AIMC Topic: Adolescent

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Use of Machine Learning to Determine Deviance in Neuroanatomical Maturity Associated With Future Psychosis in Youths at Clinically High Risk.

JAMA psychiatry
IMPORTANCE: Altered neurodevelopmental trajectories are thought to reflect heterogeneity in the pathophysiologic characteristics of schizophrenia, but whether neural indicators of these trajectories are associated with future psychosis is unclear.

MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.

Journal of digital imaging
Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269-1274, 2016; Arch ...

Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disability and suicide. In this paper, we explore anatomical features and apply machine learning approaches t...

Automated Assessment of Bone Age Using Deep Learning and Gaussian Process Regression.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Bone age is an essential measure of skeletal maturity in children with growth disorders. It is typically assessed by a trained physician using radiographs of the hand and a reference model. However, it has been described that the reference models lea...

A Two Stage Approach for the Automatic Detection of Insomnia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exist that can assist clinicians in the timely detection of insomnia. In this paper, we propose a two st...

Classifying Treated vs. Untreated MDD Adolescents from Anatomical Connectivity using Nonlinear SVM.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Identification of the treatment-related responders for adolescent Major Depressive Disorder (MDD) is urgently needed to develop effective treatments. In this paper, machine learning based classifiers are used to reveal anatomical features as responde...

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

Medical image analysis
fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional...

Hookworm Detection in Wireless Capsule Endoscopy Images With Deep Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
As one of the most common human helminths, hookworm is a leading cause of maternal and child morbidity, which seriously threatens human health. Recently, wireless capsule endoscopy (WCE) has been applied to automatic hookworm detection. Unfortunately...

Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks.

Abdominal radiology (New York)
The purpose of this pilot study is to determine whether a deep convolutional neural network can be trained with limited image data to detect high-grade small bowel obstruction patterns on supine abdominal radiographs. Grayscale images from 3663 clini...

Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation.

Neuroinformatics
The advances in neuroimaging methods reveal that resting-state functional fMRI (rs-fMRI) connectivity measures can be potential diagnostic biomarkers for autism spectrum disorder (ASD). Recent data sharing projects help us replicating the robustness ...