A Deep Learning Frame-Work for Recognizing Developmental Disorders

Shukla, Pushkar; Gupta, Tanu; Saini, Aradhya; Singh, Priyanka; Raman, Balasubramanian

doi:10.1109/wacv.2017.84

Cited by 63 publications

(42 citation statements)

References 27 publications

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“…Their results show that different facial attributes are statistically significant and improve classification performance by about 7%. A deep convolutional neural network (DCNN) for feature extraction followed by an SVM for classification has been trained by Shukla et al 60 to detect whether a person in an image has ASD, cerebral palsy, Down syndrome, foetal alcohol spectrum syndrome, progeria or other intellectual disabilities. Their results indicate that their model has an accuracy of 98.80% and performs better than average human intelligence in distinguishing between different disorders.…”

Section: Facial Expression/emotionmentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included [‘autis*’ AND (‘computer vision’ OR ‘behavio* imaging’ OR ‘behavio* analysis’ OR ‘affective computing’)]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.

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Section: Facial Expression/emotionmentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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“…Only a few studies (Gurovich et al, 2019;Mor & Dardeck, 2018;Rad et al, 2018;Shukla, Gupta, Saini, Singh, & Balasubramanian, 2017) is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted September 29, 2020. ; https://doi.org/10.1101/2020.09.29.20203810 doi: medRxiv preprint disorders because neuroimaging is rarely used in psychology because of its high cost (Galatzer-Levy et al, 2014). Mor and Dardeck (2018)…”

Section: Deep Learning and Diagnosismentioning

confidence: 99%

“…Only a few studies (Gurovich et al, 2019; Mor & Dardeck, 2018; Rad et al, 2018; Shukla, Gupta, Saini, Singh, & Balasubramanian, 2017) have been published on using deep learning that do not employ neuroimaging to flag possible mental disorders. This fact impedes the implementation of deep learning in the diagnostic screening process of mental disorders because neuroimaging is rarely used in psychology because of its high cost (Galatzer-Levy et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Mor and Dardeck (2018) succeeded in identifying people at risk for Post Traumatic Stress Disorder (PTSD) using readily collected ecological risk factors and deep learning. Shukla et al (2017) succeeded in detecting developmental disorders from facial images using deep learning. They succeeded in building a deep learning model that performs better than humans in recognizing and differentiating among a spectrum of neurodevelopmental disorders including autism spectrum disorder, fetal alcohol syndrome, Down syndrome, progeria, cerebral palsy, and intellectual disability.…”

Section: Introductionmentioning

confidence: 99%

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Applying Deep Learning to Specific Learning Disorder Screening

Mor¹,

Dardeck

2020

Preprint

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Early detection is key for treating those diagnosed with specific learning disorder, which includes problems with spelling, grammar, punctuation, clarity and organization of written expression. Intervening early can prevent potential negative consequences from this disorder. Deep convolutional neural networks (CNNs) perform better than human beings in many visual tasks such as making a medical diagnosis from visual data. The purpose of this study was to evaluate the ability of a deep CNN to detect students with a diagnosis of specific learning disorder from their handwriting. The MobileNetV2 deep CNN architecture was used by applying transfer learning. The model was trained using a data set of 497 images of handwriting samples from students with a diagnosis of specific learning disorder, as well as those without this diagnosis. The detection of a specific learning disorder yielded on the validation set a mean area under the receiver operating characteristics curve of 0.89. This is a novel attempt to detect students with the diagnosis of specific learning disorder using deep learning. Such a system as was built for this study, may potentially provide fast initial screening of students who may meet the criteria for a diagnosis of specific learning disorder.

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“…It has been shown that they can be trained to be highly robust for imaging variation, reducing the need for highly controlled subject poses (Xiangyu Zhu et al, 2015). There are a number of current research and commercial efforts to create fully automated analysis pipelines for clinical interpretation of dysmorphologies (Ansari et al, 2014; Ferry et al, 2014; Manousaki et al, 2015; Basel-Vanagaite et al, 2016; Gripp et al, 2016; Baynam et al, 2017; Bengani et al, 2017; Dudding-Byth et al, 2017; Deciphering Developmental Disorders Study, 2017; Gardner et al, 2017; Hadj-Rabia et al, 2017; Kruszka et al, 2017a; Kruszka et al, 2017b; Kruszka et al, 2017c; Lumaka et al, 2017; Shukla et al, 2017; Valentine et al, 2017; Reijnders et al, 2018b; Gurovich et al, 2018; Knaus et al, 2018; Kruszka et al, 2018; Liehr et al, 2018; Pantel et al, 2018; Reijnders et al, 2018a; Reijnders et al, 2018b; Zarate et al, 2018). However, all these efforts are meeting the same barriers to progression of the methods and prospects for clinical impact, challenges to do with data access, ethics, governance, and security.…”

Section: Phenotyping From Photographsmentioning

confidence: 99%

Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative

Nellåker¹,

Alkuraya²,

Baynam³

et al. 2019

Front. Genet.

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The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

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A Deep Learning Frame-Work for Recognizing Developmental Disorders

Cited by 63 publications

References 27 publications

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Applying Deep Learning to Specific Learning Disorder Screening

Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative

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