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AI-Driven Innovation for Next-Generation Vision Healthcare: A First Step Toward Intelligent and Proactive Eye Care Solutions
(2025-04-01) Dr. David Marvin Hart and Saumya Singh Jaiswal
Abstract
Diabetic retinopathy (DR) is a leading cause of preventable blindness, and deep learning has shown promise in automating its diagnosis. However, most models treat retinal images as static inputs, overlooking the temporal nature of disease progression. In this work, we propose a Temporal Vision Recurrent Transformer (TVRT): a hybrid architecture combining a fine-tuned ViT-Tiny backbone with a bidirectional LSTM, to capture both spatial features and temporal evolution from fundus image sequences.
To address the lack of temporal data in the APTOS 2019 dataset, we introduce two synthetic sequence generation methods: (1) stage-based augmentation using contrast and geometric transformations to mimic progressive DR stages, and (2) neural style transfer to simulate intra-stage variability using higher-stage fundus images as style references.
Experimental results show that while ViT and ResNet perform well on static classification, TVRT significantly outperforms them on progression modeling, achieving an F1-score of 0.86 on synthetic sequences with 5+ timesteps. Furthermore, soft attention maps derived from the ViT encoder provide interpretable visualizations that highlight clinically relevant features like hemorrhages and exudates. Our findings suggest that temporal modeling not only enhances predictive accuracy but also improves interpretability, offering a promising direction for intelligent, progression-aware eye care systems.
The Role of Nutrition Education in Reducing Pregnancy Complications
(2025-04-22) Lindsay Torruella
False Narratives in Female Adolescent Skincare
(2025-04) Elizabeth Money
Failing to utilize sun-protective behaviors in adolescence is associated with an increased incidence of skin cancer in adulthood. An age-appropriate skincare routine in adolescence emphasizes the daily use of sunscreen; however, persuasive and influential messages from social media promote expensive, unnecessary, and potentially harmful routines and products. Due to developmental age and low e-health literacy levels, adolescents are vulnerable to misinformation. Through the implementation of this pre- and post-test quasi-experimental mixed-method non-control quality improvement project, the author wanted to determine if an educational series, consisting of one in-person and four subsequent virtual sessions, would increase the intent to use sun-protective behaviors, increase e-health literacy, and decrease the intent to use inappropriate skincare routines and products endorsed by social media. The sample consisted of middle and high school adolescents aged 11-18. Although the sample size was small: n=6, limiting most generalizability and statistical significance, there were several significant clinical takeaways following implementation. Within the sample, there was positive behavior change intent with increased sun-protective behaviors and decreased perception of tanned skin as "healthy;" however, results were mixed with self-assessed e-health literacy. In addition, the data also revealed a significant contrast between the concepts of "skincare" and "social media and skincare."
Executive Summary: Utilizing Motivational Interviewing to Increase Pediatric Influenza Vaccine Uptake
(2025-04-22) Nolan, Alyssa Jane
DNP Executive Summary: Use for the paper required to fulfill the Doctor of Nursing Practice program.