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. 2024 Dec 24;16(1):71.
doi: 10.1186/s11689-024-09588-z.

Objective approach to diagnosing attention deficit hyperactivity disorder by using pixel subtraction and machine learning classification of outpatient consultation videos

Yi-Hung Chiu  1 , Ying-Han Lee  2 , San-Yuan Wang  1 , Chen-Sen Ouyang  3 , Rong-Ching Wu  4 , Rei-Cheng Yang  5 , Lung-Chang Lin  6   7
Affiliations

Objective approach to diagnosing attention deficit hyperactivity disorder by using pixel subtraction and machine learning classification of outpatient consultation videos

Yi-Hung Chiu et al. J Neurodev Disord. .

Abstract

Background: Attention deficit hyperactivity disorder (ADHD) is a common childhood neurodevelopmental disorder, affecting between 5% and 7% of school-age children. ADHD is typically characterized by persistent patterns of inattention or hyperactivity-impulsivity, and it is diagnosed on the basis of the criteria outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, through subjective observations and information provided by parents and teachers. Diagnosing ADHD in children is challenging, despite several assessment tools, such as the Swanson, Nolan, and Pelham questionnaire, being widely available. Such scales provide only a subjective understanding of the disorder. In this study, we employed video pixel subtraction and machine learning classification to objectively categorize 85 participants (43 with a diagnosis of ADHD and 42 without) into an ADHD group or a non-ADHD group by quantifying their movements.

Methods: We employed pixel subtraction movement quantization by analyzing movement features in videos of patients in outpatient consultation rooms. Pixel subtraction is a technique in which the number of pixels in one frame is subtracted from that in another frame to detect changes between the two frames. A difference between the pixel values indicates the presence of movement. In the current study, the patients' subtracted image sequences were characterized using three movement feature values: mean, variance, and Shannon entropy value. A classification analysis based on six machine learning models was performed to compare the performance indices and the discriminatory power of various features.

Results: The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger values for all movement features. Notably, the Shannon entropy values were 2.38 ± 0.59 and 1.0 ± 0.38 in the ADHD and non-ADHD groups, respectively (P < 0.0001). The Random Forest machine learning classification model achieved the most favorable results, with an accuracy of 90.24%, sensitivity of 88.85%, specificity of 91.75%, and area under the curve of 93.87%.

Conclusion: Our pixel subtraction and machine learning classification approach is an objective and practical method that can aid to clinical decisions regarding ADHD diagnosis.

Keywords: Attention deficit hyperactivity disorder; Machine learning; Nolan; Pelham questionnaire; Pixel subtraction; Swanson; Video analysis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Written informed consent was obtained from the participants’ family members or legal guardians after the study procedures had been explained. Informed consent was also obtained for publication of their children’s images. This study was approved by the Institutional Review Board of Kaohsiung Medical University Hospital (KMUIRB-SV(I)- 20190060). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Video recorder view in the consultation room
Fig. 2
Fig. 2
Diagram of the pixel subtraction method
Fig. 3
Fig. 3
Comparisons of classification test performance metrics, specifically, of accuracy, sensitivity, specificity, and area under the curve, were conducted by downsampling the images to 6 Hz across classification models in all feature sets
Fig. 4
Fig. 4
Comparisons of classification test performance metrics, specifically, of accuracy, sensitivity, specificity, and area under the curve, were conducted by downsampling the images to 15 Hz across classification models in all feature sets
Fig. 5
Fig. 5
Comparisons of classification test performance metrics, specifically, of accuracy, sensitivity, specificity, and area under the curve, were conducted using the original image sampling rate of 30 Hz across classification models in all feature sets
Fig. 6
Fig. 6
Comparisons of the SEM for performance metrics, specifically accuracy, sensitivity, specificity, and AUC, were conducted by downsampling the images to 6 Hz across classification models in all feature sets
Fig. 7
Fig. 7
Comparisons of the SEM for performance metrics, specifically accuracy, sensitivity, specificity, and AUC, were conducted by downsampling the images to 15 Hz across classification models in all feature sets
Fig. 8
Fig. 8
Comparisons of the SEM for performance metrics, specifically accuracy, sensitivity, specificity, and AUC, were conducted using the original image sampling rate of 30 Hz across classification models in all feature sets
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References

    1. Huang CL, Wang JJ, Ho CH. Trends in incidence rates of diagnosed attention-deficit/hyperactivity disorder (ADHD) over 12 years in Taiwan: a nationwide population-based study. Psychiatry Res. 2020;284:112792. - PubMed
    1. Tseng MH, et al. Relationship between motor proficiency, attention, impulse, and activity in children with ADHD. Dev Med Child Neurol. 2004;46(6):381–8. - PubMed
    1. Chen MH, Tsai SJ. Treatment-resistant panic disorder: clinical significance, concept and management. Prog Neuropsychopharmacol Biol Psychiatry. 2016;70:219–26. - PubMed
    1. Boland H, et al. A literature review and meta-analysis on the effects of ADHD medications on functional outcomes. J Psychiatr Res. 2020;123:21–30. - PubMed
    1. Wood AC, et al. Is overactivity a core feature in ADHD? Familial and receiver operating characteristic curve analysis of mechanically assessed activity level. J Am Acad Child Adolesc Psychiatry. 2009;48(10):1023–30. - PubMed