Faculty Bibliography

BACKGROUND:/Objective: Subdural hematomas (SDH) are a common and dangerous condition, with potential for a rapid rise in incidence given the aging United States population, but the magnitude of this increase is unknown. Our objective was to characterize the number of SDH and practicing neurosurgeons from 2003-2016 and project these numbers to 2040. METHODS:Utilizing the National Inpatient Sample years 2003-2016 (nearly 500 million hospitalizations), all hospitalizations with a diagnosis of SDH were identified and grouped by age. Numerical estimates of SDH were projected to 2040 in ten-year increments for each age group using Poisson modeling with population estimates from the United States Census Bureau. The number of neurosurgeons who billed CMS from 2012 to 2017 was noted and linearly projected to 2040. RESULTS:From 2020-2040, SDH volume is expected to increase by 78.3%, from 135,859 to 208,212. Most of this increase will be seen in the elderly, as patients 75-84 years old will experience an increase from 37,941 to 69,914 and patients greater than 85 years old will experience an increase from 31,200 to 67,181. The number of neurosurgeons is projected to increase from 4,675 in 2020 to 6,252 in 2040. CONCLUSION/CONCLUSIONS:SDH is expected to increase significantly from 2020-2040, with the majority of this increase being concentrated in elderly patients. While the number of neurosurgeons will also increase, the ability of current neurosurgical resources to properly handle this expected increase in SDH will need to be addressed on a national scale.

OBJECTIVE:To predict surgical volumes for two common cervical spine procedures from 2020 to 2040. METHODS:Utilizing the National Inpatient Sample from 2003-2016, nationwide estimates of anterior cervical discectomy and fusion (ACDF) and posterior cervical decompression and fusion (PCDF) volumes were calculated using International Classification of Diseases, Ninth and Tenth Revision (ICD-9, ICD-10) procedure codes. With data from the United States Census Bureau, estimates of the United States population were used to create Poisson models controlling for age and sex. Age was categorized into ranges (<25 years old, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and >85), and estimates of surgical volume for each age group were created. RESULTS:From 2020-2040, increases in surgical volume from 13.3% (153,288 to 173,699) and 19.3% (29,620 to 35,335) are expected for ACDF and PCDF, respectively. For ACDF, the largest increases are expected in the 45-54 (42,077 to 49,827) and 75-84 (8,065 to 14,862) age groups, whereas for PCDF, the largest increases will be seen in the 75-84 (3,710 to 6,836) group. In accordance with an aging population, modest increases will be seen for ACDF (858 to 1,847) and PCDF (730 to 1,573) in the >85 years old cohort. CONCLUSION/CONCLUSIONS:As expected, large growth in cervical spine surgical volumes is likely to be seen, which could indicate a need for increased numbers of spinal neurosurgeons and orthopedic surgeons. Further studies are needed to investigate the needs of the field in light of these expected increases in volume.

STUDY DESIGN/METHODS:Cross sectional database study. OBJECTIVE:To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis. METHODS:Lateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151). RESULTS:> .05). CONCLUSION/CONCLUSIONS:This study is the first to use artificial intelligence and computer vision in a combined pipeline to rapidly measure a sagittal spinopelvic parameter without prior manual surgeon input. The pipeline measures angles with no statistically significant differences from manual measurements by surgeons. This pipeline offers clinical utility in an assistive capacity, and future work should focus on improving segmentation network performance.

Brain-computer interface (BCI) technology is rapidly developing and changing the paradigm of neurorestoration by linking cortical activity with control of an external effector to provide patients with tangible improvements in their ability to interact with the environment. The sensor component of a BCI circuit dictates the resolution of brain pattern recognition and therefore plays an integral role in the technology. Several sensor modalities are currently in use for BCI applications and are broadly either electrode-based or functional neuroimaging-based. Sensors vary in their inherent spatial and temporal resolutions, as well as in practical aspects such as invasiveness, portability, and maintenance. Hybrid BCI systems with multimodal sensory inputs represent a promising development in the field allowing for complimentary function. Artificial intelligence and deep learning algorithms have been applied to BCI systems to achieve faster and more accurate classifications of sensory input and improve user performance in various tasks. Neurofeedback is an important advancement in the field that has been implemented in several types of BCI systems by showing users a real-time display of their recorded brain activity during a task to facilitate their control over their own cortical activity. In this way, neurofeedback has improved BCI classification and enhanced user control over BCI output. Taken together, BCI systems have progressed significantly in recent years in terms of accuracy, speed, and communication. Understanding the sensory components of a BCI is essential for neurosurgeons and clinicians as they help advance this technology in the clinical setting.

BACKGROUND:Modern science and healthcare generate vast amounts of data, and, coupled with the increasingly inexpensive and accessible computing, a tremendous opportunity exists to use these data to improve care. A better understanding of data science and its relationship to neurosurgical practice will be increasingly important as we transition into this modern "big data" era. METHODS:A review of the literature was performed for key articles referencing big data for neurosurgical care or related topics. RESULTS:In the present report, we first defined the nature and scope of data science from a technical perspective. We then discussed its relationship to the modern neurosurgical practice, highlighting key references, which might form a useful introductory reading list. CONCLUSIONS:Numerous challenges exist going forward; however, organized neurosurgery has an important role in fostering and facilitating these efforts to merge data science with neurosurgical practice.

Deciphering the massive volume of complex electronic data that has been compiled by hospital systems over the past decades has the potential to revolutionize modern medicine, as well as present significant challenges. Deep learning is uniquely suited to address these challenges, and recent advances in techniques and hardware have poised the field of medical machine learning for transformational growth. The clinical neurosciences are particularly well positioned to benefit from these advances given the subtle presentation of symptoms typical of neurologic disease. Here we review the various domains in which deep learning algorithms have already provided impetus for change-areas such as medical image analysis for the improved diagnosis of Alzheimer's disease and the early detection of acute neurologic events; medical image segmentation for quantitative evaluation of neuroanatomy and vasculature; connectome mapping for the diagnosis of Alzheimer's, autism spectrum disorder, and attention deficit hyperactivity disorder; and mining of microscopic electroencephalogram signals and granular genetic signatures. We additionally note important challenges in the integration of deep learning tools in the clinical setting and discuss the barriers to tackling the challenges that currently exist.