DeepSeek辅助诊断抗体阳性自身免疫性脑炎数据集（表3）

数据集获取（Figshare）

• 源站标题：Table 3_The potential of DeepSeek for AI-aided diagnosis of antibody-positive autoimmune encephalitis: a single-center, retrospective, observational study.docx
• DOI：10.3389/frai.2025.1638904.s003
• 发布日期：2025-10-06
• 文件数：1 个
• 文件总体积：18.79 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_3_The_potential_of_DeepSeek_for_AI-aided_diagnosis_of_antibody-positive_autoimmune_encephalitis_a_single-center_retrospective_observational_study_docx/30283627

命令行下载

curl -L -o "Table 3_The potential of DeepSeek for AI-aided diagnosis of antibody-positive autoimmune encephalitis: a single-center, retrospective, observational s.docx" "https://ndownloader.figshare.com/files/58508107"

文件清单

• Table 3_The potential of DeepSeek for AI-aided diagnosis of antibody-positive autoimmune encephalitis: a single-center, retrospective, observational s.docx（18.79 KB）

文件总体积 18.79 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

BackgroundAutoimmune encephalitis (AIE) is challenging to diagnose, especially in primary hospitals in China with limited medical resources. DeepSeek, a newly developed AI, shows potential as a cost-effective tool for improving diagnostic efficiency. However, no studies have evaluated the diagnostic accuracy of DeepSeek for AIE.
MethodsThis retrospective study included 100 patients with anti-neuronal antibody-positive AIE treated at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. After removing personally identifiable information, antibody results, and history of immunotherapy from patients’ medical histories, the following information was sequentially input into DeepSeek: sex, age, chief complaint, medical history, EEG findings, head MRI description, and cerebrospinal fluid (CSF) results. The positive rates of AIE diagnoses predicted by DeepSeek were then categorized as most likely diagnosis, differential diagnosis, and total diagnosis.
ResultsUsing DeepSeek, the probabilities of AIE appearing as the most likely diagnosis and total diagnosis accuracy were 49 and 65%. When patient data were input stepwise, both the total diagnosis accuracy and the most likely diagnosis accuracy did not significantly increase. AIE patients with anti-MOG and anti-GABAbR positivity had predicted total diagnostic positivity rates of 88 and 100%, respectively. Patients presenting with headache and epilepsy were more likely to be diagnosed with AIE (96 and 100%).
ConclusionDeepSeek shows limited positive diagnostic accuracy for predicting the diagnosis of AIE. The application of this new AI technology could be used to promote early screening for AIE in primary hospitals in China, improve medical education, and lead to research advances in AIE.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/frai.2025.1638904.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_3_The_potential_of_DeepSeek_for_AI-aided_diagnosis_of_antibody-positive_autoimmune_encephalitis_a_single-center_retrospective_observational_study_docx/30283627）。