A carregar...
Publicação
GrandQC adaptation as an artificial intelligence tool for quantitative artifact detection in hematoxylin and eosin whole-slide images : simulation of quality control biopsies day
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 695.58 KB | Adobe PDF |
Orientador(es)
Resumo(s)
Introduction: Digital pathology continues to transform the daily routine of pathology, in terms of the increasingly automated laboratory and in the diagnostic paradigm through the adoption of artificial intelligence (AI) tools to support diagnosis—computational pathology. The reliability and performance of these tools depend on the whole-slide image (WSI) quality being guaranteed a priori. Pre-analytical quality control step that underpins this guarantee, and artifact detection remains largely qualitative and is frequently overlooked in routine digital pathology. This operational feasibility study evaluated whether an adaptation of GrandQC, an open-source AI tool, enables automated, quantitative artifact assessment of a complete single-day biopsy workload from a high-throughput digital pathology laboratory, analyzed retrospectively.
Material and methods: A random biopsies day of 2025 at Centro de Anatomia Patológica Germano de Sousa (CAPGS) was selected as a sample to test the performance of GrandQC on the WSI generated (n = 544) in order to simulate the daily workflow. A script was created to quantify the pixels corresponding to the type of artifact automatically, creating an Excel file for registering and statistical analysis.
Results: Analysis took a median of 24 s per WSI, detecting a median of 1.46% of tissue area with some type of artifact. Dark Spots and blurring areas were the most representative detected artifacts.
Conclusion: GrandQC is a valuable tool in the quantitative quality control of biopsies tissue, allowing quick evaluation, signaling types of artifacts, and identifying cases that need to be reviewed before being handed over to the pathologist allowing the recognition of opportunities to improve laboratory histology quality and precision medicine.
Descrição
Palavras-chave
Digital pathology Computational pathology Artificial intelligence Quality control
Contexto Educativo
Citação
Gonçalo Borrecho, Pedro Nina, Ricardo Santos, Inês Ferreira, Pedro Salgueiro, Catarina Madeira, Luís Rato, Rui Caetano Oliveira, GrandQC adaptation as an artificial intelligence tool for quantitative artifact detection in hematoxylin and eosin whole-slide images—Simulation of quality control biopsies day, Journal of Pathology Informatics, Volume 22, 2026, 100682, https://doi.org/10.1016/j.jpi.2026.100682
Editora
Elsevier