Clinical validation of eye vergence as an objective marker for diagnosis of ADHD in children
Objective: ADHD youth show poor oculomotor control. Recent research shows that attention-related eye vergence is weak in ADHD children. Method: To validate vergence as a marker to classify ADHD, we assessed the modulation in the angle of vergence of children (n = 43) previously diagnosed with ADHD w...
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| creator | Varela Casal, Paloma Esposito, Flavia Lorena Morata Martínez, Imanol Capdevila, Alba Solé Puig, Maria Osa, Nuria de la Ezpeleta, Lourdes Perera Lluna, Alexandre Ramos-Quiroga, Josep Antoni Supèr, Hans Cañete-Crespillo, Josep |
| description | Objective: ADHD youth show poor oculomotor control. Recent research shows that attention-related eye vergence is weak in ADHD children. Method: To validate vergence as a marker to classify ADHD, we assessed the modulation in the angle of vergence of children (n = 43) previously diagnosed with ADHD while performing an attention task and compared the results with age-matched clinical controls (n = 19) and healthy peers (n = 30). Results: We observed strong vergence responses in healthy participants and weak vergence in the clinical controls. ADHD children showed no significant vergence responses. Machine-learning models classified ADHD patients (n = 21) from healthy controls (n = 21) with an accuracy of 96.3% (false positive [FP]: 5.12%; false negative [FN]: 0%; area under the curve [AUC]: 0.99) and ADHD children (n = 11) from clinical controls (n = 14) with an accuracy of 85.7% (FP: 4.5%; FN: 19.2%, AUC: 0.90). Conclusion: In combination with an attention task, vergence responses can be used as an objective marker to detect ADHD in children |
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Recent research shows that attention-related eye vergence is weak in ADHD children. Method: To validate vergence as a marker to classify ADHD, we assessed the modulation in the angle of vergence of children (n = 43) previously diagnosed with ADHD while performing an attention task and compared the results with age-matched clinical controls (n = 19) and healthy peers (n = 30). Results: We observed strong vergence responses in healthy participants and weak vergence in the clinical controls. ADHD children showed no significant vergence responses. Machine-learning models classified ADHD patients (n = 21) from healthy controls (n = 21) with an accuracy of 96.3% (false positive [FP]: 5.12%; false negative [FN]: 0%; area under the curve [AUC]: 0.99) and ADHD children (n = 11) from clinical controls (n = 14) with an accuracy of 85.7% (FP: 4.5%; FN: 19.2%, AUC: 0.90). 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Recent research shows that attention-related eye vergence is weak in ADHD children. Method: To validate vergence as a marker to classify ADHD, we assessed the modulation in the angle of vergence of children (n = 43) previously diagnosed with ADHD while performing an attention task and compared the results with age-matched clinical controls (n = 19) and healthy peers (n = 30). Results: We observed strong vergence responses in healthy participants and weak vergence in the clinical controls. ADHD children showed no significant vergence responses. Machine-learning models classified ADHD patients (n = 21) from healthy controls (n = 21) with an accuracy of 96.3% (false positive [FP]: 5.12%; false negative [FN]: 0%; area under the curve [AUC]: 0.99) and ADHD children (n = 11) from clinical controls (n = 14) with an accuracy of 85.7% (FP: 4.5%; FN: 19.2%, AUC: 0.90). 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Recent research shows that attention-related eye vergence is weak in ADHD children. Method: To validate vergence as a marker to classify ADHD, we assessed the modulation in the angle of vergence of children (n = 43) previously diagnosed with ADHD while performing an attention task and compared the results with age-matched clinical controls (n = 19) and healthy peers (n = 30). Results: We observed strong vergence responses in healthy participants and weak vergence in the clinical controls. ADHD children showed no significant vergence responses. Machine-learning models classified ADHD patients (n = 21) from healthy controls (n = 21) with an accuracy of 96.3% (false positive [FP]: 5.12%; false negative [FN]: 0%; area under the curve [AUC]: 0.99) and ADHD children (n = 11) from clinical controls (n = 14) with an accuracy of 85.7% (FP: 4.5%; FN: 19.2%, AUC: 0.90). Conclusion: In combination with an attention task, vergence responses can be used as an objective marker to detect ADHD in children</abstract><oa>free_for_read</oa></addata></record> |
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| title | Clinical validation of eye vergence as an objective marker for diagnosis of ADHD in children |
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