Autoimmune diseases, particularly inflammatory bowel diseases (IBD), are a growing global health challenge. These conditions involve complex immune system dysfunctions that damage healthy tissues, leading to chronic inflammation. Diagnosing and understanding these diseases often requires invasive biopsies and labor-intensive processes, which delay timely diagnosis and research. Traditional methods use chemical stains to highlight specific tissue structures, but these processes are slow, costly, and unsuitable for real-time, in vivo analysis.
The MICS project – Multiphoton Imaging with Computational Specificity is designed to overcome these limitations by combining high-resolution label-free multiphoton microscopy (MPM) with the power of artificial intelligence (AI). Specifically, the project uses deep neural networks to enhance the specificity of MPM images, enabling them to function like traditional stained images while retaining the advantages of being label-free and non-invasive.
Deep neural networks are advanced computational models inspired by how the human brain processes information. These networks consist of multiple layers of interconnected "neurons" that transform input data, such as images, into meaningful outputs, like classifications or predictions. To train a neural network on image data, it is shown thousands of labeled examples, such as images of healthy tissue and inflamed tissue. The network learns patterns by adjusting its internal connections through a process called training, which uses algorithms to minimize errors between its predictions and the known annotations. Over time, the network becomes highly skilled at recognizing features in unseen images. In many AI applications, the limiting factor is to actually obtain these large data sets of high-quality and well-trusted annotations.
By combining previously trained deep neural networks with label-free multiphoton imaging, MICS will be able to directly image samples from many autoimmune diseases without any labor-intensive processing of biopsies and then to digitally augment the readout. This approach offers a faster, non-invasive alternative to conventional histological analysis and has a great potential to advance research into autoimmune diseases and pave the way for new diagnostic tools.
