Predicting Clinician Fixations on Glaucoma OCT Reports via CNN-Based Saliency Prediction Methods

IF 2.7 Q3 ENGINEERING, BIOMEDICAL

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-02-20 DOI:10.1109/OJEMB.2024.3367492

Mingyang Zang;Pooja Mukund;Britney Forsyth;Andrew F. Laine;Kaveri A. Thakoor

{"title":"Predicting Clinician Fixations on Glaucoma OCT Reports via CNN-Based Saliency Prediction Methods","authors":"Mingyang Zang;Pooja Mukund;Britney Forsyth;Andrew F. Laine;Kaveri A. Thakoor","doi":"10.1109/OJEMB.2024.3367492","DOIUrl":null,"url":null,"abstract":"<italic>Goal:</i>\n To predict physician fixations specifically on ophthalmology optical coherence tomography (OCT) reports from eye tracking data using CNN based saliency prediction methods in order to aid in the education of ophthalmologists and ophthalmologists-in-training. \n<italic>Methods:</i>\n Fifteen ophthalmologists were recruited to each examine 20 randomly selected OCT reports and evaluate the likelihood of glaucoma for each report on a scale of 0-100. Eye movements were collected using a Pupil Labs Core eye-tracker. Fixation heat maps were generated using fixation data. \n<italic>Results:</i>\n A model trained with traditional saliency mapping resulted in a correlation coefficient (CC) value of 0.208, a Normalized Scanpath Saliency (NSS) value of 0.8172, a Kullback–Leibler (KLD) value of 2.573, and a Structural Similarity Index (SSIM) of 0.169. \n<italic>Conclusions</i>\n: The TranSalNet model was able to predict fixations within certain regions of the OCT report with reasonable accuracy, but more data is needed to improve model accuracy. Future steps include increasing data collection, improving quality of data, and modifying the model architecture.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"191-197"},"PeriodicalIF":2.7000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10440538","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Engineering in Medicine and Biology","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10440538/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Goal: To predict physician fixations specifically on ophthalmology optical coherence tomography (OCT) reports from eye tracking data using CNN based saliency prediction methods in order to aid in the education of ophthalmologists and ophthalmologists-in-training. Methods: Fifteen ophthalmologists were recruited to each examine 20 randomly selected OCT reports and evaluate the likelihood of glaucoma for each report on a scale of 0-100. Eye movements were collected using a Pupil Labs Core eye-tracker. Fixation heat maps were generated using fixation data. Results: A model trained with traditional saliency mapping resulted in a correlation coefficient (CC) value of 0.208, a Normalized Scanpath Saliency (NSS) value of 0.8172, a Kullback–Leibler (KLD) value of 2.573, and a Structural Similarity Index (SSIM) of 0.169. Conclusions : The TranSalNet model was able to predict fixations within certain regions of the OCT report with reasonable accuracy, but more data is needed to improve model accuracy. Future steps include increasing data collection, improving quality of data, and modifying the model architecture.

查看原文本刊更多论文

通过基于 CNN 的显著性预测方法预测临床医生对青光眼 OCT 报告的固定点

目标：利用基于 CNN 的显著性预测方法，从眼动跟踪数据中预测眼科光学相干断层扫描 (OCT) 报告中医生的特定定点，以帮助眼科医生和眼科实习医生的教育。方法：招募 15 名眼科医生，每人检查 20 份随机选取的 OCT 报告，并对每份报告进行青光眼可能性评估（0-100 分）。使用 Pupil Labs Core 眼球跟踪器收集眼球运动数据。使用固定数据生成固定热图。结果用传统的显著性映射训练的模型得出的相关系数 (CC) 值为 0.208，归一化扫描路径显著性 (NSS) 值为 0.8172，库尔巴克-莱伯勒 (KLD) 值为 2.573，结构相似性指数 (SSIM) 为 0.169。结论TranSalNet 模型能够以合理的准确度预测 OCT 报告某些区域内的定点，但还需要更多数据来提高模型的准确度。未来的步骤包括增加数据收集、提高数据质量和修改模型结构。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Open Journal of Engineering in Medicine and Biology ENGINEERING, BIOMEDICAL-

CiteScore

9.50

自引率

3.40%

发文量

审稿时长

10 weeks

期刊介绍： The IEEE Open Journal of Engineering in Medicine and Biology (IEEE OJEMB) is dedicated to serving the community of innovators in medicine, technology, and the sciences, with the core goal of advancing the highest-quality interdisciplinary research between these disciplines. The journal firmly believes that the future of medicine depends on close collaboration between biology and technology, and that fostering interaction between these fields is an important way to advance key discoveries that can improve clinical care.IEEE OJEMB is a gold open access journal in which the authors retain the copyright to their papers and readers have free access to the full text and PDFs on the IEEE Xplore® Digital Library. However, authors are required to pay an article processing fee at the time their paper is accepted for publication, using to cover the cost of publication.