Addressing healthcare disparities in homeless neurosurgical patients: A comprehensive literature review on strategies for equitable care and improved outcomes.

Background: Homelessness is a growing concern in the US, with 3.5 million people experiencing it annually and 600,000 on any given night. Homeless individuals face increased vulnerability to 30-day hospital readmissions and higher mortality rates, straining the healthcare system and exacerbating existing disparities.

Leukocyte differential gene expression prognostic value for high versus low seizure frequency in temporal lobe epilepsy.

Background: This study was performed to test the hypothesis that systemic leukocyte gene expression has prognostic value differentiating low from high seizure frequency refractory temporal lobe epilepsy (TLE).

Methods: A consecutive series of patients with refractory temporal lobe epilepsy was studied. Based on a median baseline seizure frequency of 2.

Emotional intelligence in neurosurgery: Mitigating burnout and enhancing performance.

Background: This study underscores the high burnout rates among physicians, particularly surgical residents, attributing it to the demanding health-care ecosystem. It highlights the negative impacts of burnout, such as medical errors and increased health-care costs, while exploring the potential mitigating role of emotional intelligence (EI) and mindfulness. The research aimed to analyze the existing literature on EI in neurosurgery, focusing on its relationship with physician burnout and its potential role in healthcare leadership and residency training programs.

Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema.

Importance: Best-corrected visual acuity (BCVA) is a measure used to manage diabetic macular edema (DME), sometimes suggesting development of DME or consideration of initiating, repeating, withholding, or resuming treatment with anti-vascular endothelial growth factor. Using artificial intelligence (AI) to estimate BCVA from fundus images could help clinicians manage DME by reducing the personnel needed for refraction, the time presently required for assessing BCVA, or even the number of office visits if imaged remotely.

Objective: To evaluate the potential application of AI techniques for estimating BCVA from fundus photographs with and without ancillary information.

Spinal cord stimulation for genitofemoral neuropathy: A case report and review of the literature.

Background: Chronic testicular pain due to genitofemoral neuropathy often becomes refractory to conservative medical therapy. Neurostimulation is a potentially useful treatment option, should the neuropathic pain remain refractory to more invasive procedures such as orchiectomy. We provide a case report of spinal cord stimulation (SCS) for successful treatment of genitofemoral neuropathy and have also reviewed the literature to find similar cases which required a similar treatment paradigm.

Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms.

Background: Germinal matrix hemorrhage-intraventricular hemorrhage is among the most common intracranial complications in premature infants. Early detection is important to guide clinical management for improved patient prognosis.

Objective: The purpose of this study was to assess whether a convolutional neural network (CNN) can be trained via transfer learning to accurately diagnose germinal matrix hemorrhage on head ultrasound.

TARA: Training and Representation Alteration for AI Fairness and Domain Generalization.

We propose a novel method for enforcing AI fairness with respect to protected or sensitive factors. This method uses a dual strategy performing training and representation alteration (TARA) for the mitigation of prominent causes of AI bias. It includes the use of representation learning alteration via adversarial independence to suppress the bias-inducing dependence of the data representation from protected factors and training set alteration via intelligent augmentation to address bias-causing data imbalance by using generative models that allow the fine control of sensitive factors related to underrepresented populations via domain adaptation and latent space manipulation.

Transrectal ultrasound-guided prostate needle biopsy remains a safe method in confirming a prostate cancer diagnosis: a multicentre Australian analysis of infection rates.

Purpose: Worldwide, transrectal ultrasound-guided prostate needle remains the most common method of diagnosing prostate cancer. Due to high infective complications reported, some have suggested it is now time to abandon this technique in preference of a trans-perineal approach. The aim of this study was to report on the infection rates following transrectal ultrasound-guided prostate needle biopsy in multiple Australian centres.

Addressing Artificial Intelligence Bias in Retinal Diagnostics.

Purpose: This study evaluated generative methods to potentially mitigate artificial intelligence (AI) bias when diagnosing diabetic retinopathy (DR) resulting from training data imbalance or domain generalization, which occurs when deep learning systems (DLSs) face concepts at test/inference time they were not initially trained on.

Methods: The public domain Kaggle EyePACS dataset (88,692 fundi and 44,346 individuals, originally diverse for ethnicity) was modified by adding clinician-annotated labels and constructing an artificial scenario of data imbalance and domain generalization by disallowing training (but not testing) exemplars for images of retinas with DR warranting referral (DR-referable) from darker-skin individuals, who presumably have greater concentration of melanin within uveal melanocytes, on average, contributing to retinal image pigmentation. A traditional/baseline diagnostic DLS was compared against new DLSs that would use training data augmented via generative models for debiasing.

AI-based detection of erythema migrans and disambiguation against other skin lesions.

This study examines the use of AI methods and deep learning (DL) for prescreening skin lesions and detecting the characteristic erythema migrans rash of acute Lyme disease. Accurate identification of erythema migrans allows for early diagnosis and treatment, which avoids the potential for later neurologic, rheumatologic, and cardiac complications of Lyme disease. We develop and test several deep learning models for detecting erythema migrans versus several other clinically relevant skin conditions, including cellulitis, tinea corporis, herpes zoster, erythema multiforme, lesions due to tick bites and insect bites, as well as non-pathogenic normal skin.

Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.

Importance: Recent studies have demonstrated the successful application of artificial intelligence (AI) for automated retinal disease diagnostics but have not addressed a fundamental challenge for deep learning systems: the current need for large, criterion standard-annotated retinal data sets for training. Low-shot learning algorithms, aiming to learn from a relatively low number of training data, may be beneficial for clinical situations involving rare retinal diseases or when addressing potential bias resulting from data that may not adequately represent certain groups for training, such as individuals older than 85 years.

Objective: To evaluate whether low-shot deep learning methods are beneficial when using small training data sets for automated retinal diagnostics.

The Neural Sulcus of the Cervical Vertebrae: A Review of Its Anatomy and Surgical Perspectives.

The neural sulcus is a bony channel that spans the transverse process in the subaxial cervical spine. It is located between the anterior and posterior tubercles on either side of the transverse foramen, housing the spinal nerve as it passes through the intervertebral foramina. Although numerous studies have evaluated the anatomy of the cervical spine, very little data on detailed anatomy of the neural sulcus and its implication in cervical spine surgery exist.

Social Media in Neurosurgery: Using ResearchGate.

Background: Neurosurgery is a unique field, which would benefit greatly from increased global collaboration, furthering research efforts. ResearchGate is a social media platform geared toward scientists and researchers.

Objective: This study evaluated the use of ResearchGate for neurosurgical research collaboration and compared the ResearchGate score with more classic bibliometrics.

Automated detection of erythema migrans and other confounding skin lesions via deep learning.

Lyme disease can lead to neurological, cardiac, and rheumatologic complications when untreated. Timely recognition of the erythema migrans rash of acute Lyme disease by patients and clinicians is crucial to early diagnosis and treatment. Our objective in this study was to develop deep learning approaches using deep convolutional neural networks for detecting acute Lyme disease from erythema migrans images of varying quality and acquisition conditions.

Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.

Importance: Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks (DCNNs). In contrast, generative DL techniques could synthesize large new data sets of artificial retina images with different stages of AMD. Such images could enhance existing data sets of common and rare ophthalmic diseases without concern for personally identifying information to assist medical education of students, residents, and retinal specialists, as well as for training new DL diagnostic models for which extensive data sets from large clinical trials of expertly graded images may not exist.

Deep embeddings for novelty detection in myopathy.

We address the challenge of finding anomalies in ultrasound images via deep learning, specifically applying this to screening for myopathies and finding rare presentations of myopathic disease. Among myopathic diseases, this study focuses on the use case of myositis given the spectrum of muscle involvement seen in these inflammatory muscle diseases, as well as the potential for treatment. For this study, we have developed a fully annotated dataset (called "Myositis3K") which includes 3586 images of eighty-nine individuals (35 control and 54 with myositis) acquired with informed consent.

Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration.

Importance: Although deep learning (DL) can identify the intermediate or advanced stages of age-related macular degeneration (AMD) as a binary yes or no, stratified gradings using the more granular Age-Related Eye Disease Study (AREDS) 9-step detailed severity scale for AMD provide more precise estimation of 5-year progression to advanced stages. The AREDS 9-step detailed scale's complexity and implementation solely with highly trained fundus photograph graders potentially hampered its clinical use, warranting development and use of an alternate AREDS simple scale, which although valuable, has less predictive ability.

Objective: To describe DL techniques for the AREDS 9-step detailed severity scale for AMD to estimate 5-year risk probability with reasonable accuracy.

Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration.

This study uses fundus images from a national data set to assess 2 deep learning methods for referability classification of age-related macular degeneration.

Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks.

Importance: Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage.

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

Objective: To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis.

Methods: Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs.

Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.

Background: When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is often asymptomatic with regard to visual deficit. These individuals are at high risk for progressing to the advanced stage where the often treatable choroidal neovascular form of AMD can occur.

Impact of posterior musculofascial reconstruction on early continence after robot-assisted laparoscopic radical prostatectomy: results of a prospective parallel group trial.

Background: A significant proportion of patients develop urinary incontinence early after radical prostatectomy. Posterior reconstruction of supporting tissues has been found to reduce incontinence in open and conventional laparoscopic prostatectomy series.

Objective: To investigate whether our version of a posterior musculofascial reconstruction will reduce early incontinence and have a beneficial effect on patients' quality of life (QoL).