TY - JOUR
T1 - Objective Video-Based Assessment of ADHD-Like Canine Behavior Using Machine Learning
AU - Fux, Asaf
AU - Zamansky, Anna
AU - Bleuer-Elsner, Stephane
AU - van der Linden, Dirk
AU - Sinitca, Aleksandr
AU - Romanov, Sergey
AU - Kaplun, Dmitry
N1 - Funding information: The research was supported by the grant from the Ministry of Science and Technology of Israel and RFBR according to the research project no. 19-57-06007.
PY - 2021/9/26
Y1 - 2021/9/26
N2 - 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.
AB - 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.
KW - ADHD-like behavior
KW - Behavioral assessment
KW - Machine learning
KW - Veterinary science
UR - http://www.scopus.com/inward/record.url?scp=85115628217&partnerID=8YFLogxK
U2 - 10.3390/ani11102806
DO - 10.3390/ani11102806
M3 - Article
SN - 2076-2615
VL - 11
JO - Animals
JF - Animals
IS - 10
M1 - 2806
ER -