Automated detection of ADHD: Current trends and future perspective

ASIA unversity > 資訊學院 > 生物資訊與醫學工程學系 > 期刊論文 > Item 310904400/115048

請使用永久網址來引用或連結此文件: http://asiair.asia.edu.tw/ir/handle/310904400/115048

題名:	Automated detection of ADHD: Current trends and future perspective
作者:	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
貢獻者:	資訊電機學院生物資訊與醫學工程學系
關鍵詞:	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.
日期:	2022-04-01
上傳時間:	2023-03-28 02:06:53 (UTC+0)
出版者:	亞洲大學
摘要:	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.
顯示於類別:	[生物資訊與醫學工程學系 ] 期刊論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
index.html		0Kb	HTML	59	檢視/開啟

在ASIAIR中所有的資料項目都受到原著作權保護.

資料載入中.....