Automated detection of ADHD: Current trends and future perspective

ASIAIR > College of Computer Science > Department of Biomedical informatics > Journal Article > Item 310904400/115048

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/115048

Title:	Automated detection of ADHD: Current trends and future perspective
Authors:	Loh, Hui Wen;Loh, Hui Wen;Oo, Chui Ping;Ooi, Chui Ping;Datta, Prabal;Barua, Prabal Datta;Elizabeth, Elizabeth E.;Palmer, Elizabeth E.;Moli, Filippo;Molinari, Filippo;Rajendra, U.;Acharya, U. Rajendra
Contributors:	資訊電機學院生物資訊與醫學工程學系
Keywords:	Accelerometer;Actigraphy;Artificial intelligence;Attention deficit hyperactivity disorder (ADHD);CPT;Deep learning;ECG;EEG;Genetic;HRV;MRI;Machine learning;PRISMA;Pupillometric;Questionnaires;RST;Social media.
Date:	2022-04-01
Issue Date:	2023-03-28 02:06:53 (UTC+0)
Publisher:	亞洲大學
Abstract:	Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit combinations of inattention, impulsiveness, and hyperactivity. With early treatment and diagnosis, there is potential to modify neuronal connections and improve symptoms. However, the heterogeneous nature of ADHD, combined with its comorbidities and a global shortage of diagnostic clinicians, means diagnosis of ADHD is often delayed. Hence, it is important to consider other pathways to improve the efficiency of early diagnosis, including the role of artificial intelligence. In this study, we reviewed the current literature on machine learning and deep learning studies on ADHD diagnosis and identified the various diagnostic tools used. Subsequently, we categorized these studies according to their diagnostic tool as brain magnetic resonance imaging (MRI), physiological signals, questionnaires, game simulator and performance test, and motion data. We identified research gaps include the paucity of publicly available database for all modalities in ADHD assessment other than MRI, as well as a lack of focus on using data from wearable devices for ADHD diagnosis, such as ECG, PPG, and motion data. We hope that this review will inspire future work to create more publicly available datasets and conduct research for other modes of ADHD diagnosis and monitoring. Ultimately, we hope that artificial intelligence can be extended to multiple ADHD diagnostic tools, allowing for the development of a powerful clinical decision support pathway that can be used both in and out of the hospital.
Appears in Collections:	[Department of Biomedical informatics ] Journal Article

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