Structural or/and functional MRI-based machine learning techniques for attention-deficit/hyperactivity disorder
Lu Tian1, Helin Zheng1, Ke Zhang1
1Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing 400014, China.
Background:
The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD.
Methods:
We conducted a comprehensive search (from database creation date to March 2024) for relevant English articles on sMRI or/and fMRI-based ML techniques for diagnosing ADHD. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were calculated to assess the diagnostic value of sMRI or/and fMRI-based ML techniques. The I2 test was used to assess heterogeneity and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test.
Results:
Forty-three studies were included in the systematic review, 27 of which were included in our meta-analysis. The pooled sensitivity and specificity of sMRI or/and fMRI-based ML techniques for the diagnosis of ADHD were 0.74 (95 % CI 0.65-0.81) and 0.75 (95 % CI 0.67-0.81), respectively. SROC curve showed that AUC was 0.81 (95 % CI 0.77-0.84). Based on these findings, the sMRI or/and fMRI-based ML techniques have relatively good diagnostic value for ADHD.
Limitations:
Our meta-analysis specifically focused on ML techniques based on sMRI or/and fMRI studies. Since EEG-based ML techniques are also used for diagnosing ADHD, further systematic analyses are necessary to explore ML methods based on multimodal medical data.
Conclusion:
sMRI or/and fMRI-based ML technique is a promising objective diagnostic method for ADHD.
Insights
Machine learning (ML) techniques using structural MRI (sMRI) or functional MRI (fMRI) show promise for diagnosing ADHD. These methods offer a valuable, objective approach for ADHD diagnosis.
Area of Science:
- Neuroimaging
- Artificial Intelligence
- Medical Diagnostics
Background:
- Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis relies on subjective criteria.
- Objective diagnostic methods are needed to improve ADHD assessment.
- Machine learning (ML) applied to neuroimaging data offers a potential solution.
Approach:
- Systematic review and meta-analysis of studies using sMRI or fMRI with ML for ADHD diagnosis.
- Searched literature up to March 2024 for relevant English articles.
- Calculated pooled sensitivity, specificity, likelihood ratios, and AUC for diagnostic accuracy.
Key Points:
- Pooled sensitivity was 0.74 and specificity was 0.75.
- Area under the ROC curve (AUC) was 0.81, indicating good diagnostic value.
- ML techniques based on sMRI/fMRI demonstrate significant potential for ADHD diagnosis.
Conclusions:
- sMRI or fMRI-based ML techniques present a promising objective diagnostic method for ADHD.
- Further research into multimodal data, including EEG, is warranted for comprehensive ML-based ADHD diagnosis.
Related Concept Videos
01:30Attention-Deficit/Hyperactivity Disorder
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
01:14Brain Imaging
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
01:24Magnetic Resonance Imaging