ADHD classification using auto-encoding neural network and binary hypothesis testing

Tang, Yibin; Sun, Jia; Wang, Chun; Zhong, Yuan; Jiang, Aimin; Liu, Gang; Liu, Xiaofeng

doi:10.1016/j.artmed.2021.102209

Cited by 28 publications

(30 citation statements)

References 41 publications

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“…One-hundred fifty of 555 models (27.0%; 95% CI, 23.3%-30.7%) were rated with poor reporting quality in the applications domain across 2 signaling questions. 26 , 28 , 29 , 32 , 36 , 38 , 41 , 45 , 50 , 51 , 60 , 65 , 69 , 74 , 78 , 82 , 84 , 94 , 97 , 99 , 105 , 107 , 108 , 112 , 117 , 119 , 120 , 125 , 132 , 133 , 134 , 138 , 139 , 141 , 143 , 146 , 150 , 159 , 172 , 183 , 186 , 190 , 193 , 194 , 200 , 203 , 204 , 214 , 219 , 220 , 222 , 223 , 228 , 229 , 230 , 232 , 233 , 241 ,…”

Section: Resultsmentioning

confidence: 99%

“…A total of 517 studies presenting 555 development-purpose AI models were eligible for inclusion in the analysis…”

Section: Resultsmentioning

confidence: 99%

“…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%

“… a Of the 555 artificial intelligence (AI) models, 469 were tested at the internal sample and 86 at the independent external sample, including intellectual disability, autism spectrum disorder, attention-deficit/hyperactivity disorder,…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…A total of 517 studies presenting 555 development-purpose AI models were eligible for inclusion in the analysis…”

Section: Resultsmentioning

confidence: 99%

“…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%

Section: Methodsmentioning

confidence: 99%

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

et al. 2023

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“…Moreover, HRV has been researched concerning ADHD, although the results are relatively less promising [ 1 ]. Generally, Electrocardiogram (ECG) [ 18 ], electroencephalogram (EEG) [ 19 , 20 ], magnetic resonance imaging (MRI) [ 21 , 22 ], game simulators [ 23 ], accelerometers [ 24 ] and many other methods are available for collecting sensory data. In practice, research is also carried out to analyze the EEG signal in the field of concentration of attention in mothers and children [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate Identification of ADHD among Adults Using Real-Time Activity Data

Kaur

Kahlon

2022

Brain Sciences

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Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopment disorder that affects millions of children and typically persists into adulthood. It must be diagnosed efficiently and consistently to receive adequate treatment, otherwise, it can have a detrimental impact on the patient’s professional performance, mental health, and relationships. In this work, motor activity data of adults suffering from ADHD and clinical controls has been preprocessed to obtain 788 activity-related statistical features. Afterwards, principal component analysis has been carried out to obtain significant features for accurate classification. These features are then fed into six different machine learning algorithms for classification, which include C4.5, kNN, Random Forest, LogitBoost, SVM, and Naive Bayes. The detailed evaluation of the results through 10-fold cross-validation reveals that SVM outperforms other classifiers with an accuracy of 98.43%, F-measure of 98.42%, sensitivity of 98.33%, specificity of 98.56% and AUC of 0.983. Thus, a PCA-based SVM approach appears to be an effective choice for accurate identification of ADHD patients among other clinical controls using real-time analysis of activity data.

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