Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs

Flanders WH, Moïse NS, Otani NF. Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs. J Vet Intern Med. 2024; doi: 10.1111/jvim.17071.

This study evaluated the effectiveness of machine learning and Poincaré plots in diagnosing sinus node dysfunction in dogs. The research involved 73 dogs divided into three groups: balanced autonomic modulation, high parasympathetic/low sympathetic modulation (HP/LSM), and sinus node dysfunction. Results showed that the number and duration of sinus pauses, rather than heart rate, effectively differentiated sinus node dysfunction from HP/LSM. The findings demonstrate that machine learning and Poincaré density grids can reliably identify sinus node dysfunction, with computer modeling supporting sinoatrial conduction block as the underlying mechanism.