Thirunavukarasu Arun James, Jain Nikhil, Sanghera Rohan, Lattuada Federico, Mahmood Shathar, Economou Anna, Yu Helmut C Y, Bourne Rupert
University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Oxford University Clinical Academic Graduate School, University of Oxford, Oxford, UK.
NPJ Digit Med. 2024 May 18;7(1):131. doi: 10.1038/s41746-024-01122-8.
Subjectivity and ambiguity of visual field classification limits the accuracy and reliability of glaucoma diagnosis, prognostication, and management decisions. Standardised rules for classifying glaucomatous visual field defects exist, but these are labour-intensive and therefore impractical for day-to-day clinical work. Here a web-application, Glaucoma Field Defect Classifier (GFDC), for automatic application of Hodapp-Parrish-Anderson, is presented and validated in a cross-sectional study. GFDC exhibits perfect accuracy in classifying mild, moderate, and severe glaucomatous field defects. GFDC may thereby improve the accuracy and fairness of clinical decision-making in glaucoma. The application and its source code are freely hosted online for clinicians and researchers to use with glaucoma patients.
视野分类的主观性和模糊性限制了青光眼诊断、预后评估及治疗决策的准确性和可靠性。虽然存在用于对青光眼性视野缺损进行分类的标准化规则，但这些规则需要耗费大量人力，因此在日常临床工作中并不实用。本文介绍了一种网络应用程序——青光眼视野缺损分类器（GFDC），它可自动应用霍达普 - 帕里什 - 安德森分类法，并在一项横断面研究中得到了验证。GFDC在对轻度、中度和重度青光眼性视野缺损进行分类时表现出完美的准确性。GFDC由此可能会提高青光眼临床决策的准确性和公正性。该应用程序及其源代码可在网上免费获取，供临床医生和研究人员用于青光眼患者。
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