“The OpenFlexure Microscope (OFM) was developed as a platform for rapid, high-throughput disease diagnosis. A low cost, 3D printed microscope which can be built for just $18, the OFM can be customised for teaching, research, or medical applications. We also present a fully automated, high resolution version for laboratory grade research, built from parts costing less than $250. As an open-source project, we freely share our designs, code, and literature in the hope that people use and, in turn, contribute improvements. This enables local manufacturing in the areas which can benefit from the OFM and ensures the end users can guide the product to match their needs.
In our recent open-access paper, we show that our imaging capabilities can rival commercial microscopes, supporting high-resolution trans- and epi-illumination, as well as polarisation contrast and even fluorescence imaging modes.
Whole-sample images can be constructed by using the precise motorised stage to translate and auto-focus the sample. This stage, based on a flexure-hinge mechanism, is moved by a Raspberry Pi controlling stepper motors, which cause the plastic hinges to flex – these flexure hinges give the OFM its name. By automatically imaging whole slides, technicians can make a diagnosis using all available data, without the need to spend time scanning a sample manually, significantly improving diagnosis throughput.
As part of our goal of “Robotic Microscopy for All”, we are committed to ensuring that the OFM is usable by anyone, regardless of resources or training. The cost of parts for a research grade OFM is two orders of magnitude lower than commercial alternatives. Software based on established, open standards, active support forums, and comprehensive documentation mean that the OFM is accessible to all users. Images are saved digitally, enabling transport, quality control, and the training of new technicians. We are working towards automated, point of care malaria diagnosis, allowing the OFM to support medical staff in areas which conventional microscopy cannot. As we welcome changes and improvements to our designs, we hope this can be extended to enable more research and diagnoses in low resource areas.”
Joe Knapper, PhD Student, University of Bath, @OpenFlexure