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While the task has ethological validity, the main endpoints of \u201cfoot slip counts\u201d and \u201ctime to cross\u201d are prone to human\u2010rater variability and offer limited sensitivity and specificity. We asked if machine learning\u2013based methods could reveal previously hidden, but biologically relevant, insights from the task. Marker\u2010less pose estimation, using DeepLabCut, was deployed to label 13 anatomical key points on mice traversing the beam. Next, we automated classical endpoint detection, including foot slips, with high recall (&gt;\u200990%) and precision (&gt;\u200980%). Using data derived from key point tracking, a total of 395 features were engineered and a random forest classifier deployed that, together with skeletal visualizations, could test for group differences and identify determinant features. 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