Prevalence and Disparities in the Detection of Autism Without Intellectual Disability.

BACKGROUND: Intellectual ability predicts functional outcomes for children with autism spectrum disorder (ASD). It is essential to classify ASD children with and without intellectual disability (ID) to aid etiological research, provide services, and inform evidence-based educational and health planning. METHODS: Using a cross-sectional study design, data from 2000 to 2016 active ASD surveillance among 8-year-olds residing in the New York-New Jersey Metropolitan Area were analyzed to determine ASD prevalence with and without ID. Multivariable Poisson regression models were used to identify trends for ASD with ID (ASD-I) and without ID (ASD-N). RESULTS: Overall, 4661 8-year-olds were identified with ASD. Those that were ASI-I were 1505 (32.3%) and 2764 (59.3%) were ASD-N. Males were 3794 (81.4%), 946 (20.3%) were non-Hispanic Black (Black), 1230 (26.4%) were Hispanic, and 2114 (45.4%) were non-Hispanic white (white). We observed 2-fold and 5-fold increases in the prevalence of ASD-I and ASD-N, respectively, from 2000-2016. Black children were 30% less likely to be identified with ASD-N compared with white children. Children residing in affluent areas were 80% more likely to be identified with ASD-N compared with children in underserved areas. A greater proportion of children with ASD-I resided in vulnerable areas compared with children with ASD-N. Males had higher prevalence compared with females regardless of ID status; however, male-to-female ratios were slightly lower among ASD-I compared with ASD-N cases. CONCLUSIONS: One-in-3 children with ASD had ID. Disparities in the identification of ASD without ID were observed among Black and Hispanic children as well as among children residing in underserved areas.

Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery.

Recent years have witnessed artificial intelligence (AI) make meteoric leaps in both medicine and surgery, bridging the gap between the capabilities of humans and machines. Digitization of operating rooms and the creation of massive quantities of data have paved the way for machine learning and computer vision applications in surgery. Surgical phase recognition (SPR) is a newly emerging technology that uses data derived from operative videos to train machine and deep learning algorithms to identify the phases of surgery. Advancement of this technology will be key in establishing context-aware surgical systems in the future. By automatically recognizing and evaluating the current surgical scenario, these intelligent systems are able to provide intraoperative decision support, improve operating room efficiency, assess surgical skills, and aid in surgical training and education. Still in its infancy, SPR has been mainly studied in laparoscopic surgeries, with a contrasting stark lack of research within neurosurgery. Given the high-tech and rapidly advancing nature of neurosurgery, we believe SPR has a tremendous untapped potential in this field. Herein, we present an overview of the SPR technology, its potential applications in neurosurgery, and the challenges that lie ahead.

The contributions of William A. Hammond (1828-1900) to Civil War medicine & modern neurology.

William Alexander Hammond was an American military physician and a main driving force for the development of modern-day clinical Neurology in America. Hammond served as the 11th Surgeon General of the United States Army, acting during the Civil War. Throughout his time as Surgeon General, with influence from Florence Nightingale, Hammond enforced strict hygienic measures and called for the construction of pavilion style hospitals in order to decrease non-wound mortalities. He implemented further reformation of the American Medical Service that would improve efficiency and decrease general mortality for years to come. After his dismissal from the military service, Hammond continued to make meaningful achievements, spearheading the specialization of Neurology. He established the first private practice limited to diseases of the nervous system, published the first American Neurology textbook, coined the term "athetosis", and was the impetus for the formation of the American Neurological Association.