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Artificial intelligence (AI) can assist by aiding pathologists with kidney donor biopsy\u00a0assessment. Herein we present the \u201cGalileo\u201d AI tool, designed specifically to assist the on-call pathologist with interpreting pre-implantation kidney biopsies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A multicenter cohort of whole slide images acquired from core-needle and wedge biopsies of the kidney was collected. A deep learning algorithm was trained to detect the main findings evaluated in the pre-implantation setting (normal glomeruli, globally sclerosed glomeruli, ischemic glomeruli, arterioles and arteries). The model obtained on the Aiforia Create platform was validated on an external dataset by three independent pathologists to evaluate the performance of the algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Galileo demonstrated a precision, sensitivity, F1 score and total area error of 81.96%, 94.39%, 87.74%, 2.81% and 74.05%, 71.03%, 72.5%, 2% in the training and validation sets, respectively. Galileo was significantly faster than pathologists, requiring 2\u00a0min overall in the validation phase (vs 25, 22 and 31\u00a0min by 3 separate human readers,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Galileo-assisted detection of renal structures and quantitative information was directly integrated in the final report.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The Galileo AI-assisted tool shows promise in\u00a0speeding up pre-implantation kidney biopsy interpretation, as well as in\u00a0reducing inter-observer variability. This tool\u00a0may represent a starting point for further improvements based on hard\u00a0endpoints such as graft survival.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s40620-024-02094-4","type":"journal-article","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T03:02:43Z","timestamp":1727838163000},"page":"1163-1169","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Galileo\u2014an Artificial Intelligence tool for evaluating pre-implantation kidney biopsies"],"prefix":"10.1093","volume":"38","author":[{"given":"Albino","family":"Eccher","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9284-2998","authenticated-orcid":false,"given":"Vincenzo","family":"L\u2019Imperio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liron","family":"Pantanowitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giorgio","family":"Cazzaniga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Del Carro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Marletta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Gambaro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Barreca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan Ulrich","family":"Becker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Gobbo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincenzo","family":"Della Mea","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"Alberici","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Pagni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelo Paolo","family":"Dei Tos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,2]]},"reference":[{"key":"2094_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1615\/CritRevOncog.2023049007","volume":"28","author":"S Marletta","year":"2023","unstructured":"Marletta S, Di Bella C, Catalano G et al (2023) Pre-implantation kidney biopsies in extended criteria donors: from on call to expert pathologist, from conventional microscope to digital pathology. 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