n model and 0.995 in the YOLOv8m model. Conclusions: The YOLOv8 model was trained for image classification and object detection of standard views of elbow joints and OCD lesions. Both tasks were able to be achieved with high accuracy and may be useful for mass screening at medical check-ups for baseball elbow.

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参考文献

1.

2.

3.

4.

5.

Kida, Y.; Morihara, T.; Kotoura, Y.; Hojo, T.; Tachiiri, H.; Sukenari, T.; Iwata, Y.; Furukawa, R.; Oda, R.; Arai, Y.; et al. Prevalence

and Clinical Characteristics of Osteochondritis Dissecans of the Humeral Capitellum Among Adolescent Baseball Players. Am. J.

Sports Med. 2014, 42, 1963–1971. [CrossRef] [PubMed]

Matsuura, T.; Suzue, N.; Iwame, T.; Nishio, S.; Sairyo, K. Prevalence of Osteochondritis Dissecans of the Capitellum in Young

Baseball Players: Results Based on Ultrasonographic Findings. Orthop. J. Sports Med. 2014, 2, 2325967114545298. [CrossRef]

[PubMed]

Bruns, J.; Werner, M.; Habermann, C.R. Osteochondritis Dissecans of Smaller Joints: The Elbow. Cartilage 2021, 12, 407–417.

[CrossRef] [PubMed]

Sayani, J.; Plotkin, T.; Burchette, D.T.; Phadnis, J. Treatment Strategies and Outcomes for Osteochondritis Dissecans of the

Capi-tellum. Am. J. Sports Med. 2021, 49, 4018–4029. [CrossRef] [PubMed]

Matsuura, T.; Iwame, T.; Iwase, J.; Sairyo, K. Osteochondritis Dissecans of the Capitellum: Review of the Literature. J. Med.

Investig. 2020, 67, 217–221. [CrossRef] [PubMed]

Appl. Sci. 2023, 13, 7623

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

11 of 11

Yoshizuka, M.; Sunagawa, T.; Nakashima, Y.; Shinomiya, R.; Masuda, T.; Makitsubo, M.; Adachi, N. Comparison of sonography

and MRI in the evaluation of stability of capitellar osteochondritis dissecans. J. Clin. Ultrasound 2018, 46, 247–252. [CrossRef]

[PubMed]

Iwame, T.; Matsuura, T.; Suzue, N.; Kashiwaguchi, S.; Iwase, T.; Fukuta, S.; Hamada, D.; Goto, T.; Tsutsui, T.; Wada, K.; et al.

Outcome of an elbow check-up system for child and adolescent baseball players. J. Med. Investig. 2016, 63, 171–174. [CrossRef]

[PubMed]

Ikeda, K.; Okamoto, Y.; Ogawa, T.; Terada, Y.; Kajiwara, M.; Miyasaka, T.; Michinobu, R.; Hara, Y.; Yoshii, Y.; Nakajima, T.; et al.

Use of a Small Car-Mounted Magnetic Resonance Imaging System for On-Field Screening for Osteochondritis Dissecans of the

Humeral Capitellum. Diagnostics 2022, 12, 2551. [CrossRef] [PubMed]

Potocnik, J.; Foley, S.; Thomas, E. Current and potential applications of artificial intelligence in medical imaging practice: A

nar-rative review. J. Med. Imaging Radiat. Sci. 2023, 54, 376–385. [CrossRef] [PubMed]

Shinohara, I.; Inui, A.; Mifune, Y.; Nishimoto, H.; Yamaura, K.; Mukohara, S.; Yoshikawa, T.; Kato, T.; Furukawa, T.; Hoshino, Y.;

et al. Using deep learning for ultrasound images to diagnose carpal tunnel syndrome with high accuracy. Ultrasound Med. Biol.

2022, 48, 2052–2059. [CrossRef] [PubMed]

Shinohara, I.; Inui, A.; Mifune, Y.; Nishimoto, H.; Mukohara, S.; Yoshikawa, T.; Kuroda, R. Ultrasound With Artificial Intelligence

Models Predicted Palmer 1B Triangular Fibrocar-tilage Complex Injuries. Arthroscopy 2022, 38, 2417–2424. [CrossRef] [PubMed]

Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 779–788.

[CrossRef]

Redmon, J.; Farhadi, A. YOLO9000: Better, Faster, Stronger. In Proceedings of the IEEE Conference on Computer Vision and

Pattern Recognition (Cvpr 2017), Honolulu, HI, USA, 21–26 July 2017; pp. 6517–6525.

Aly, G.H.; Marey, M.; El-Sayed, S.A.; Tolba, M.F. YOLO Based Breast Masses Detection and Classification in Full-Field Digital

Mammograms. Comput. Methods Programs Biomed. 2021, 200, 105823. [CrossRef] [PubMed]

Su, Y.; Liu, Q.; Xie, W.; Hu, P. YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and

seg-mentation in digital mammograms. Comput. Methods Programs. Biomed. 2022, 221, 106903. [CrossRef] [PubMed]

Li, J.; Li, S.; Li, X.; Miao, S.; Dong, C.; Gao, C.; Liu, X.; Hao, D.; Xu, W.; Huang, M.; et al. Primary bone tumor detection and

classification in full-field bone radiographs via YOLO deep learning model. Eur. Radiol. 2022, 33, 4237–4248. [CrossRef] [PubMed]

Sakata, J.; Ishikawa, H.; Inoue, R.; Urata, D.; Ohinata, J.; Kimoto, T.; Yamamoto, N. Physical functions, to be or not to be a risk

factor for osteochondritis dissecans of the humeral capitellum? JSES Int. 2022, 6, 1072–1077. [CrossRef] [PubMed]

Maruyama, M.; Takahara, M.; Satake, H. Diagnosis and treatment of osteochondritis dissecans of the humeral capitellum. J.

Orthop. Sci. 2018, 23, 213–219. [CrossRef] [PubMed]

Matsuura, T.; Iwame, T.; Suzue, N.; Takao, S.; Nishio, S.; Arisawa, K.; Sairyo, K. Cumulative Incidence of Osteochondritis

Dissecans of the Capitellum in Preadolescent Baseball Players. Arthroscopy 2019, 35, 60–66. [CrossRef] [PubMed]

Otoshi, K.; Kikuchi, S.; Kato, K.; Sato, R.; Igari, T.; Kaga, T.; Konno, S. Age-Specific Prevalence and Clinical Characteristics of

Humeral Medial Epicondyle Apophysitis and Osteochondritis Dissecans: Ultrasonographic Assessment of 4249 Players. Orthop.

J. Sports Med. 2017, 5, 2325967117707703. [CrossRef] [PubMed]

Shinohara, I.; Yoshikawa, T.; Inui, A.; Mifune, Y.; Nishimoto, H.; Mukohara, S.; Kato, T.; Furukawa, T.; Tanaka, S.; Kusunose, M.;

et al. Degree of Accuracy with Which Deep Learning for Ultrasound Images Identifies Osteochondritis Dissecans of the Humeral

Capitellum. Am. J. Sports Med. 2023, 51, 358–366. [CrossRef] [PubMed]

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