論文

査読有り 本文へのリンクあり 国際誌
2024年5月14日

Deep Learning-Based Computer-Aided Diagnosis of Osteochondritis Dissecans of the Humeral Capitellum Using Ultrasound Images.

The Journal of bone and joint surgery. American volume
  • Kenta Takatsuji
  • ,
  • Yoshikazu Kida
  • ,
  • Kenta Sasaki
  • ,
  • Daisuke Fujita
  • ,
  • Yusuke Kobayashi
  • ,
  • Tsuyoshi Sukenari
  • ,
  • Yoshihiro Kotoura
  • ,
  • Masataka Minami
  • ,
  • Syoji Kobashi
  • ,
  • Kenji Takahashi

記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.2106/JBJS.23.01164

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.

リンク情報
DOI
https://doi.org/10.2106/JBJS.23.01164 本文へのリンクあり
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/38743813
ID情報
  • DOI : 10.2106/JBJS.23.01164
  • PubMed ID : 38743813

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