Data_Sheet_1_基于眼底照片的深度学习系统鉴别急性期视神经炎与非动脉炎性前部缺血性视神经病变
基于彩色眼底照片的深度学习数据表/研究，用于急性期鉴别视神经炎与非动脉炎性前部缺血性视神经病变。
基本信息
资源简介
Data_Sheet_1_基于眼底照片的深度学习系统鉴别急性期视神经炎与非动脉炎性前部缺血性视神经病变
【一句话概览】
本数据集是 figshare 公开的影像数据集，收录 1 个文件、合计 140.52 KB，主题涵盖 artificial intelligence、acute phase、optic neuritis、color fundus photographs，可用于医学图像分割、检测与分类模型的训练与评测，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Data_Sheet_1_Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus photographs.docx》。配套论文《Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs》发表于 Ophthalmology（2018），DOI：10.1016/j.ophtha.2018.01.023。源站首次发布：2023-06-29。
【数据内容】
源站原始描述：PurposeTo develop a deep learning system to differentiate demyelinating optic neuritis (ON) and non-arteritic anterior ischemic optic neuropathy (NAION) with overlapping clinical profiles at the acute phase. MethodsWe developed a deep learning system (ONION) to distinguish ON from NAION at the acute phase. Color fundus photographs (CFPs) from 871 eyes of 547 patients were included, including 396 ON from 232 patients and 475 NAION from 315 patients. Efficientnet-B0 was used to train the model, an…
【数据结构与技术规格】
文件数 1 个，合计 140.52 KB。Data_Sheet_1_Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fund.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，140.52 KB。数据集 DOI：10.3389/fmed.2023.1188542.s001。
【主题与分类】
源站学科分类：Radiology and Organ Imaging、Foetal Development and Medicine、Obstetrics and Gynaecology、Primary Health Care、Medical and Health Sciences not elsewhere classified、Dermatology。主题标签：artificial intelligence、acute phase、optic neuritis、color fundus photographs。
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deep_learning_system_for_distinguishing_optic_neuritis_from_non-arteritic_anterior_ischemic_optic_neuropathy_at_acute_phase_based_on_fundus_photographs_docx/23597850
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Liu, Kaiqun; Liu, Shaopeng; Tan, Xiao; Li, Wangting; Wang, Ling; Li, Xinnan; et al. (2023). Data_Sheet_1_Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus photographs.docx. Frontiers. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Data_Sheet_1_Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus photographs.docx
- DOI：10.3389/fmed.2023.1188542.s001
- 发布日期：2023-06-29
- 文件数：1 个
- 文件总体积：140.52 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deep_learning_system_for_distinguishing_optic_neuritis_from_non-arteritic_anterior_ischemic_optic_neuropathy_at_acute_phase_based_on_fundus_photographs_docx/23597850
命令行下载
curl -L -o "Data_Sheet_1_Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fund.docx" "https://ndownloader.figshare.com/files/41396298"
文件清单
Data_Sheet_1_Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fund.docx（140.52 KB）
文件总体积 140.52 KB（源站 API 实测）。
数据说明
PurposeTo develop a deep learning system to differentiate demyelinating optic neuritis (ON) and non-arteritic anterior ischemic optic neuropathy (NAION) with overlapping clinical profiles at the acute phase.
MethodsWe developed a deep learning system (ONION) to distinguish ON from NAION at the acute phase. Color fundus photographs (CFPs) from 871 eyes of 547 patients were included, including 396 ON from 232 patients and 475 NAION from 315 patients. Efficientnet-B0 was used to train the model, and the performance was measured by calculating the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Also, Cohen’s kappa coefficients were obtained to compare the system’s performance to that of different ophthalmologists.
ResultsIn the validation data set, the ONION system distinguished between acute ON and NAION achieved the following mean performance: time-consuming (23 s), AUC 0.903 (95% CI 0.827–0.947), sensitivity 0.796 (95% CI 0.704–0.864), and specificity 0.865 (95% CI 0.783–0.920). Testing data set: time-consuming (17 s), AUC 0.902 (95% CI 0.832–0.944), sensitivity 0.814 (95% CI 0.732–0.875), and specificity 0.841 (95% CI 0.762–0.897). The performance (κ = 0.805) was comparable to that of a retinal expert (κ = 0.749) and was better than the other four ophthalmologists (κ = 0.309–0.609).
ConclusionThe ONION system performed satisfactorily distinguishing ON from NAION at the acute phase. It might greatly benefit the challenging differentiation between ON and NAION.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fmed.2023.1188542.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Deep_learning_system_for_distinguishing_optic_neuritis_from_non-arteritic_anterior_ischemic_optic_neuropathy_at_acute_phase_based_on_fundus_photographs_docx/23597850）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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