{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bogaerts JM"],"funding":["KWF Kankerbestrijding"],"pagination":["e70006"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11496567"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10(6)"],"pubmed_abstract":["In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessio"],"journal":["The journal of pathology. Clinical research"],"pubmed_title":["Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes."],"pmcid":["PMC11496567"],"funding_grant_id":["12950"],"pubmed_authors":["Schwartz LE","Brinkhuis M","Kooreman L","Rabban JT","Bogaerts JM","van Zanten M","Devouassoux-Shisheboran M","Steenbeek MP","Martin S","Milla J","Fernandez-Perez J","Kleijn TG","Bentz JL","Staebler A","Rijstenberg L","Guerriero A","Lastra RR","de Hullu JA","Ntala C","Volchek M","Fischer A","Schoolmeester JK","Jaconi M","Parkash V","Vang R","de Pauw C","Numan TA","Addante F","Zannoni GF","Turashvili G","Soong TR","Cordier F","Abete L","Van de Vijver K","Bokhorst JM","AI‐STIC Study Group","Aliredjo RP","Bosse T","Shih IM","Bart J","Gilks CB","van der Laak JA","Bulten J","Narducci N","Kusters-Vandevelde H","Simons M","Rottscholl R","van Bommel MH","Desouki MM","Chrzan A"],"additional_accession":[]},"is_claimable":false,"name":"Assessing the impact of deep-learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes.","description":"In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessio","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2025-04-04T02:21:28.873Z","creation":"2025-04-04T02:21:28.873Z"},"accession":"S-EPMC11496567","cross_references":{"pubmed":["39439213"],"doi":["10.1002/2056-4538.70006"]}}