RESUMEN
Importance: Infant alertness and neurologic changes are assessed by exam, which can be intermittent and subjective. Reliable, continuous methods are needed. Objective: We hypothesized that our computer vision method to track movement, pose AI, could predict neurologic changes. Design: Retrospective observational study from 2021-2022. Setting: A level four urban neonatal intensive care unit (NICU). Participants: Infants with corrected age ≤1 year, comprising 115 patients with 4,705 hours of video data linked to electroencephalograms (EEG), including 46% female and 25.2% white non-Hispanic. Exposures: Pose AI prediction of anatomic landmark position and an XGBoost classifier trained on one-minute variance in pose. Main outcomes and measures: Outcomes were cerebral dysfunction, diagnosed from EEG readings by an epileptologist, and sedation, defined by the administration of sedative medications. Measures of algorithm performance were receiver operating characteristic-area under the curves (ROC-AUCs) on cross-validation and on two test datasets comprised of held-out infants and held-out video frames from infants used in training. Results: Infant pose was accurately predicted in cross-validation, held-out frames, and held-out infants (respective ROC-AUCs 0.94, 0.83, 0.89). Median movement increased with age and, after accounting for age, was lower with sedative medications and in infants with cerebral dysfunction (all P<5×10-3, 10,000 permutations). Sedation prediction had high performance on cross-validation, held-out frames, and held-out infants (ROC-AUCs 0.90, 0.91, 0.87), as did prediction of cerebral dysfunction (ROC-AUCs 0.91, 0.90, 0.76). Conclusions and Relevance: We used pose AI to predict sedation and cerebral dysfunction in 4,705 hours of video from a large, diverse cohort of infants. Pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.
RESUMEN
Are human cultures distinctively cumulative because they are uniquely compositional? We addressed this question using a summative learning paradigm where participants saw different models build different tower elements, consisting of discrete actions and objects: stacking cubes (tower base) and linking squares (tower apex). These elements could be combined to form a tower that was optimal in terms of height and structural soundness. In addition to measuring copying fidelity, we explored whether children and adults (i) extended the knowledge demonstrated to additional tower elements and (ii) productively combined them. Results showed that children and adults copied observed demonstrations and applied them to novel exemplars. However, only adults in the imitation condition combined the two newly derived base and apex, relative to adults in a control group. Nonetheless, there were remarkable similarities between children's and adults' performance across measures. Composite measures capturing errors and overall generativity in children's and adults' performance produced few population by condition interactions. Results suggest that early in development, humans possess a suite of cognitive skills-compositionality and generativity-that transforms phylogenetically widespread social learning competencies into something that may be unique to our species, cultural learning; allowing human cultures to evolve towards greater complexity.