Quantifying the characteristics of microcirculation is essential for understanding how different vascular diseases, such as the rupture of microaneurysms in the eye’s blood vessels, arise. Current imaging techniques, including retinal photography analysis or optical coherence tomography, cannot deliver real-time observation of many in vivo biological processes that occur in microcirculation.
One potential solution is to develop microfluidic devices and laboratory-on-a-chip platforms to understand the mechanics of blood flow and the mechanisms of human vascular diseases. However, current approaches involve either analysing images of fluid flow or enforcing the underlying physics of blood flow without visualization, which can compromise the accuracy of predictions, particularly in vessels with complex geometries.
An AI algorithm trained on 2D images of blood flow…
To improve upon the existing techniques, an international team of researchers – from Brown University, Massachusetts Institute of Technology and Nanyang Technological University – has developed an artificial intelligence velocimetry (AIV) framework that can determine 3D flow fields using 2D imaging data and physics-informed neural networks. The platform has the potential for integration with existing imaging technologies to automatically infer key haemodynamic metrics from in vivo and in vitro biomedical images. The results are summarized in Proceedings of the National Academy of Sciences.
The scientists designed a microaneurysm-on-a-chip (MAOAC) platform that simulates common manifestations of diabetic retinopathy, a leading cause of vision loss arising from blood-vessel damage in the retinas of diabetic patients. More specifically, the researchers developed a microfluidic device capable of moving small amounts of fluid in tiny channels carved into a microchip. This in vitro set-up yielded 2D video images of blood flow, which the researchers used, together with the AI-powered platform, to predict blood flow characteristics.
… successfully predicts the characteristics of blood circulation
The MAOAC system contains eight microchannels with cavities of varying size, with the aim of mimicking different types of microaneurysm. The researchers drew blood from a healthy donor and used a 20 µL sample to fill the system and generate a flow pattern. During the experiment, they recorded video images at 500 frames/s with 1 µm/pixel resolution and used these as training data for the AIV platform to produce velocity fields. Additionally, they also recorded fluorescence images to perform cell-tracking measurements for AIV validation.
The model was able to accurately quantify 3D fields of velocity, pressure and stress in microchannels designed to mimic small, intermediate and large saccular-shaped microaneurysms. The results indicate that the accuracy and efficiency of this model outperform existing computational methods. This is an important step towards clinical adoption, where such a system could be critical in diagnosing and monitoring microaneurysms in patients. In future, the framework can be extended to simulate other types of vascular disorders, as well as facilitating in vivo patient diagnosis and monitoring.
