Infrared chemical imaging for histologic interpretation of renal tissue

Ghazal Azarfar; Kianoush Falahkheirkhah; Meera Gopu; Jennifer Pfister; Sergey V. Brodsky; Tibor Nadasdy M.D.; Georgina Cheng; Anjali A. Satoskar M.D.; Rohit Bhargava

doi:10.1117/12.2577337

5 March 2021 Infrared chemical imaging for histologic interpretation of renal tissue

Ghazal Azarfar, Kianoush Falahkheirkhah, Meera Gopu, Jennifer Pfister, Sergey V. Brodsky, Tibor Nadasdy M.D., Georgina Cheng, Anjali A. Satoskar M.D., Rohit Bhargava

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Proceedings Volume 11656, Advanced Chemical Microscopy for Life Science and Translational Medicine 2021; 1165612 (2021) https://doi.org/10.1117/12.2577337
Event: SPIE BiOS, 2021, Online Only

Abstract

Applications of machine learning in pathology is an active research area in modern medicine. Here, we presented a classifier for label-free renal histopathology. Three frequently encountered categories of monoclonal gammopathy-associated kidney disease were studied, which included light chain amyloidosis, monoclonal light chain disease deposition (MIDD) and myeloma cast nephropathy. Biopsies with diabetic nephropathy and normal baseline transplant biopsies were used as control. The samples are imaged using a FTIR hyperspectral microscope. More than three million infrared spectra are adopted for the training and evaluation of the computational model. The model recognizes the pixels associated with the glomerulus, and diagnoses the disease based on infrared absorption features.

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Ghazal Azarfar, Kianoush Falahkheirkhah, Meera Gopu, Jennifer Pfister, Sergey V. Brodsky, Tibor Nadasdy M.D., Georgina Cheng, Anjali A. Satoskar M.D., and Rohit Bhargava "Infrared chemical imaging for histologic interpretation of renal tissue", Proc. SPIE 11656, Advanced Chemical Microscopy for Life Science and Translational Medicine 2021, 1165612 (5 March 2021); https://doi.org/10.1117/12.2577337

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