Application of Artificial Intelligence in the Early Detection of Retinopathy of Prematurity: Review of the Literature.

Retinopathy of prematurity (ROP) is a potentially blinding disease in premature neonates that requires a skilled workforce for diagnosis, monitoring, and treatment. Artificial intelligence is a valuable tool that clinicians employ to reduce the screening burden on ophthalmologists and neonatologists and improve the detection of treatment-requiring ROP. Neural networks such as convolutional neural networks and deep learning (DL) systems are used to calculate a vascular severity score (VSS), an important component of various risk models. These DL systems have been validated in various studies, which are reviewed here. Most importantly, we discuss a promising study that validated a DL system that could predict the development of ROP despite a lack of clinical evidence of disease on the first retinal examination. Additionally, there is promise in utilizing these systems through telemedicine in more rural and resource-limited areas. This review highlights the value of these DL systems in early ROP diagnosis.

The association between insurance status and diagnostic imaging for acute abdominal pain among emergency department patients in the United States, 2005-2014.

Introduction and objectives: Acute abdominal pain (AAP) is one of the most common complaints in the emergency department (ED). Rapid diagnosis is essential and is often achieved through imaging. Computed tomography (CT) is widely considered an exemplary test in the diagnosis of AAP in adult patients. As previous studies show disparities in healthcare treatment based on insurance status, our objective was to assess the association between insurance status and frequency of CT ordered for adult patients presenting to the ED with AAP from 2005 to 2014. Methods: This study used the National Hospital and Ambulatory Medical Care Survey: Emergency Department Record (NHAMCS) database, which collects data over a randomly assigned 4 week period in the 50 states and DC, to perform an observational retrospective analysis of patients presenting to the ED with AAP. Patients with Medicaid, Medicare or no insurance were compared to patients with private insurance. The association between insurance status and frequency of CT ordered was measured by obtaining odds ratios along with 95% CIs adjusted for age, gender and race/ethnicity. Results: Individuals receiving Medicaid are 20% less likely to receive CT than those with private insurance (OR 0.8, CI 0.6-0.99, p = .046). Those on Medicare or who are uninsured have no difference in odds of obtaining a CT scan compared to patients with private insurance. Additional findings are that black patients are 42% less likely to receive a CT scan than white patients. Conclusions and implications: Patients on Medicaid are significantly less likely to receive a CT when presenting to the ED with AAP. Differences in diagnostic care may correlate to inferior health outcomes in patients without private insurance.