No abstract

Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing/harmonizing images, improving image quality, lowering radiation and contrast dose, and shortening the duration of imaging studies. This article will address this topic and will seek to present an overview of deep learning applied to neuroimaging techniques.

OBJECTIVES/GOALS: Klotho is a protein linked to improved cognition in aging adults. A specific KLOTHO gene variant, KL-VS, increases circulating levels of Klotho. The current study aims to identify if the KL-VS haplotype and Klotho levels are associated with improved neurocognition in pediatric brain tumor survivors. METHODS/STUDY POPULATION: We are actively accruing pediatric brain tumor patients at UCSF alongside an existing multi-institutional cohort study investigating radiation-induced vasculopathies and cognitive outcomes in this population. Normal controls are being enrolled in parallel. Each patient undergoes: 1) single nucleotide polymorphism genotyping to identify KL-VS haplotype status, 2) enzyme-linked immunosorbent assays to measure circulating Klotho, 3) neurocognitive assessments with a computer-based, validated Cogstate battery, and 4) brain volume and white matter lesion segmentation analyses using MRI sequences obtained as part of routine care. RESULTS/ANTICIPATED RESULTS: Genotyping has been performed on 99 enrolled patients. KL-VS heterozygosity was seen in 22.7% of patients. To date, KL-VS status is not associated with neurocognitive outcomes at baseline or Year 1 testing. Association between KL-VS status, circulating Klotho levels, neurocognitive outcomes, brain volume and white matter lesion segmentation analyses is ongoing. We hypothesize that elevated Klotho levels will be associated with improved neurocognition, increased brain volumes in regions of interest and decreased white matter lesion volumes. DISCUSSION/SIGNIFICANCE OF IMPACT: If circulating Klotho leads to improved neurocognition in pediatric brain tumor survivors, Klotho levels might serve as a prognostic biomarker. Furthermore, as Klotho is being investigated for therapeutic indications, it may represent an intervention to prevent cognitive deficits in these patients.

Aim: The aim of this study is to develop and optimize artificial neural network models for accurate prediction of premature ovarian failure (POF), to test these models on data collected prospectively from different centres. Materials and Methods: The study used data from 316 women presenting to six communities governed by a street in Wuhan, Hubei, China. Unbiased randomization was divided into training samples (177 cases), test samples (44 cases), and adherence samples (95 cases). Data from training samples and test samples were used to train the models, which were then tested on independent data from adherence samples. From 35 potential factors, variables were selected by Analytic Hierarchy Process (AHP), and then were used in the ANN model to make the prediction. Results: The predicting accuracy of the train set, validation set, and test set were 98.73%, 94.15%, and 92.15%, respectively, when the generalization ability was verified. Conclusion: This study confirms that artificial neural network can offer a useful approach for developing diagnostic algorithms for POF prediction.