Digital Histopathological Discrimination of Label-Free Healthy Tissues by Decision Tree Classifier

Histopathology is the gold standard for disease diagnosis. The use of digital histology on fresh samples can reduce processing time and potential image artifacts, as label-free samples do not need to be fixed nor stained. This fact allows for a faster diagnosis, increasing the speed of the process and the impact on patient prognosis. This work proposes, implements, and validates a novel digital diagnosis procedure of fresh label-free histological samples. The procedure is based on advanced phase-imaging microscopy parameters and artificial intelligence. Fresh human histological samples of healthy brain, stomach, ganglion, liver, colon, lung, kidney, testicle and thyroid were collected and imaged with phase-imaging microscopy. Decision Tree approach provided the best general sensibility and specificity results, with values over 90% for the majority of biological tissues at some magnifications. These results show the potential to provide a label-free automatic significant labelled of fresh histological samples with advanced parameters of phase-imaging microscopy.