Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation.

In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis. This study proposes a Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction that focuses on high-risk regions. By learning spatial correlation in the detector, MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy.

Acquisition and Loss of Developmental Milestones and Time to Disease-Related Outcomes in Cerebral Alexander Disease.

To determine the ages at acquisition of developmental milestones, loss of motor function, and clinical symptoms in Alexander disease. Patients with confirmed cerebral Alexander disease were included. Data abstraction of developmental and disease-specific milestones was performed from medical records, physical exams, and questionnaires.

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT.

Background: Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS).

Methods: A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression.

Artificial intelligence for medical image analysis in epilepsy.

Given improvements in computing power, artificial intelligence (AI) with deep learning has emerged as the state-of-the art method for the analysis of medical imaging data and will increasingly be used in the clinical setting. Recent work in epilepsy research has aimed to use AI methods to improve diagnosis, prognosis, and treatment, with the ultimate goal of developing highly accurate and reliable tools to aid clinical decision making. Here, we review how researchers are currently using AI methods in the analysis of neuroimaging data in epilepsy, focusing on challenges unique to each imaging modality with an emphasis on clinical significance.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

Objectives: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU).

Methods: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation.

An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository.

Interocular Difference in Retinal Nerve Fiber Layer Thickness Predicts Optic Neuritis in Pediatric-Onset Multiple Sclerosis.

Background: Optical coherence tomography (OCT) is capable of quantifying retinal damage. Defining the extent of anterior visual pathway injury is important in multiple sclerosis (MS) as a way to document evidence of prior disease, including subclinical injury, and setting a baseline for patients early in the course of disease. Retinal nerve fiber layer (RNFL) thickness is typically classified as low if values fall outside of a predefined range for a healthy population.

Structural correlates of atypical visual and motor cortical oscillations in pediatric-onset multiple sclerosis.

We have previously demonstrated that pediatric-onset multiple sclerosis (POMS) negatively impacts the visual pathway as well as motor processing speed. Relationships between MS-related diffuse structural damage of gray and white matter (WM) tissue and cortical responses to visual and motor stimuli remain poorly understood. We used magnetoencephalography in 14 POMS patients and 15 age- and sex-matched healthy controls to assess visual gamma (30-80 Hz), motor gamma (60-90 Hz), and motor beta (15-30 Hz) cortical oscillatory responses to a visual-motor task.

Effect of age and neurofibromatosis type 1 status on white matter integrity in the optic radiations.

Background: Adults with neurofibromatosis type 1 (NF1) have decreased white matter integrity, but differences in children with NF1 have not been described. Defining normal values for diffusion tensor imaging (DTI) measures, especially in the optic radiations, is important to the development of DTI as a potential biomarker of visual acuity in children with optic pathway glioma. This study examines the effect of age and NF1 status on DTI measures in children.