首页|New Artificial Intelligence Study Findings Recently Were Reported by a Researcher at National Taiwan University Hospital and School of Medicine (Application of Artificial Intelligence in Infant Movement Classification: A Reliability and ...)
New Artificial Intelligence Study Findings Recently Were Reported by a Researcher at National Taiwan University Hospital and School of Medicine (Application of Artificial Intelligence in Infant Movement Classification: A Reliability and ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Oxford Univ Press
Research findings on artificial intelligence are discussed in a new report. According to news reporting from Taipei, Taiwan, by NewsRx journalists, research stated, “Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body.” Financial supporters for this research include National Science And Technology Council. The news correspondents obtained a quote from the research from National Taiwan University Hospital and School of Medicine: “It remains unclear whether the proposed pose estimation model and the skeletonbased action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants. This observational study prospectively assessed 30 full-term infants and 54 preterm infants using the Alberta Infant Motor Scale (58 movements) from 4 to 18 months of age with their movements recorded by 5 video cameras simultaneously in a standardized clinical setup. The movement videos were annotated for the start/end times and presence of movements by 3 pediatric physical therapists. The annotated videos were used for the development and testing of an AI algorithm that consisted of a 17-point human pose estimation model and a skeleton-based action recognition model. The infants contributed 153 sessions of Alberta Infant Motor Scale assessment that yielded 13,139 videos of movements for data processing. The intra and interrater reliabilities for movement annotation of videos by the therapists showed high agreements (88%-100%). Thirty-one of the 58 movements were selected for machine learning because of sufficient data samples and developmental significance. Using the annotated results as the standards, the AI algorithm showed satisfactory agreement in classifying the 31 movements (accuracy = 0.91, recall = 0.91, precision = 0.91, and F1 score = 0.91).”
National Taiwan University Hospital and School of MedicineTaipeiTaiwanAsiaAlgorithmsArtificial IntelligenceCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Oxford Univ Press
