Low-cost, autonomous microscopy using deep learning and robotics: A crystal morphology case study

Abstract

Data availability is a major obstacle to the successful application of artificial intelligence. In chemistry, pharmaceutical and materials science, generating labelled data on scale relies on expensive screening approaches that require major human and/or economic investments. Although industry uses sophisticated autonomous systems for such tasks, tight intellectual property concerns and strict confidentiality agreements limit data accessibility, thus restricting innovation and hindering the concept of ”open science”. Without automation, data collection is laborious and sample characterization often suffers from subjectivity introduced by the operator. This study tackles the challenge of pharmaceutical particle characterization by modifying a 3D printer to enable rapid, autonomous sample characterization using light microscopy. The system uses low-cost hardware and open-source software in a platform that is accessible and reproducible for the scientific community. As well as increasing throughput, this system uses deep learning to assign sample labels in real-time overcoming bias from subjective labelling, which hinders model performance. Different neural architectures were applied, including convolution and attention blocks, to maximize performance and demonstrate the transferability of the system. This autonomous system effectively characterized pharmaceutical crystal morphology, an area where subjective labelling and limited data have hindered applying machine learning models. By releasing this platform, researchers without access to sophisticated automation platforms can carry out larger in-house screening efforts to generate datasets for developing data-driven, intelligent models.