Eye tracking in digital pathology: A comprehensive literature review

Alana Lopes , Aaron D. Ward , Matthew Cecchini
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引用次数: 0

Abstract

Eye tracking has been used for decades in attempt to understand the cognitive processes of individuals. From memory access to problem-solving to decision-making, such insight has the potential to improve workflows and the education of students to become experts in relevant fields. Until recently, the traditional use of microscopes in pathology made eye tracking exceptionally difficult. However, the digital revolution of pathology from conventional microscopes to digital whole slide images allows for new research to be conducted and information to be learned with regards to pathologist visual search patterns and learning experiences. This has the promise to make pathology education more efficient and engaging, ultimately creating stronger and more proficient generations of pathologists to come. The goal of this review on eye tracking in pathology is to characterize and compare the visual search patterns of pathologists. The PubMed and Web of Science databases were searched using ‘pathology’ AND ‘eye tracking’ synonyms. A total of 22 relevant full-text articles published up to and including 2023 were identified and included in this review. Thematic analysis was conducted to organize each study into one or more of the 10 themes identified to characterize the visual search patterns of pathologists: (1) effect of experience, (2) fixations, (3) zooming, (4) panning, (5) saccades, (6) pupil diameter, (7) interpretation time, (8) strategies, (9) machine learning, and (10) education. Expert pathologists were found to have higher diagnostic accuracy, fewer fixations, and shorter interpretation times than pathologists with less experience. Further, literature on eye tracking in pathology indicates that there are several visual strategies for diagnostic interpretation of digital pathology images, but no evidence of a superior strategy exists. The educational implications of eye tracking in pathology have also been explored but the effect of teaching novices how to search as an expert remains unclear. In this article, the main challenges and prospects of eye tracking in pathology are briefly discussed along with their implications to the field.

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数字病理学中的眼动仪:综合文献综述
几十年来,人们一直在使用眼动仪试图了解个人的认知过程。从获取记忆到解决问题再到决策,这种洞察力有可能改进工作流程和学生教育,使其成为相关领域的专家。直到最近,病理学中显微镜的传统使用还使得眼球跟踪异常困难。然而,病理学从传统显微镜到全玻片数字图像的数字化革命,使病理学家的视觉搜索模式和学习经验方面的新研究和新信息得以开展。这有望提高病理学教育的效率和吸引力,最终培养出更强大、更精通的新一代病理学家。这篇关于病理学眼动追踪的综述旨在描述和比较病理学家的视觉搜索模式。我们使用 "病理学 "和 "眼动仪 "同义词在 PubMed 和 Web of Science 数据库中进行了搜索。共找到 22 篇截至 2023 年(含 2023 年)发表的相关全文文章,并将其纳入本综述。我们对每篇研究进行了主题分析,将其归纳为 10 个主题中的一个或多个,以描述病理学家的视觉搜索模式:(1) 经验的影响;(2) 固定;(3) 缩放;(4) 平移;(5) 囊视;(6) 瞳孔直径;(7) 解释时间;(8) 策略;(9) 机器学习;(10) 教育。研究发现,与经验较少的病理学家相比,专家级病理学家的诊断准确率更高、定点次数更少、判读时间更短。此外,有关病理学眼动追踪的文献表明,有几种视觉策略可用于数字病理图像的诊断解读,但没有证据表明存在一种更优越的策略。也有人探讨了眼动追踪在病理学中的教育意义,但教导新手如何像专家一样进行搜索的效果仍不明确。本文简要讨论了眼动追踪技术在病理学领域的主要挑战和前景,以及对该领域的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Pathology Informatics
Journal of Pathology Informatics Medicine-Pathology and Forensic Medicine
CiteScore
3.70
自引率
0.00%
发文量
2
审稿时长
18 weeks
期刊介绍: The Journal of Pathology Informatics (JPI) is an open access peer-reviewed journal dedicated to the advancement of pathology informatics. This is the official journal of the Association for Pathology Informatics (API). The journal aims to publish broadly about pathology informatics and freely disseminate all articles worldwide. This journal is of interest to pathologists, informaticians, academics, researchers, health IT specialists, information officers, IT staff, vendors, and anyone with an interest in informatics. We encourage submissions from anyone with an interest in the field of pathology informatics. We publish all types of papers related to pathology informatics including original research articles, technical notes, reviews, viewpoints, commentaries, editorials, symposia, meeting abstracts, book reviews, and correspondence to the editors. All submissions are subject to rigorous peer review by the well-regarded editorial board and by expert referees in appropriate specialties.
期刊最新文献
Digital mapping of resected cancer specimens: The visual pathology report A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI ViCE: An automated and quantitative program to assess intestinal tissue morphology Deep feature batch correction using ComBat for machine learning applications in computational pathology LVI-PathNet: Segmentation-classification pipeline for detection of lymphovascular invasion in whole slide images of lung adenocarcinoma
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