Montezuma Diana, Oliveira Sara P, Tolkach Yuri, Boor Peter, Haragan Alex, Carvalho Rita, Della Mea Vincenzo, Kiehl Tim-Rasmus, Leh Sabine, Yousif Mustafa, Ameisen David, Zerbe Norman, L'Imperio Vincenzo
European Society of Digital and Integrative Pathology (ESDIP), Lisboa, Portugal; Research & Development Unit, IMP Diagnostics, Porto, Portugal; Cancer Biology and Epigenetics Group, Research Center of Portuguese Oncology Institute of Porto/RISE@Research Center of Portuguese Oncology Institute of Porto (Health Research Network), Portuguese Oncology Institute of Porto/Porto Comprehensive Cancer Centre Raquel Seruca, Porto, Portugal.
European Society of Digital and Integrative Pathology (ESDIP), Lisboa, Portugal; Department of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Lab Invest. 2025 Mar;105(3):102203. doi: 10.1016/j.labinv.2024.102203. Epub 2024 Nov 29.
Integrating digital pathology and artificial intelligence (AI) algorithms can potentially improve diagnostic practice and precision medicine. Developing reliable, generalizable, and comparable AI algorithms depends on access to meticulously annotated data. However, achieving this requires robust collaboration among pathologists, computer scientists, and other researchers to ensure data quality and consistency. The lack of standardization and scalability is a significant challenge when generating annotations and annotated data sets. Recognizing these limitations, the Scientific Committee of the European Society of Digital and Integrative Pathology (ESDIP) performed a comprehensive international survey to understand the current state of annotation practices and identify actionable areas to address critical needs in the annotation process. The analysis and summary of the survey results provide several insights for all stakeholders involved in data preparation and ground truthing, ultimately contributing to the advancement of AI in computational pathology.
整合数字病理学和人工智能（AI）算法有可能改善诊断实践和精准医学。开发可靠、通用且可比的AI算法依赖于获取经过精心注释的数据。然而，要实现这一点，病理学家、计算机科学家和其他研究人员之间需要强大的协作，以确保数据质量和一致性。在生成注释和带注释的数据集时，缺乏标准化和可扩展性是一个重大挑战。认识到这些局限性，欧洲数字与整合病理学学会（ESDIP）科学委员会开展了一项全面的国际调查，以了解注释实践的现状，并确定可采取行动的领域，以满足注释过程中的关键需求。对调查结果的分析和总结为参与数据准备和确定真实情况的所有利益相关者提供了几点见解，最终有助于推动计算病理学中AI的发展。
