Assessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes
Bogaerts J, Steenbeek M, Bokhorst J, van Bommel M, Abete L, Addante F, Brinkhuis M, Chrzan A, Cordier F, Devouassoux‐Shisheboran M, Fernández‐Pérez J, Fischer A, Gilks C, Guerriero A, Jaconi M, Kleijn T, Kooreman L, Martin S, Milla J, Narducci N, Ntala C, Parkash V, de Pauw C, Rabban J, Rijstenberg L, Rottscholl R, Staebler A, Van de Vijver K, Zannoni G, van Zanten M, Bart J, Bentz J, Bosse T, Bulten J, Desouki M, Lastra R, Numan T, Schoolmeester J, Schwartz L, Shih I, Soong T, Turashvili G, Vang R, Volchek M, Aliredjo R, Kusters‐Vandevelde H, de Hullu J, Simons M, van der Laak J. Assessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes. The Journal Of Pathology Clinical Research 2024, 10: e70006. PMID: 39439213, PMCID: PMC11496567, DOI: 10.1002/2056-4538.70006.Peer-Reviewed Original ResearchConceptsDeep learning modelsSerous tubal intraepithelial carcinomaArtificial intelligenceAI assistanceDiagnosis of serous tubal intraepithelial carcinomaTubal intraepithelial carcinomaReview timeFallopian tubeIntraepithelial carcinomaAI supportHigh-grade serous ovarian carcinomaSerous ovarian carcinomaStandalone performanceAverage sensitivityGroup of pathologistsAccuracyOvarian carcinomaHistopathological diagnosisPathologist performanceMixed-model analysisDiagnostic certaintyCarcinomaDiagnostic setting
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