Assessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes

Assessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes

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‐gr...

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Main Authors: Joep MA Bogaerts, Miranda P Steenbeek, John‐Melle Bokhorst, Majke HD vanBommel, Luca Abete, Francesca Addante, Mariel Brinkhuis, Alicja Chrzan, Fleur Cordier, Mojgan Devouassoux‐Shisheboran, Juan Fernández‐Pérez, Anna Fischer, C Blake Gilks, Angela Guerriero, Marta Jaconi, Tony G Kleijn, Loes Kooreman, Spencer Martin, Jakob Milla, Nadine Narducci, Chara Ntala, Vinita Parkash, Christophe dePauw, Joseph T Rabban, Lucia Rijstenberg, Robert Rottscholl, Annette Staebler, Koen Van de Vijver, Gian Franco Zannoni, Monica vanZanten, AI‐STIC Study Group, Joanne A deHullu, Michiel Simons, Jeroen AWM van derLaak
Format: Article
Language:English
Published: Wiley 2024-11-01
Series:The Journal of Pathology: Clinical Research
Subjects:
serous tubal intraepithelial carcinoma
STIC
high‐grade serous carcinoma
deep learning
artificial intelligence
histopathology
Online Access:https://doi.org/10.1002/2056-4538.70006
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https://doi.org/10.1002/2056-4538.70006

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