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.
Automated detection of ADHD: Current trends and future perspective / Loh, Hui Wen; Ooi, Chui Ping; Barua, Prabal Datta; Palmer, Elizabeth E; Molinari, Filippo; Acharya, U Rajendra. - In: COMPUTERS IN BIOLOGY AND MEDICINE. - ISSN 0010-4825. - ELETTRONICO. - 146:(2022), p. 105525. [10.1016/j.compbiomed.2022.105525]
Automated detection of ADHD: Current trends and future perspective
Molinari, Filippo;
2022
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.File | Dimensione | Formato | |
---|---|---|---|
1-s2.0-S0010482522003171-main-2.pdf
non disponibili
Descrizione: AHDH Review - post
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
5.12 MB
Formato
Adobe PDF
|
5.12 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
CIBM-S-22-01516 (1).pdf
Open Access dal 23/04/2023
Descrizione: ADHDRev - CBM2022 - Post
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
Creative commons
Dimensione
1.86 MB
Formato
Adobe PDF
|
1.86 MB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2974268