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

• Main Authors: Joep MA Bogaerts (Author), Miranda P Steenbeek (Author), John‐Melle Bokhorst (Author), Majke HD vanBommel (Author), Luca Abete (Author), Francesca Addante (Author), Mariel Brinkhuis (Author), Alicja Chrzan (Author), Fleur Cordier (Author), Mojgan Devouassoux‐Shisheboran (Author), Juan Fernández‐Pérez (Author), Anna Fischer (Author), C Blake Gilks (Author), Angela Guerriero (Author), Marta Jaconi (Author), Tony G Kleijn (Author), Loes Kooreman (Author), Spencer Martin (Author), Jakob Milla (Author), Nadine Narducci (Author), Chara Ntala (Author), Vinita Parkash (Author), Christophe dePauw (Author), Joseph T Rabban (Author), Lucia Rijstenberg (Author), Robert Rottscholl (Author), Annette Staebler (Author), Koen Van de Vijver (Author), Gian Franco Zannoni (Author), Monica vanZanten (Author), AI‐STIC Study Group (Author), Joanne A deHullu (Author), Michiel Simons (Author), Jeroen AWM van derLaak (Author)
• Format: Book
• Published: Wiley, 2024-11-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

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...