Computational Intelligence in Detection and Support of Autism Spectrum Disorder

Ahmed, Shamsuddin; Nur, Silvia Binte; Hossain, Md. Farhad; Kaiser, M. Shamim; Mahmud, Mufti; Chen, Tianhua

doi:10.1007/978-981-19-5272-2_9

Cited by 4 publications

(2 citation statements)

References 73 publications

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“…In order to handle these challenges and coupled with the fact that Artificial Intelligence (AI) is enjoying an increasing number of successes in medical applications (13)(14)(15)(16)), an AI system, called NeuroIntel, was developed. For this work, clinical information collected from an NHS adult ADHD Service, which delivers a clinical pathway compliant with NICE recommendations (i.e., the gold standard), was used for creating a decision support tool that can first automate the process of making a diagnosis and second prioritize the ADHD cases based on levels of complexity.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK

et al. 2023

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Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learning model and a knowledge-based model) for assisting adult ADHD diagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future.

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Section: Introductionmentioning

confidence: 99%

Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK

et al. 2023

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“…Across various works, investigational modalities, and jobs, persons with ASD display a transformed interest in public data matched to non-ASD controls. Eye tracking (ET) provides insight into social attention by permitting accurate moment-by-moment quantification of gaze paradigms of persons since they visually processed social data [7]. As ET can be scalable, safe, non-invasive, and simply accepted by contributors from infancy over maturity and through various functions which include important cognitive impairment [8].…”

Section: Introductionmentioning

confidence: 99%

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Thanarajan,

Alotaibi,

Rajendran

et al. 2023

Computers, Materials &Amp; Continua

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Autism spectrum disorder (ASD) can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics, like changes in behavior, social disabilities, and difficulty communicating with others. Eye tracking (ET) has become a useful method to detect ASD. One vital aspect of moral erudition is the aptitude to have common visual attention. The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection. Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD, but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection. It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism. The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking. The Chaotic Butterfly Optimization technique is used to identify this specific disturbance. Therefore, this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning (ETASD-CBODL) technique. The presented ETASD-CBODL technique mainly focuses on the recognition of ASD via the ET and DL models. To accomplish this, the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS. In addition, the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization. Finally, the long-shortterm memory (LSTM) model is exploited for the recognition and classification of ASD. To assess the performance of the ETASD-CBODL technique, a series of simulations were performed on datasets from the figure-shared data repository. The experimental values of accuracy (99.29%), precision (98.78%), sensitivity (99.29%) and specificity (99.29%) showed a better performance in the ETASD-CBODL technique over recent approaches.

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Machine Learning-based Evaluation of Heart Rate Variability Response in Children with Autism Spectrum Disorder

Mohammed

et al. 2023

2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)

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Computational Intelligence in Detection and Support of Autism Spectrum Disorder

Cited by 4 publications

References 73 publications

Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK

Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK

Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic Butterfly Optimization with Deep Learning Model

Machine Learning-based Evaluation of Heart Rate Variability Response in Children with Autism Spectrum Disorder

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