Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm.
Objective 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. It remains unclear whether the proposed pose estimation model and the skelet...
| Publicado en: | PTJ: Physical Therapy & Rehabilitation Journal Vol. 104; no. 2; pp. 1 - 11 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Feb2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176064843&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176064843 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15386724 MZW0 jtl: PTJ: Physical Therapy & Rehabilitation Journal issn: 15386724 maglogo: N pubinfo: dt: Feb2024 vid: 104 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 176064843 176064843 176064843 10.1093/ptj/pzad176 176064843 ppf: 1 ppct: 10 formats: tig: atl: Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm. aug: au: Lin, Shiang-Chin Chandra, Erick Tsao, Po Nien Liao, Wei-Chih Chen, Wei-J Yen, Ting-An Hsu, Jane Yung-Jen Jeng, Suh-Fang affil: School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine , Taipei , Taiwan sug: subj: Artificial Intelligence Utilization Human Reliability and Validity Infant Funding Source Conceptual Framework Nonexperimental Studies Prospective Studies Scales Intrarater Reliability Interrater Reliability Machine Learning Motor Activity Descriptive Statistics Infant: 1-23 months ab: Objective 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. It remains unclear whether the proposed pose estimation model and the skeleton-based 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. Methods 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. Results 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). Conclusion The AI algorithm was accurate in classifying 31 movements in full-term and preterm infants from 4 to 18 months of age in a standardized clinical setup. Impact The findings provide the basis for future refinement and validation of the algorithm on home videos to be a remote infant movement assessment. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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