Cobbs Lucy V, Al-Hindi Hytham, Fathy Cherie, Mahmoudzadeh Raziyeh, Uhler Tara, Xu David
Retina Service, Wills Eye Hospital, Mid Atlantic Retina, Thomas Jefferson University, Philadelphia, Pennsylvania.
Department of Ophthalmology, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, Pennsylvania.
J Acad Ophthalmol (2017). 2023 Apr 12;15(1):e93-e98. doi: 10.1055/s-0043-1768025. eCollection 2023 Jan.
Ophthalmology residency training heavily relies on visual and pattern recognition-based learning. In parallel with traditional reference texts, online internet search via Google Image Search (GIS) is commonly used and offers an accessible fund of reference images for ophthalmology trainees seeking rapid exposure to images of retinal pathology. However, the accuracy and quality of this tool within this context is unknown. We aim to evaluate the accuracy and quality of GIS images of selected retinal pathologies. A cross-sectional study was performed of GIS of 15 common and 15 rare retinal diseases drawn from the American Academy of Ophthalmology residency textbook series. A total of 300 evaluable image results were assessed for accuracy of images and image source accountability in consultation with a vitreoretinal surgeon. A total of 377 images were reviewed with 77 excluded prior to final analysis. A total of 288 (96%) search results accurately portrayed the retinal disease being searched, whereas 12 (4%) were of an erroneous diagnosis. More images of common retinal diseases were from patient education Web sites than were images of rare diseases ( < 0.01). Significantly more images of rare retinal diseases were found in peer-reviewed sources ( = 0.01). GIS search results yielded a modest level of accuracy for the purposes of ophthalmic education. Despite the ease and rapidity of accessing multimodal retinal imaging examples, this tool may best be suited as a supplementary resource for learning among residents due to limited accuracy, lack of sufficient supporting information, and the source Web site's focus on patient education.
眼科住院医师培训严重依赖基于视觉和模式识别的学习。与传统参考文本并行,通过谷歌图像搜索(GIS)进行在线互联网搜索是常用的方法,为寻求快速接触视网膜病理图像的眼科实习生提供了可获取的参考图像库。然而,在此背景下该工具的准确性和质量尚不清楚。我们旨在评估选定视网膜病变的GIS图像的准确性和质量。
对从美国眼科学会住院医师教科书系列中选取的15种常见和15种罕见视网膜疾病的GIS进行了横断面研究。与玻璃体视网膜外科医生协商,共评估了300个可评估的图像结果的图像准确性和图像来源可追溯性。
在最终分析之前,共审查了377幅图像,排除了77幅。共有288个(96%)搜索结果准确描绘了所搜索的视网膜疾病,而12个(4%)诊断错误。常见视网膜疾病的图像来自患者教育网站的比罕见疾病的图像更多(P<0.01)。在同行评审来源中发现的罕见视网膜疾病的图像明显更多(P = 0.01)。
GIS搜索结果在眼科教育方面的准确性处于中等水平。尽管获取多模态视网膜成像示例既方便又快捷,但由于准确性有限、缺乏足够的支持信息以及来源网站侧重于患者教育,该工具可能最适合作为住院医师学习的补充资源。