Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological and Neural Markers

Rostami, Mohammad; Farashi, Sajjad; Khosrowabadi, Reza; Pouretemad, Hamidreza

doi:10.32598/bcn.9.10.115

Cited by 9 publications

(20 citation statements)

References 31 publications

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“…The study protocol was registered at PROSPERO (CRD42022340624) and followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline (Figure 1…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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ImportanceNeuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.FindingsA total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).Conclusions and RelevanceThis systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

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“…The study protocol was registered at PROSPERO (CRD42022340624) and followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline (Figure 1…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

et al. 2023

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“…The third study applied a decision tree algorithm to resting state EEG to classify ADHD subtypes (including all three subtypes, ADHD-I, ADHD-H and ADHD-C) and controls (Rostami et al, 2020). The algorithm used a combination of the Child Behavior Checklist (CBCL), Integrated Visual and Auditory (IVA) test, and spectral power in the delta, theta, alpha and beta bands, as well as TBR.…”

Section: Resultsmentioning

confidence: 99%

“…The algorithm used a combination of the Child Behavior Checklist (CBCL), Integrated Visual and Auditory (IVA) test, and spectral power in the delta, theta, alpha and beta bands, as well as TBR. A perfect classification accuracy (100%) was attained for the categories of ADHD versus controls; the algorithm was also able to discriminate among the three ADHD subtypes with an accuracy of 80.41% (ADHD-C), 84.17% (ADHD-I), and 71.46% (ADHD-H), respectively (Rostami et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Can electroencephalography (EEG) identify ADHD subtypes? A systematic review

Slater

Joober

Koborsy

et al. 2022

Preprint

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Attention Deficit/Hyperactivity Disorder (ADHD) has been associated with atypical patterns of neural activity measured by electroencephalography (EEG). However, the identification of EEG diagnostic biomarkers has been complicated by the disorder's heterogeneity. The objective of this review was to synthesize the literature investigating EEG variation in patients diagnosed with ADHD, addressing the following questions: 1) Are the diagnostic ADHD subtypes associated with different EEG characteristics? 2) Are EEG measures correlated with ADHD traits and/or symptom severity? and 3) Do classification techniques using EEG measures reveal different clinical presentations of ADHD? Outcomes highlight the potential for electrophysiological measures to provide meaningful insights into the heterogeneity of ADHD, although direct translation of EEG biomarkers for diagnostic purposes is not yet supported. Key measures that show promise for the discrimination of existing ADHD subtypes and symptomatology include: resting state and task-related modulation of alpha, beta and theta power, and the event-related N2 and P3 components. Prescriptions are discussed for future studies that may help to bridge the gap between research and clinical application.

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“…They reported that the decision tree model yielded excellent classification accuracy (100%). Also, subtypes of ADHD can be distinguished by key nodes in decision-making rules such as behavioral, neuropsychiatric and electrophysiological parameters ( 28 ). New algorithms based on classical decision tree algorithms, including the ones using alternate decision trees, multi-class alternate decision trees, have been used to construct models based on genomic and magnetic resonance data.…”

Section: Supervised Learningmentioning

confidence: 99%

Application and research progress of machine learning in the diagnosis and treatment of neurodevelopmental disorders in children

et al. 2022

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The prevalence of neurodevelopment disorders (NDDs) among children has been on the rise. This has affected the health and social life of children. This condition has also imposed a huge economic burden on families and health care systems. Currently, it is difficult to perform early diagnosis of NDDs, which results in delayed intervention. For this reason, patients with NDDs have a prognosis. In recent years, machine learning (ML) technology, which integrates artificial intelligence technology and medicine, has been applied in the early detection and prediction of diseases based on data mining. This paper reviews the progress made in the application of ML in the diagnosis and treatment of NDDs in children based on supervised and unsupervised learning tools. The data reviewed here provide new perspectives on early diagnosis and treatment of NDDs.

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Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological and Neural Markers

Cited by 9 publications

References 31 publications

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis

Can electroencephalography (EEG) identify ADHD subtypes? A systematic review

Application and research progress of machine learning in the diagnosis and treatment of neurodevelopmental disorders in children

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