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Attention Deficit Hyperactivity Disorder (ADHD) is considered as a common psychiatric disorder in childhood, which is also continued to adulthood. ADHD is known as under-diagnosed as there is no exact mechanism to diagnose this disorder in an accurate manner. Highest ratio of child patients with ADHD is continued to have symptoms of adulthood as a result of a lack of diagnosis. Therefore, early detection of ADHD will be a help to minimize the severe impact on patients and to develop good mental health. There is no single test in current practice to diagnose ADHD. Hence many studies have been conducted to diagnose ADHD using other clinical data such as EEG and fMRI. Most of them have shown a significant improvement in classification between ADHD and other control subjects. But the problem lies where there is no such a mathematical model or a scoring model that can be used for the diagnosing procedure.
The research addresses the design and development of a composite or similarity score where the doctors can easily diagnose ADHD conducting the currently available tests such as fMRI and eye movement test. Therefore, there is a major requirement to generate an objective biological tool which is capable of classifying ADHD and non-ADHD. The earlier the disorder is diagnosed, the earlier treatment can be begun to prevent severe symptoms.
Data to test:
fMRI (ADHD) adhd.nii
fMRI (non-ADHD) nadhd.nii
Eye movement data (ADHD) test_data_adhd
Eye movement data (non-ADHD) test_data_nadhd
ICSCA Conference presentation: ADHD Identification using Convolutional Neural Network with Seed-based Approach for fMRI Data
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