| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
The Journal of Pathology: Clinical Research, Vol 10, Iss 6, Pp n/a-n/a (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2056-4538 |
| DOI: |
10.1002/2056-4538.70006 |
| Popis: |
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 sessions. We evaluated the effect of the deep‐learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed‐models analysis. With AI assistance, we found a significant increase in accuracy (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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