A Segment Anything Model-based tool for semi-automated behavioural analysis of <i>Drosophila</i> and other model organisms

Quantitative behavioural analysis is a powerful approach for linking genotype to phenotype, but many existing tools require specialised hardware, extensive preprocessing or coding expertise. We present Segment Anything Model for Behavioural Analysis (SAMBA), an open-access, Google Colab-based pipeline that harnesses the Segment Anything Model 2 (SAM2) for accurate, semi-automated tracking without thresholding or background subtraction. With minimal user input, SAMBA extracts movement parameters, detects behavioural states and supports batch processing. Validating SAMBA in three Drosophila melanogaster models of human neurological disease revealed impaired locomotion, reduced speed and altered decision-making. We further demonstrated adaptability to adult Drosophila and larval zebrafish, underscoring its cross-species utility. By combining foundation-model segmentation with an accessible interface, SAMBA lowers technical barriers to analysing motor pattern defects and is readily extendable to diverse model organisms, life stages and experimental paradigms. This flexibility positions SAMBA as a valuable platform for accelerating disease mechanism studies, genetic screens and preclinical testing.