Genome-wide association identifies novel ROP risk loci in a multiethnic cohort.

We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 1 locus at genome-wide significance level (p < 5×10) and 9 with significance of p < 5×10 for ROP ≥ stage 3. The most significant locus, rs2058019, reached genome-wide significance within the full multiethnic cohort (p = 4.96×10); Hispanic and European Ancestry infants driving the association.

Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea.

Goal And Aims: Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification.

Focus Technology: Belun Ring with second-generation deep learning algorithms REFERENCE TECHNOLOGY: In-lab polysomnography (PSG) SAMPLE: Eighty-four subjects (M: F = 1:1) referred for an overnight sleep study were eligible. Of these, 26% had PSG-AHI<5; 24% had PSG-AHI 5-15; 23% had PSG-AHI 15-30; 27% had PSG-AHI ≥ 30.

Development of an Open-Source Annotated Glaucoma Medication Dataset From Clinical Notes in the Electronic Health Record.

Purpose: To describe the methods involved in processing and characteristics of an open dataset of annotated clinical notes from the electronic health record (EHR) annotated for glaucoma medications.

Methods: In this study, 480 clinical notes from office visits, medical record numbers (MRNs), visit identification numbers, provider names, and billing codes were extracted for 480 patients seen for glaucoma by a comprehensive or glaucoma ophthalmologist from January 1, 2019, to August 31, 2020. MRNs and all visit data were de-identified using a hash function with salt from the deidentifyr package.

The clinical and molecular characteristics of progressive hypothalamic/optic pathway pilocytic astrocytoma.

Background: Unresectable hypothalamic/optic pathway pilocytic astrocytoma (PA) often progresses despite multiple therapies. Identifying clinical and molecular characteristics of progressive tumors may aid in prognostication and treatment.

Methods: We collected 72 unresectable, non-neurofibromatosis type 1-associated hypothalamic/optic pathway PA to identify clinical and biologic factors associated with tumor progression.

Multimodality Imaging in Primary Progressive Aphasia.

Primary progressive aphasia is a clinically and neuropathologically heterogeneous group of progressive neurodegenerative disorders, characterized by language-predominant impairment and commonly associated with atrophy of the dominant language hemisphere. While this clinical entity has been recognized dating back to the 19th century, important advances have been made in defining our current understanding of primary progressive aphasia, with 3 recognized subtypes to date: logopenic variant, semantic variant, and nonfluent/agrammatic variant. Given the ongoing progress in our understanding of the neurobiology and genomics of these rare neurodegenerative conditions, accurate imaging diagnoses are of the utmost importance and carry implications for future therapeutic triaging.

Clinical sequencing of soft tissue and bone sarcomas delineates diverse genomic landscapes and potential therapeutic targets.

The genetic, biologic, and clinical heterogeneity of sarcomas poses a challenge for the identification of therapeutic targets, clinical research, and advancing patient care. Because there are > 100 sarcoma subtypes, in-depth genetic studies have focused on one or a few subtypes. Herein, we report a comparative genetic analysis of 2,138 sarcomas representing 45 pathological entities.

Spontaneous hyphema in the setting of COVID-19 pneumonia.

Purpose: To report a challenging case of spontaneous hyphema in the setting of prone positioning for COVID-19 pneumonia.

Observations: A previously healthy patient was concomitantly diagnosed with acute myelogenous leukemia (AML) and COVID-19 infection. During his hospitalization he required intubation and prone positioning.

Contrast-Enhanced Ultrasound Findings in Patients with Rare Solitary Necrotic Nodule of the Liver - a Multicenter Report.

Purpose:  This multicenter retrospective study highlights the contrast-enhanced ultrasound (CEUS) findings in a series of histologically proven solitary necrotic nodules (SNN) of the liver, a poorly understood pathologic entity of uncertain origin that mimics malignancy.

Materials And Methods:  22 patients (M/F 13/9; mean age 59.4 years, SD ± 10.

Impact of Artificial Intelligence on Medical Education in Ophthalmology.

Clinical care in ophthalmology is rapidly evolving as artificial intelligence (AI) algorithms are being developed. The medical community and national and federal regulatory bodies are recognizing the importance of adapting to AI. However, there is a gap in physicians' understanding of AI and its implications regarding its potential use in clinical care, and there are limited resources and established programs focused on AI and medical education in ophthalmology.

Reproducibility and reliability analysis of the Luk Distal Radius and Ulna Classification for European patients with adolescent idiopathic scoliosis.

Purpose: Current clinical and radiological methods of predicting a patient's growth potential are limited in terms of practicality, accuracy and known to differ in different races. This information influences optimal timing of bracing and surgical intervention in adolescent idiopathic scoliosis (AIS). The Luk classification was developed to mitigate limitations of existing tools.

Disruption of the Atrophy-based Functional Network in Multiple Sclerosis Is Associated with Clinical Disability: Validation of a Meta-Analytic Model in Resting-State Functional MRI.

Background In multiple sclerosis (MS), gray matter (GM) atrophy exhibits a specific pattern, which correlates strongly with clinical disability. However, the mechanism of regional specificity in GM atrophy remains largely unknown. Recently, the network degeneration hypothesis (NDH) was quantitatively defined (using coordinate-based meta-analysis) as the atrophy-based functional network (AFN) model, which posits that localized GM atrophy in MS is mediated by functional networks.

Ranibizumab Population Pharmacokinetics and Free VEGF Pharmacodynamics in Preterm Infants With Retinopathy of Prematurity in the RAINBOW Trial.

Purpose: To develop a population pharmacokinetic (PK) model for intravitreal ranibizumab in infants with retinopathy of prematurity (ROP) and assess plasma free vascular endothelial growth factor (VEGF) pharmacodynamics (PD).

Methods: The RAnibizumab compared with laser therapy for the treatment of INfants BOrn prematurely With retinopathy of prematurity (RAINBOW) trial enrolled 225 infants to receive a bilateral intravitreal injection of ranibizumab 0.1 mg, ranibizumab 0.

Introduction to Machine Learning, Neural Networks, and Deep Learning.

Purpose: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning.

Methods: A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology.

Results: A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background.

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effective secondary use of this EHR data for improving patient care and facilitating clinical decision-making has remained challenging due to the complexity and heterogeneity of these data.

Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach.

Purpose: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images.

Artificial Intelligence in Retinopathy of Prematurity Diagnosis.

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The diagnosis of ROP is subclassified by zone, stage, and plus disease, with each area demonstrating significant intra- and interexpert subjectivity and disagreement. In addition to improved efficiencies for ROP screening, artificial intelligence may lead to automated, quantifiable, and objective diagnosis in ROP.

Artificial intelligence for retinopathy of prematurity.

Purpose Of Review: In this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provide insight on challenges as well as strategies for bringing these algorithms to the bedside.

Recent Findings: In the past few years, there has been a dramatic shift from machine learning approaches based on feature extraction to 'deep' convolutional neural networks for artificial intelligence applications. Several artificial intelligence for ROP approaches have demonstrated adequate proof-of-concept performance in research studies.

Sonothrombolysis Improves Myocardial Dynamics and Microvascular Obstruction Preventing Left Ventricular Remodeling in Patients With ST Elevation Myocardial Infarction.

Background: It has recently been demonstrated that high-energy diagnostic transthoracic ultrasound and intravenous microbubbles dissolve thrombi (sonothrombolysis) and increase angiographic recanalization rates in patients with ST-segment-elevation myocardial infarction. We aimed to study the effect of sonothrombolysis on the myocardial dynamics and infarct size obtained by real-time myocardial perfusion echocardiography and their value in preventing left ventricular remodeling.

Methods: One hundred patients with ST-segment-elevation myocardial infarction were randomized to therapy (50 patients treated with sonothrombolysis and percutaneous coronary intervention) or control (50 patients treated with percutaneous coronary intervention only).