scholarly journals Elucidating a Magnetic Resonance Imaging-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms

2009 ◽  
Vol 66 (11) ◽  
pp. 1055-1060 ◽  
Author(s):  
Daqiang Sun ◽  
Theo G.M. van Erp ◽  
Paul M. Thompson ◽  
Carrie E. Bearden ◽  
Melita Daley ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Magnetic Resonance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Brain Atlas ◽  
Resonance Imaging ◽  
Classification Analysis

scholarly journals Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies

10.2196/14108 ◽  
2019 ◽  
Vol 6 (12) ◽  
pp. e14108 ◽  
Author(s):  
Sun Jae Moon ◽  
Jinseub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Systematic Review ◽  
Magnetic Resonance ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Resonance Imaging ◽  
Accuracy Measures

Background In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy. Objective This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD. Methods The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC). Results A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67. Conclusions The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings. Trial Registration PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779

scholarly journals Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning

2017 ◽  
Vol 56 (6) ◽  
pp. 806-812 ◽  
Author(s):  
Turid Torheim ◽  
Eirik Malinen ◽  
Knut Håkon Hole ◽  
Kjersti Vassmo Lund ◽  
Ulf G. Indahl ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Cervical Cancers ◽  
Multiparametric Magnetic Resonance Imaging

scholarly journals A Baboon Brain Atlas for Magnetic Resonance Imaging and Positron Emission Tomography Image Analysis

2022 ◽  
Vol 15 ◽  
Author(s):  
Artur Agaronyan ◽  
Raeyan Syed ◽  
Ryan Kim ◽  
Chao-Hsiung Hsu ◽  
Scott A. Love ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Positron Emission Tomography ◽  
Magnetic Resonance ◽  
Standardized Uptake Value ◽  
Emission Tomography ◽  
Brain Atlas ◽  
Resonance Imaging ◽  
Papio Anubis ◽  
Olive Baboon ◽  
Positron Emission

The olive baboon (Papio anubis) is phylogenetically proximal to humans. Investigation into the baboon brain has shed light on the function and organization of the human brain, as well as on the mechanistic insights of neurological disorders such as Alzheimer’s and Parkinson’s. Non-invasive brain imaging, including positron emission tomography (PET) and magnetic resonance imaging (MRI), are the primary outcome measures frequently used in baboon studies. PET functional imaging has long been used to study cerebral metabolic processes, though it lacks clear and reliable anatomical information. In contrast, MRI provides a clear definition of soft tissue with high resolution and contrast to distinguish brain pathology and anatomy, but lacks specific markers of neuroreceptors and/or neurometabolites. There is a need to create a brain atlas that combines the anatomical and functional/neurochemical data independently available from MRI and PET. For this purpose, a three-dimensional atlas of the olive baboon brain was developed to enable multimodal imaging analysis. The atlas was created on a population-representative template encompassing 89 baboon brains. The atlas defines 24 brain regions, including the thalamus, cerebral cortex, putamen, corpus callosum, and insula. The atlas was evaluated with four MRI images and 20 PET images employing the radiotracers for [11C]benzamide, [11C]metergoline, [18F]FAHA, and [11C]rolipram, with and without structural aids like [18F]flurodeoxyglycose images. The atlas-based analysis pipeline includes automated segmentation, registration, quantification of region volume, the volume of distribution, and standardized uptake value. Results showed that, in comparison to PET analysis utilizing the “gold standard” manual quantification by neuroscientists, the performance of the atlas-based analysis was at >80 and >70% agreement for MRI and PET, respectively. The atlas can serve as a foundation for further refinement, and incorporation into a high-throughput workflow of baboon PET and MRI data. The new atlas is freely available on the Figshare online repository (https://doi.org/10.6084/m9.figshare.16663339), and the template images are available from neuroImaging tools & resources collaboratory (NITRC) (https://www.nitrc.org/projects/haiko89/).

Sign in / Sign up

Export Citation Format

Share Document