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As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices (e.g., The Paris System for Reporting Urinary Cytology), algorithms to emulate semiautonomous diagnostic decision\u2010making have lagged behind, in part because of the complex and nuanced nature of urine cytology reporting.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, the authors report on the development and large\u2010scale validation of\u00a0a deep\u2010learning tool, AutoParis\u2010X, which\u00a0can facilitate rapid, semiautonomous examination of urine cytology specimens.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results of this large\u2010scale, retrospective validation study indicate that AutoParis\u2010X can accurately determine urothelial cell atypia and aggregate a wide variety of cell\u2010related and cluster\u2010related information across a slide to yield an atypia burden score, which correlates closely with overall specimen atypia and is predictive of Paris system diagnostic categories. Importantly, this approach accounts for challenges associated with the assessment of overlapping cell cluster borders, which improve the ability to predict specimen atypia and accurately estimate the nuclear\u2010to\u2010cytoplasm ratio for cells in these clusters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The authors developed a publicly available, open\u2010source, interactive web application that features a simple, easy\u2010to\u2010use display for examining urine cytology whole\u2010slide images and determining the level of atypia in specific cells, flagging the most abnormal cells for pathologist review. 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