Fux Asaf, Zamansky Anna, Bleuer-Elsner Stephane, van der Linden Dirk, Sinitca Aleksandr, Romanov Sergey, Kaplun Dmitrii
Information Systems Department, University of Haifa, Haifa 3498838, Israel.
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE7 7XA, UK.
Animals (Basel). 2021 Sep 26;11(10):2806. doi: 10.3390/ani11102806.
Canine ADHD-like behavior is a behavioral problem that often compromises dogs' well-being, as well as the quality of life of their owners; early diagnosis and clinical intervention are often critical for successful treatment, which usually involves medication and/or behavioral modification. Diagnosis mainly relies on owner reports and some assessment scales, which are subject to subjectivity. This study is the first to propose an objective method for automated assessment of ADHD-like behavior based on video taken in a consultation room. We trained a machine learning classifier to differentiate between dogs clinically treated in the context of ADHD-like behavior and health control group with 81% accuracy; we then used its output to score the degree of exhibited ADHD-like behavior. In a preliminary evaluation in clinical context, in 8 out of 11 patients receiving medical treatment to treat excessive ADHD-like behavior, H-score was reduced. We further discuss the potential applications of the provided artifacts in clinical settings, based on feedback on H-score received from a focus group of four behavior experts.
犬类多动症样行为是一种行为问题，常常会损害犬只的健康以及其主人的生活质量；早期诊断和临床干预对于成功治疗通常至关重要，治疗通常包括药物治疗和/或行为矫正。诊断主要依赖于主人的报告和一些评估量表，这些都存在主观性。本研究首次提出了一种基于在咨询室拍摄的视频对多动症样行为进行自动评估的客观方法。我们训练了一个机器学习分类器，以区分在多动症样行为背景下接受临床治疗的犬只和健康对照组，准确率达到81%；然后我们使用其输出结果对表现出的多动症样行为程度进行评分。在临床环境中的初步评估中，在11名接受治疗以治疗过度多动症样行为的患者中，有8名患者的H评分降低。我们根据来自四位行为专家焦点小组对H评分的反馈，进一步讨论了所提供的工具在临床环境中的潜在应用。
