Knee injury emerges as one of the most common diseases, causing dislocation of knee joints, immobility, etc., in which the anterior cruciate ligament (ACL) injury is the most common one. The development of various artificial intelligence (AI) frameworks gained enormous attention in many areas, including injury prediction and health management via medical image analysis. The objective of the current study is to focus on a comprehensive high accurate prediction of ACL injury based on MRI medical images, and also demonstrate the ability of AI in practical and outline conceptual prediction and diagnosis frameworks for other types of knee injuries in the future. Our dataset comprised of knee MRI reports from Cho Ray Hospital, Vietnam which are composed of ACL and non-ACL injury patients. The MRI images were used as supporting data in the deep learning classification model with DenseNet-121 algorithm. The successful establishment of an ACL injury diagnosis model from MRI will pave the way for us to develop more diagnostic models of other injuries in the body as well as the prediction of bone diseases.

Original languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2021
EditorsRuey-Feng Chang
ISBN (Electronic)9781510644205
Publication statusPublished - 2021
EventInternational Forum on Medical Imaging in Asia 2021, IFMIA 2021 - Taipei, Taiwan
Duration: Jan 24 2021Jan 26 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceInternational Forum on Medical Imaging in Asia 2021, IFMIA 2021


  • anterior cruciate ligament
  • classification
  • convolutional neural network
  • deep learning
  • DenseNet-121
  • knee-injuries
  • orthopedic

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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