Clinical validation of an abridged AIMS: Streamlining motor screening in the first-year infant.

This study validates an abridged version of the Alberta Infant Motor Scale (AIMS), termed the "salient set," to streamline infant screening using video analysis and machine learning. Twenty-one retrospective infant videos were manually tagged by trained occupational therapists using only the 15-item salient set with support vector regressors (SVRs) trained on a larger sample (n = 102) predicting the true (full) AIMS score. The SVR demonstrated strong concurrent validity of the salient set with the full 58-item AIMS (Pearson correlation: 0.

Enhancing motor screening efficiency: Toward an empirically derived abridged version of the Alberta Infant Motor Scale.

Use of machine learning (ML) in the early detection of developmental delay is possible through the analysis of infant motor skills, though the large number of potential indicators limits the speed at which the system can be trained. Body joint obstructions, the inability to infer aspects of movement such as muscle tone and volition, and the complexities of the home environment - confound machine learning's ability to distinguish between some motor items. To train the system efficiently requires using an excerpted list of validated items, a salient set, which uses only those motor items that are the 'easiest' to see and identify, while being the most highly correlated to a low/qualifying score.

Motivation and organizational principles for anatomical knowledge representation: the digital anatomist symbolic knowledge base.

Objective: Conceptualization of the physical objects and spaces that constitute the human body at the macroscopic level of organization, specified as a machine-parseable ontology that, in its human-readable form, is comprehensible to both expert and novice users of anatomical information.

Design: Conceived as an anatomical enhancement of the UMLS Semantic Network and Metathesaurus, the anatomical ontology was formulated by specifying defining attributes and differentia for classes and subclasses of physical anatomical entities based on their partitive and spatial relationships. The validity of the classification was assessed by instantiating the ontology for the thorax.

Visualization-based mapping of language function in the brain.

Cortical language maps, obtained through intraoperative electrical stimulation studies, provide a rich source of information for research on language organization. Previous studies have shown interesting correlations between the distribution of essential language sites and such behavioral indicators as verbal IQ and have provided suggestive evidence for regarding human language cortex as an organization of multiple distributed systems. Noninvasive studies using ECoG, PET, and functional MR lend support to this model; however, there as yet are no studies that integrate these two forms of information.

Evaluation of a visualization-based approach to functional brain mapping.

We describe a method for mapping stimulation data, obtained at the time of neurosurgery for intractable epilepsy, onto a 3D MRI-based neuroanatomic model of the individual patient. The mapping is done by comparing an intraoperative photograph of the exposed cortical surface with a computer-based MR visualization of the surface, interactively indicating corresponding stimulation sites, and recording 3-D MR machine coordinates of the indicated sites. Repeatability studies were performed to validate the accuracy of the mapping technique.