Automated computer vision and dose-response modeling improve throughput and accuracy of an ex vivo functional precision medicine platform.

Functional Precision Medicine platforms face significant obstacles to clinical translation, including stringent requirements for analytical consistency. In this study, we present an automated data analysis workflow for an organotypic brain slice culture-based functional assay that addresses these concerns by combining computer vision and dose-response modeling approaches. Automation reduces analysis time by approximately 99% compared to the original manual workflow (from ~ 20 h to ~ 15 min to analyze an 11-drug assay), increasing throughput capabilities and freeing researchers from tedious analyses. Comparison of automated measurements with previously published manual results revealed that automation increased consistency both within experiments and across replicate experiments by removing human subjectivities. Our workflow demonstrates how to implement computer vision using limited computational resources and minimal programming expertise by leveraging cloud-based, user-friendly ML tools (e.g., Biodock) integrated into local scripts. It also addresses challenges in automated modeling of datasets that include diverse dose-response behaviors, such as hormesis and plateau effects. Finally, we outline a validation strategy for automated analysis in the absence of gold-standard measurements, an issue common in functional assays where manual measurements involve user subjectivity.