眼科多模态AI诊断与分诊研究方案表1

数据集获取（Figshare）

• 源站标题：Table_1_Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study.DOCX
• DOI：10.3389/frai.2023.1323924.s001
• 发布日期：2023-12-08
• 文件数：1 个
• 文件总体积：19.68 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_evaluation_of_multimodal_AI_for_diagnosis_and_triage_of_ophthalmic_diseases_using_ChatGPT_and_anterior_segment_images_protocol_for_a_two-stage_cross-sectional_study_DOCX/24770097

命令行下载

curl -L -o "Table_1_Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protoco.DOCX" "https://ndownloader.figshare.com/files/43532529"

文件清单

• Table_1_Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protoco.DOCX（19.68 KB）

文件总体积 19.68 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

IntroductionArtificial intelligence (AI) technology has made rapid progress for disease diagnosis and triage. In the field of ophthalmic diseases, image-based diagnosis has achieved high accuracy but still encounters limitations due to the lack of medical history. The emergence of ChatGPT enables human-computer interaction, allowing for the development of a multimodal AI system that integrates interactive text and image information.
ObjectiveTo develop a multimodal AI system using ChatGPT and anterior segment images for diagnosing and triaging ophthalmic diseases. To assess the AI system’‘s performance through a two-stage cross-sectional study, starting with silent evaluation and followed by early clinical evaluation in outpatient clinics.
Methods and analysisOur study will be conducted across three distinct centers in Shanghai, Nanjing, and Suqian. The development of the smartphone-based multimodal AI system will take place in Shanghai with the goal of achieving ≥90% sensitivity and ≥95% specificity for diagnosing and triaging ophthalmic diseases. The first stage of the cross-sectional study will explore the system’‘s performance in Shanghai’'s outpatient clinics. Medical histories will be collected without patient interaction, and anterior segment images will be captured using slit lamp equipment. This stage aims for ≥85% sensitivity and ≥95% specificity with a sample size of 100 patients. The second stage will take place at three locations, with Shanghai serving as the internal validation dataset, and Nanjing and Suqian as the external validation dataset. Medical history will be collected through patient interviews, and anterior segment images will be captured via smartphone devices. An expert panel will establish reference standards and assess AI accuracy for diagnosis and triage throughout all stages. A one-vs.-rest strategy will be used for data analysis, and a post-hoc power calculation will be performed to evaluate the impact of disease types on AI performance.
DiscussionOur study may provide a user-friendly smartphone-based multimodal AI system for diagnosis and triage of ophthalmic diseases. This innovative system may support early detection of ocular abn

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/frai.2023.1323924.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_and_evaluation_of_multimodal_AI_for_diagnosis_and_triage_of_ophthalmic_diseases_using_ChatGPT_and_anterior_segment_images_protocol_for_a_two-stage_cross-sectional_study_DOCX/24770097）。