Annals of the Child Neurology Society最新文献

The varicella vaccination program has an excellent safety record. The vaccine virus, like its wild-type counterpart, can enter latency and later reactivate as herpes zoster. A lesser known but serious adverse event following reactivation is varicella vaccine meningitis. We investigate that adverse event. We performed a literature search using the PubMed and Google Scholar search engines to locate all published cases of varicella vaccine meningitis. We continued the search through January 2023. We found 17 cases of varicella vaccine meningitis. The first case was published in 2003, and the last case was published in 2023. The children lived in the United States, Greece, Germany, Switzerland, and Japan. Among the 17 cases, 14 were immunocompetent; nine of the 17 were adolescents. One potential risk factor was the administration of corticosteroids three to four weeks before the onset of meningitis. Varicella vaccine meningitis is a rare but one of the more serious adverse events that occurs several years following varicella vaccination. In immunocompetent children, this complication is treatable with a single course of intravenous acyclovir after hospitalization.

The ideal approach for child neurology training has become a topic of controversy in recent years, with strong opinions from both sides of the discussion often generating more heat than light. What follows is a dispassionate analysis of our child neurology training requirements. My intent is to facilitate constructive discussion and reframe the questions, not to take sides. We owe it to our trainees and to the children entrusted to us to create the best possible child neurology training.

As a neurologist who still sees adult patients, I am not opposed to adult neurology training for child neurologists. The question is not whether a year of adult neurology training still has value for child neurologists, but whether requiring a full year continues to represent the best use of the residents' time given the major changes in the field in the half century since the basic training requirements were initially established. Similarly, it may be appropriate to discuss the optimal amount of preliminary pediatric training for child neurologists.

The American Board of Psychiatry and Neurology (ABPN) was founded in 1934, and separate training and certification programs for neurologists and psychiatrists began in 1946. In 1959, Sidney Carter became the first child neurologist to serve as an ABPN director. That same year, the ABPN began to include child neurology topics in its certification examination, which at the time consisted of an eight-hour oral examination.² During the next eight years, a written examination with child neurology topics was introduced, and in 1967, the oral examination was scaled back to four hours, one of which focused on child neurology. In 1969, the ABPN created its “Special Qualification in Child Neurology” category, awarding 106 “grandfather” certificates to individuals who were already focusing on child neurology.²

There have been a number of modifications to the child neurology certification process since it began. The oral examination was eventually reduced to three hours and later replaced entirely with an expanded written examination. Time-limited certification was introduced. The option of dual certification in pediatrics and in neurology remains, although fewer and fewer trainees opt to pursue certification in pediatrics.³ Two infrequently used alternate certification pathways allowing one year of general pediatrics training to be replaced by either internal medicine or pre-approved neuroscience research were approved, of course without the option of certification in general pediatrics. The six-year neurodevelopmental pediatrics track was approved as a separate pathway. Thus, the certification process has not been stagnant, but the requirement for 12 months of clinical adult neurology training has remained intact.

A decade or so ago, the Child Neurology Society's (CNS) leadership and the society's representatives on the Neurology

The National Institutes of Health is renowned for being the largest funder of biomedical research in the world. Although it also houses unique research laboratories and clinics on its campuses around the United States, the National Institutes of Health Intramural Research Program is much less well-known than its extramural enterprise. The Intramural Research Program provides many outstanding opportunities for research-intensive careers and training and includes a vibrant and diverse community of developmental neurobiology and child neurology investigators.

Artificial intelligence is the science and engineering of machines that can mimic human intelligence. Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without being explicitly programmed. Deep learning algorithms learn by processing the data with increasing levels of abstraction in each layer. We present a narrative review of the relevant literature with a particular focus on deep learning for image classification and image segmentation in neuroimaging. For the first time in history, computers can automatically perform some clinically relevant tasks at the level, or even above the level, of the relevant medical specialists. A turning point in machine learning occurred in the 2010s as a result of (1) the multiple technical improvements that machine learning has been accumulating over several decades, (2) the exponential increase in computing power, and (3) the wide availability of very large databases with millions of observations and thousands of variables. Machine learning is starting to be successfully applied to several areas of medicine, including predictive analytics, decision support, natural language processing of free-text notes, and automatic interpretation of electrophysiological recordings. Among all the applications of machine learning in medicine, deep learning for computer vision is the one that has enjoyed the greatest success. The emphasis of this review is the application of convolutional neural networks for image classification and for image segmentation in neuroimaging. Machine learning and deep learning are increasingly integrated into the clinical workflow and applied in neuroimaging interpretation. Natural language processing is likely to gain increasing importance in medicine in the near future. Complex decision-making that mimics human thinking with reinforcement learning is still far away on the horizon.