Automated detection of ADHD: Current trends and future perspective. (July 2022)

• Record Type: Journal Article
• Authors: Loh, Hui Wen; Ooi, Chui Ping; Barua, Prabal Datta; Palmer, Elizabeth E.; Molinari, Filippo; Acharya, U Rajendra
• Is Part Of: Computers in biology and medicine. Volume 146(2022)
• Publication Date: 2022-07
• Subjects: Attention deficit hyperactivity disorder (ADHD) -- Deep learning -- Machine learning -- PRISMA -- MRI -- EEG -- ECG -- HRV -- Questionnaires -- CPT -- RST -- Accelerometer -- Actigraphy -- Pupillometric -- Genetic -- Social media -- Artificial intelligence; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105525
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: Review on studies that had used machine learning and deep learning techniques for ADHD diagnosis. Various diagnostic tools of ADHD were identified: MRI, EEG, questionnaires, motion data, performance test, etc. We discovered that, except for MRI, there are lack of publicly available datasets for all modalities used in ADHD assessment. ~75% of the studies proposed machine learning models, but deep learning models have grown in popularity in recent years. Data from wearable devices such as the ECG, PPG, and accelerometer are underutilized for ADHD diagnosis.