MADE-for-ASD: A multi-atlas deep ensemble network for diagnosing Autism Spectrum Disorder

In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), this paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple...

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Veröffentlicht in:Computers in biology and medicine 2024-11, Vol.182, p.109083, Article 109083
Hauptverfasser: Liu, Xuehan, Hasan, Md Rakibul, Gedeon, Tom, Hossain, Md Zakir
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creator Liu, Xuehan
Hasan, Md Rakibul
Gedeon, Tom
Hossain, Md Zakir
description In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), this paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple atlases of the brain’s functional magnetic resonance imaging (fMRI) data through a weighted deep ensemble network. Our approach integrates demographic information into the prediction workflow, which enhances ASD diagnosis performance and offers a more holistic perspective on patient profiling. We experiment with the well-known publicly available ABIDE (Autism Brain Imaging Data Exchange) I dataset, consisting of resting state fMRI data from 17 different laboratories around the globe. Our proposed system achieves 75.20% accuracy on the entire dataset and 96.40% on a specific subset — both surpassing reported ASD diagnosis accuracy in ABIDE I fMRI studies. Specifically, our model improves by 4.4 percentage points over prior works on the same amount of data. The model exhibits a sensitivity of 82.90% and a specificity of 69.70% on the entire dataset, and 91.00% and 99.50%, respectively, on the specific subset. We leverage the F-score to pinpoint the top 10 ROI in ASD diagnosis, such as precuneus and anterior cingulate/ventromedial. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis. Codes and evaluations are publicly available at https://github.com/hasan-rakibul/MADE-for-ASD. •Introduces MADE-for-ASD, a deep ensemble network for fMRI-based ASD diagnosis.•Achieves 75.20% accuracy, surpassing prior work by 4.4 percentage points on ABIDE I.•Demonstrates robust sensitivity (82.90%) and specificity (69.70%) across datasets.•Identifies key regions of interest like precuneus and anterior cingulate/ventromedial.•Provides open-access code to promote reproducibility and further research.
doi_str_mv 10.1016/j.compbiomed.2024.109083
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The model exhibits a sensitivity of 82.90% and a specificity of 69.70% on the entire dataset, and 91.00% and 99.50%, respectively, on the specific subset. We leverage the F-score to pinpoint the top 10 ROI in ASD diagnosis, such as precuneus and anterior cingulate/ventromedial. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis. 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subjects Autism
Autism Spectrum Disorder - diagnosis
Autism Spectrum Disorder - diagnostic imaging
Autism Spectrum Disorder - physiopathology
Brain
Brain - diagnostic imaging
Brain - physiopathology
Brain mapping
Child
Computer vision
Cortex (parietal)
Data exchange
Databases, Factual
Datasets
Deep learning
Depth profiling
Diagnosis
Female
Functional magnetic resonance imaging
Health computing
Humans
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical imaging
Neuroimaging
Workflow
title MADE-for-ASD: A multi-atlas deep ensemble network for diagnosing Autism Spectrum Disorder
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