CVClient

Daisuke FUJITA

  (藤田 大輔)

Profile Information

Affiliation
Graduate School of Engineering, University of Hyogo
Faculty of Regional Promotion, Nara Prefectural University
Degree
Doctor of Systems Engineering(Mar, 2020, Wakayama University)

Researcher number
90907867
J-GLOBAL ID
202101002056754195
researchmap Member ID
R000019969

Papers

 52
  • Daisuke FUJITA, Yuki ADACHI, Syoji KOBASHI
    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 36(2) 610-615, May 15, 2024  
  • Kenta Takatsuji, Yoshikazu Kida, Kenta Sasaki, Daisuke Fujita, Yusuke Kobayashi, Tsuyoshi Sukenari, Yoshihiro Kotoura, Masataka Minami, Syoji Kobashi, Kenji Takahashi
    The Journal of bone and joint surgery. American volume, May 14, 2024  
    BACKGROUND: Ultrasonography is used to diagnose osteochondritis dissecans (OCD) of the humerus; however, its reliability depends on the technical proficiency of the examiner. Recently, computer-aided diagnosis (CAD) using deep learning has been applied in the field of medical science, and high diagnostic accuracy has been reported. We aimed to develop a deep learning-based CAD system for OCD detection on ultrasound images and to evaluate the accuracy of OCD detection using the CAD system. METHODS: The CAD process comprises 2 steps: humeral capitellum detection using an object-detection algorithm and OCD classification using an image classification network. Four-directional ultrasound images of the elbow of the throwing arm of 196 baseball players (mean age, 11.2 years), including 104 players with normal findings and 92 with OCD, were used for training and validation. An external dataset of 20 baseball players (10 with normal findings and 10 with OCD) was used to evaluate the accuracy of the CAD system. A confusion matrix and the area under the receiver operating characteristic curve (AUC) were used to evaluate the system. RESULTS: Clinical evaluation using the external dataset resulted in high AUCs in all 4 directions: 0.969 for the anterior long axis, 0.966 for the anterior short axis, 0.996 for the posterior long axis, and 0.993 for the posterior short axis. The accuracy of OCD detection thus exceeded 0.9 in all 4 directions. CONCLUSIONS: We propose a deep learning-based CAD system to detect OCD lesions on ultrasound images. The CAD system achieved high accuracy in all 4 directions of the elbow. This CAD system with a deep learning model may be useful for OCD screening during medical checkups to reduce the probability of missing an OCD lesion. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.
  • Kenta Sasaki, Daisuke Fujita, Kenta Takatsuji, Yoshihiro Kotoura, Masataka Minami, Yusuke Kobayashi, Tsuyoshi Sukenari, Yoshikazu Kida, Kenji Takahashi, Syoji Kobashi
    International journal of computer assisted radiology and surgery, Jan 17, 2024  Peer-reviewed
    PURPOSE: Osteochondritis dissecans (OCD) of the humeral capitellum is a common cause of elbow disorders, particularly among young throwing athletes. Conservative treatment is the preferred treatment for managing OCD, and early intervention significantly influences the possibility of complete disease resolution. The purpose of this study is to develop a deep learning-based classification model in ultrasound images for computer-aided diagnosis. METHODS: This paper proposes a deep learning-based OCD classification method in ultrasound images. The proposed method first detects the humeral capitellum detection using YOLO and then estimates the OCD probability of the detected region probability using VGG16. We hypothesis that the performance will be improved by eliminating unnecessary regions. To validate the performance of the proposed method, it was applied to 158 subjects (OCD: 67, Normal: 91) using five-fold-cross-validation. RESULTS: The study demonstrated that the humeral capitellum detection achieved a mean average precision (mAP) of over 0.95, while OCD probability estimation achieved an average accuracy of 0.890, precision of 0.888, recall of 0.927, F1 score of 0.894, and an area under the curve (AUC) of 0.962. On the other hand, when the classification model was constructed for the entire image, accuracy, precision, recall, F1 score, and AUC were 0.806, 0.806, 0.932, 0.843, and 0.928, respectively. The findings suggest the high-performance potential of the proposed model for OCD classification in ultrasonic images. CONCLUSION: This paper introduces a deep learning-based OCD classification method. The experimental results emphasize the effectiveness of focusing on the humeral capitellum for OCD classification in ultrasound images. Future work should involve evaluating the effectiveness of employing the proposed method by physicians during medical check-ups for OCD.
  • 室井 大和, 藤田 大輔, 小橋 昌司, 藤田 孝之
    バイオメディカル・ファジィ・システム学会年次大会講演論文集, 36回 5-8, Dec, 2023  
  • 佐々木 研太, 藤田 大輔, 高辻 謙太, 琴浦 義浩, 南 昌孝, 小林 雄輔, 祐成 毅, 木田 圭重, 高橋 謙治, 小橋 昌司
    バイオメディカル・ファジィ・システム学会年次大会講演論文集, 36回 9-12, Dec, 2023  

Misc.

 31

Presentations

 54

Teaching Experience

 2

Research Projects

 8

Academic Activities

 6