基于大语言模型的多模态眼表疾病检测与分级系统数据集

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Large language model-based multimodal system for detecting and grading ocular surface diseases from smartphone images.docx
• DOI：10.3389/fcell.2025.1600202.s001
• 发布日期：2025-05-23
• 文件数：1 个
• 文件总体积：286.30 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Large_language_model-based_multimodal_system_for_detecting_and_grading_ocular_surface_diseases_from_smartphone_images_docx/29134268

命令行下载

curl -L -o "Data Sheet 1_Large language model-based multimodal system for detecting and grading ocular surface diseases from smartphone images.docx" "https://ndownloader.figshare.com/files/54767708"

文件清单

• Data Sheet 1_Large language model-based multimodal system for detecting and grading ocular surface diseases from smartphone images.docx（286.30 KB）

文件总体积 286.30 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

BackgroundThe development of medical artificial intelligence (AI) models is primarily driven by the need to address healthcare resource scarcity, particularly in underserved regions. Proposing an affordable, accessible, interpretable, and automated AI system for non-clinical settings is crucial to expanding access to quality healthcare.
MethodsThis cross-sectional study developed the Multimodal Ocular Surface Assessment and Interpretation Copilot (MOSAIC) using three multimodal large language models: gpt-4-turbo, claude-3-opus, and gemini-1.5-pro-latest, for detecting three ocular surface diseases (OSDs) and grading keratitis and pterygium. A total of 375 smartphone-captured ocular surface images collected from 290 eyes were utilized to validate MOSAIC. The performance of MOSAIC was evaluated in both zero-shot and few-shot settings, with tasks including image quality control, OSD detection, analysis of the severity of keratitis, and pterygium grading. The interpretability of the system was also evaluated.
ResultsMOSAIC achieved 95.00% accuracy in image quality control, 86.96% in OSD detection, 88.33% in distinguishing mild from severe keratitis, and 66.67% in determining pterygium grades with five-shot settings. The performance significantly improved with the increasing learning shots (p MOSAIC exhibited exceptional few-shot learning capabilities, achieving high accuracy in OSD management with minimal training examples. This system has significant potential for smartphone integration to enhance the accessibility and effectiveness of OSD detection and grading in resource-limited settings.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcell.2025.1600202.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Large_language_model-based_multimodal_system_for_detecting_and_grading_ocular_surface_diseases_from_smartphone_images_docx/29134268）。