{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:11:15Z","timestamp":1768781475215,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Science and Innovation, co-financed with FEDER funds","award":["PID2021-127691OB-I00"],"award-info":[{"award-number":["PID2021-127691OB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 and tumoral liver, kidney, ganglion, testicle and brain were collected and imaged with phase-imaging microscopy. Advanced phase parameters were calculated from the images. The statistical significance of each parameter for each tissue type was evaluated at different magnifications of 10\u00d7, 20\u00d7 and 40\u00d7. Several classification algorithms based on artificial intelligence were applied and evaluated. Artificial Neural Network and Decision Tree approaches 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 diagnosis of fresh histological samples with advanced parameters of phase-imaging microscopy. This approach can complement the present clinical procedures.<\/jats:p>","DOI":"10.3390\/s22239295","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T08:46:41Z","timestamp":1669798001000},"page":"9295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Digital Histopathological Discrimination of Label-Free Tumoral Tissues by Artificial Intelligence Phase-Imaging Microscopy"],"prefix":"10.3390","volume":"22","author":[{"given":"Jos\u00e9 Luis","family":"Ganoza-Quintana","sequence":"first","affiliation":[{"name":"Biomedical Engineering Group, TEISA Department, Universidad de Cantabria, Av. de los Castros 46, 39005 Santander, Spain"}]},{"given":"Jos\u00e9 Luis","family":"Arce-Diego","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, TEISA Department, Universidad de Cantabria, Av. de los Castros 46, 39005 Santander, Spain"}]},{"given":"F\u00e9lix","family":"Fanjul-V\u00e9lez","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, TEISA Department, Universidad de Cantabria, Av. de los Castros 46, 39005 Santander, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"ref_1","unstructured":"Tortora, G.J., and Derrickson, B.H. 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