National Football League Game Officials Self-Rating of Knowledge in Neuro-Ophthalmic Principles and Practice: A Pilot Program to Improve Precision and Accuracy of Game Official Calls

BACKGROUND

To determine whether a neuro-ophthalmic curriculum would improve National Football League (NFL) game officials' self-rated knowledge and interest in neuro-ophthalmic principles to improve precision and accuracy of NFL play-calling.

METHODS

The formalized and structured neuro-ophthalmic principles (NOP) curriculum was introduced to 121 NFL game officials, 17 replay officials, and 4 officiating staff who attended the NFL Official Training Camp in Irving, Texas, on September 8 and 9, 2023. Before and after the lecture and videos were introduced, participants completed an optional hard-copy feedback form pertaining to self-reported NOP knowledge, likelihood of using said terms, and interest in future content of NOP applicable NFL officiating. Paired 2-tailed t tests were used for statistical analysis to directly compare the self-reported knowledge before and after the neuro-ophthalmic curriculum introduction.

RESULTS

One hundred forty-two participants completed the prelecture and postlecture feedback forms self-reported knowledge after the NOP curriculum was given to the NFL officiating staff. All (142/142) participants completed a survey. There was a statistically significant improvement in the mean ratings of the prelecture vs. postlecture understanding of the specific neuro-ophthalmic terms pertinent to NFL game officials (2.6 [95% CI, 2.3-3.0] vs. 7.9 [95% CI, 7.6-8.2], P < 0.001) and 2.7 [95% CI, 2.3-3.0] vs. 7.7 [95% CI, 7.4-8.0]), respectively. There was a statistically significant greater likelihood of using said terms prelecture vs. postlecture (2.9 [95% CI, 2.4-3.4] vs. 7.5 [95% CI, 7.2-7.9], P < 0.001).

CONCLUSIONS

This study found a statistically significant improvement in neuro-ophthalmic knowledge and a greater likelihood of using NOP terms following the NOP curriculum. NFL game officials, replay officials, and staff are interested in expanding their knowledge in the vision science of neuro-ophthalmic concepts and applications involved in play-calling. We hope that our pilot data will lead to a model of education that will improve the precision and accuracy of NFL play-calls by officials on game days.

Ethical Considerations of Neuralink and Brain-Computer Interfaces

Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine.

Neuro-ophthalmic challenges and multi-morbidity in vasculitis among the older adults

INTRODUCTION

Vasculitides are a heterogeneous group of disorders producing inflammation of blood vessels (e.g. arteries or veins). All major vasculitides potentially have ophthalmological symptoms and signs including visual loss. Co-morbidity, multimorbidity, polypharmacy, and geriatric syndromes all play important roles in patient outcomes for these rheumatic conditions in the elderly. This monograph reviews the NCBI PubMed database (Feb 2023) literature on the neuro-ophthalmic and geriatric considerations in vasculitis.

AREAS COVERED

Cogan Syndrome, Granulomatosis with Polyangiitis, Giant Cell Arteritis, Polyarteritis Nodosa, Takayasu Arteritis, Vasculitis epidemiology, and neuro-ophthalmological symptoms.

EXPERT OPINION

Geriatric patient care for vasculitis with neuro-ophthalmological manifestations can be complicated by the interplay of multiple co-morbidities, polypharmacy, and specific geriatric syndromes. The valuation and treatment of vasculitis and the complications associated with the disease can negatively impact patient care. Advances in noninvasive imaging and updates in diagnostic criteria have enabled increased identification of patients at earlier stages with less severe disease burden. Novel therapeutic agents can be glucocorticoid sparing and might reduce the adverse effects of chronic steroid use. Holistic care models like the 5 M geriatric care model (mind, mobility, medications, multicomplexity, and matters most) allow patients' needs to be in the forefront with biopsychosocial aspects of a patient being addressed.

Concerns with OpenAI's Sora in Medicine

Open AI's Sora represents a ground-breaking innovation in AI that can generate lifelike and imaginative visual scenes based on text prompts. However, Sora has also produced some new concerns surrounding artificial video generation in medicine. While Sora is highly promising to enhance patient education, facilitate remote consultations and simulate surgical procedures, AI-generated videos also bring technical, legal, and ethical challenges. In this paper, we explore the clinical and ethical implications of Sora's AI-generated videos in the field of medicine.

SANS-CNN: An automated machine learning technique for spaceflight associated neuro-ocular syndrome with astronaut imaging data

Spaceflight associated neuro-ocular syndrome (SANS) is one of the largest physiologic barriers to spaceflight and requires evaluation and mitigation for future planetary missions. As the spaceflight environment is a clinically limited environment, the purpose of this research is to provide automated, early detection and prognosis of SANS with a machine learning model trained and validated on astronaut SANS optical coherence tomography (OCT) images. In this study, we present a lightweight convolutional neural network (CNN) incorporating an EfficientNet encoder for detecting SANS from OCT images titled "SANS-CNN." We used 6303 OCT B-scan images for training/validation (80%/20% split) and 945 for testing with a combination of terrestrial images and astronaut SANS images for both testing and validation. SANS-CNN was validated with SANS images labeled by NASA to evaluate accuracy, specificity, and sensitivity. To evaluate real-world outcomes, two state-of-the-art pre-trained architectures were also employed on this dataset. We use GRAD-CAM to visualize activation maps of intermediate layers to test the interpretability of SANS-CNN's prediction. SANS-CNN achieved 84.2% accuracy on the test set with an 85.6% specificity, 82.8% sensitivity, and 84.1% F1-score. Moreover, SANS-CNN outperforms two other state-of-the-art pre-trained architectures, ResNet50-v2 and MobileNet-v2, in accuracy by 21.4% and 13.1%, respectively. We also apply two class-activation map techniques to visualize critical SANS features perceived by the model. SANS-CNN represents a CNN model trained and validated with real astronaut OCT images, enabling fast and efficient prediction of SANS-like conditions for spaceflight missions beyond Earth's orbit in which clinical and computational resources are extremely limited.