圆锥角膜AI诊断与管理系统评价表4

数据集获取（Figshare）

• 源站标题：Table 4_Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.docx
• DOI：10.3389/fopht.2024.1380701.s004
• 发布日期：2024-05-17
• 文件数：1 个
• 文件总体积：31.74 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_4_Utility_of_artificial_intelligence_in_the_diagnosis_and_management_of_keratoconus_a_systematic_review_docx/25845304

命令行下载

curl -L -o "Table 4_Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.docx" "https://ndownloader.figshare.com/files/46384648"

文件清单

• Table 4_Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.docx（31.74 KB）

文件总体积 31.74 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

IntroductionThe application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias.
MethodsWe conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings.
ResultsNinety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus.
DiscussionAlgorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fopht.2024.1380701.s004（source: https://frontiersin.figshare.com/articles/dataset/Table_4_Utility_of_artificial_intelligence_in_the_diagnosis_and_management_of_keratoconus_a_systematic_review_docx/25845304）。