Integrating Clinical Infrared and Raman Spectroscopy with digital pathology and AI: CLIRPath-AI

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A key feature of the diagnosis of any disease, but particularly various forms of cancer, is the critical information obtained through a biopsy. A biopsy involves the removal of a small sample of tissue, or a few cells, from the patient for examination by a pathologist looking down an optical microscope. In current practice is that the sample is stained with a combination of dyes to help gain some contrast in the image which helps the pathologist see the cells. Generally, based upon this visual inspection of the sample and other relevant medical information, a diagnosis is made. This process, however, is far from ideal since it relies on the expertise of the clinician concerned and is subject to intra in inter observer error. (In other words the process is not exact and depends upon the opinion of the clinicians which may differ). Recently a number of developments have been made in the field of Digital Pathology and Artificial Intelligence (AI). This is where a high resolution photograph of the biopsy slide is taken and examined by a computer algorithm which helps the pathologist make a diagnosis. However analysing the data from just the visible region of the spectrum severely restricts information content of the images obtained. Recently a number of proof of concept studies have shown that molecular spectroscopic techniques such as infrared and Raman are capable of distinguishing diseased from non-diseased cells and tissue based upon the inherent chemistry contained within the cells. (These regions of the spectrum have 40 times the bandwidth of the visible and therefore contain 40 times the amount of information.) The UK is at the forefront in developments associated with both Digital Pathology and AI, the latter augmented by five new technology centres funded by the Industrial Strategy Challenge Fund. In addition, partly due to an EPSRC funded network (CLIRSPEC) the UK is also world leading in the field biomedical infrared and Raman spectroscopy. At present however the Digital pathology/AI and biomedical infrared/Raman these two communities are separate and are not interacting. As a result therefore, the advances made in one area are not being translated to another. In both areas of research there are many hurdles that need to be overcome if this technology is to move from the proof of concept stage through the translational stage and into the clinical setting. It is the belief of the academic community that we are much more likely to overcome these hurdles if we pool our resources, bring in both industrial and clinical partners and work on these generic problems together. This application is for funding to support such a network of partners that will develop dynamic and synergistic interaction between these separate communities for the next four years, for the specific aim of benefiting patients.