Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework

Xie, Jin; Wang, Long; Webster, Paula; Yang, Yao; Sun, Jiayao; Wang, Shuo; Zhou, Huihui

doi:10.1007/s12539-022-00510-6

Cited by 7 publications

(3 citation statements)

References 83 publications

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“…With the advances of artificial intelligence and merging eye-tracking technology [ 62 ], ophthalmic photography has been increasingly used for the prediction of psychiatric disorders such as schizophrenia (SCZ) [ 36 , 63 ] and autism spectrum disorders [ 64 , 65 ]. For example, a deep learning algorithm developed by Appaji et al [ 36 ] achieved an AUC of 0.98 for classifying SCZ.…”

Section: Resultsmentioning

confidence: 99%

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

Cao

Grzybowski³

et al. 2023

Healthcare

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The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.

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Section: Resultsmentioning

confidence: 99%

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

Cao

Grzybowski³

et al. 2023

Healthcare

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“…These results imply that neural networks can learn useful facial risk markers of ASD. In other works, Xie et al [8] and Han et al [9] designed neural network models using ET data to predict ASD. Their experiments demonstrated that image data (face, ET) collected in vitro is a very promising direction for designing ASD CAD systems.…”

Section: Introductionmentioning

confidence: 99%

ViTASD: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis

Xu¹,

Ye²,

Sizikova³

et al. 2022

Preprint

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Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder with very high prevalence around the world. Research progress in the field of ASD facial analysis in pediatric patients has been hindered due to a lack of wellestablished baselines. In this paper, we propose the use of the Vision Transformer (ViT) for the computational analysis of pediatric ASD. The presented model, known as ViTASD, distills knowledge from large facial expression datasets and offers model structure transferability. Specifically, ViTASD employs a vanilla ViT to extract features from patients' face images and adopts a lightweight decoder with a Gaussian Process layer to enhance the robustness for ASD analysis. Extensive experiments conducted on standard ASD facial analysis benchmarks show that our method outperforms all of the representative approaches in ASD facial analysis, while the ViTASD-L achieves a new state-of-the-art. Our code and pretrained models are available at https://github.com/IrohXu/ViTASD.

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“…These risk markers learned by deep learning models can be used for early diagnosis of ASD in the future. (Xie et al, 2022) collect ET data from 39 samples and use VGG-19 to detect abnormal visual attention in ASD. (Han, Jiang, Ouyang, & Li, 2022) propose a multimodal deep learning model combining electroencephalogram (EEG) with ET to predict ASD.…”

mentioning

confidence: 99%

Commentary: Machine learning for autism spectrum disorder diagnosis – challenges and opportunities – a commentary on Schulte‐Rüther et al. (2022)

Cao

2023

Child Psychology Psychiatry

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The commentary cites a study by Schulte‐Rüther et al. (Journal of Child Psychology and Psychiatry, 2022) that proposed a machine learning model to predict a clinical best‐estimate diagnosis of ASD when existing other co‐occurring diagnoses. We discuss the valuable contribution of this work to developing a reliable computer‐assisted diagnosis (CAD) system for ASD and point out that related research can be integrated with other multimodal machine learning methods. For future studies on developing the CAD system for ASD, we propose problems that need to be solved and potential research directions.

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Identifying Visual Attention Features Accurately Discerning Between Autism and Typically Developing: a Deep Learning Framework

Cited by 7 publications

References 83 publications

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

ViTASD: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis

Commentary: Machine learning for autism spectrum disorder diagnosis – challenges and opportunities – a commentary on Schulte‐Rüther et al. (2022)

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