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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. [ABSTRACT FROM AUTHOR]

Background Helicobacter pylori infection is associated with colorectal adenoma and confers a 1.3- to 2.26-fold increased risk. We evaluated the association between H. pylori and the progression of colorectal adenoma. Methods This retrospective cohort study included 615 adults with no history of colorectal adenoma or cancer at baseline who participated in a repeated, regular health screening examination, which included a bidirectional gastrointestinal endoscopy, between July 2006 and June 2015. A gastric biopsy specimen from each subject was tested for H. pylori. Results During follow-up, the incidence rates of colorectal adenoma progression in participants with persistent H. pylori infections (persistent group) and those whose infections had previously been successfully eradicated (eradication group) were 160.52 and 51.60 per 1000 person-years, respectively (P =.0003). After adjustment for confounding factors, the persistent group exhibited a higher risk of colorectal adenoma than the eradication group (hazard ratio = 3.04, 95% CI 1.899, 5.864). The colorectal adenoma ratio of patients uninfected with H. pylori was similar to that of the eradication group (23.93% vs 20.12%, P =.328). Conclusions Persistent H. pylori infection was associated significantly with the independent development of colorectal adenoma. H. pylori infection may have a pathophysiological role in colorectal adenoma development and, after successful eradication of H. pylori, the colorectal adenoma ratio might decrease. [ABSTRACT FROM AUTHOR]