Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays.

Journal: Journal of orthopaedic surgery and research
PMID: 34823550

Abstract

BACKGROUND: Although the automatic diagnosis of fractures using artificial intelligence (AI) has recently been reported to be more accurate than those by orthopedics specialists, big data with at least 1000 images or more are required for deep learning of the convolutional neural network (CNN) to improve diagnostic accuracy. The aim of this study was to develop an AI system capable of diagnosing distal radius fractures with high accuracy even when learning with relatively small data by learning to use bi-planar X-rays images.

Authors

  • Kunihiro Oka
    Department of Orthopaedic Surgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
  • Ryoya Shiode
    Department of Orthopaedic Surgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan. frontier.of.mode@gmail.com.
  • Yuichi Yoshii
    Ibaraki Medical Center, Department of Orthopaedic Surgery, Tokyo Medical University, 3-20-1 Chuo, Ami, Inashiki, Ibaraki, 300-0395, Japan.
  • Hiroyuki Tanaka
    Department of Pharmacognosy, Graduate School of Pharmaceutical Sciences, Kyushu University, Fukuoka, Japan.
  • Toru Iwahashi
    Department of Orthopaedic Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, Japan.
  • Tsuyoshi Murase
    Department of Orthopaedic Surgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.

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