机器学习与人工智能在多囊卵巢综合征诊断与分类中的应用：系统综述表2数据集

数据集获取（Figshare）

• 源站标题：Table_2_Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review.docx
• DOI：10.3389/fendo.2023.1106625.s002
• 发布日期：2023-09-18
• 文件数：1 个
• 文件总体积：14.47 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Application_of_machine_learning_and_artificial_intelligence_in_the_diagnosis_and_classification_of_polycystic_ovarian_syndrome_a_systematic_review_docx/24153777

命令行下载

curl -L -o "Table_2_Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic r.docx" "https://ndownloader.figshare.com/files/42377964"

文件清单

• Table_2_Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic r.docx（14.47 KB）

文件总体积 14.47 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

IntroductionPolycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.
MethodsWe applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022.
Results135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies).
ConclusionArtificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility.
Systematic review registrationhttps://www.crd.york.ac.uk/prospero/

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fendo.2023.1106625.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Application_of_machine_learning_and_artificial_intelligence_in_the_diagnosis_and_classification_of_polycystic_ovarian_syndrome_a_systematic_review_docx/24153777）。