Table 1_人工智能支持儿童神经发育障碍诊断的伞状综述

数据集获取（Figshare）

• 源站标题：Table 1_Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.docx
• DOI：10.3389/fpsyt.2026.1697185.s002
• 发布日期：2026-03-18
• 文件数：1 个
• 文件总体积：142.59 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Artificial_intelligence_support_for_diagnosis_of_neurodevelopmental_disorders_during_childhood_an_umbrella_review_docx/31798924

命令行下载

curl -L -o "Table 1_Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.docx" "https://ndownloader.figshare.com/files/62913565"

文件清单

• Table 1_Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.docx（142.59 KB）

文件总体积 142.59 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 growing demand for earlier diagnosis of neurodevelopmental disorders has boosted critical assessment of artificial intelligence (AI) as a complementary tool for clinical decision-making.
MethodsThis umbrella review aimed to synthesize the available evidence from systematic reviews and meta-analyses on the use of AI to diagnose during childhood any neurodevelopmental disorder [autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), intellectual disability, communication disorders, developmental coordination disorder, and specific learning disorders]. A systematic search was conducted on the Web of Science, PsycINFO, and PubMed, covering studies published from January 2015 to August 2025 and available in any language.
ResultsOf the 148 records identified, 64 studies were included based on the predefined inclusion and exclusion criteria. ASD (n = 31) and ADHD (n = 14) were the most frequently examined conditions in which AI was applied for diagnostic purposes. To a lesser extent, it was applied to specific learning disorders (n = 5) and other developmental disorders (intellectual disability and communication disorders, jointly addressed along with other diagnoses, n = 9). The most employed AI models were machine learning (support vector machines and artificial neural networks) and particularly deep learning (such as convolutional neural networks). These models were applied to diverse data modalities, such as neuroimaging (n = 59 studies), electrophysiological (n = 19), clinical/sociodemographic (n = 15), and motion/sensor-based data (n = 11). Overall, these AI models achieved diagnostic accuracy levels ranging from 66% (based on head/facial/eye movements) to 99% (based on neuroimaging, voice, motion, and sensors). However, the methodological quality of most studies was rated as critically low according to the AMSTAR-2 criteria (80%), while only 5% of studies achieved high quality levels (focused on ASD and ADHD).
ConclusionAI shows promising potential for supporting biomarker identification and diagnosis of neurodevelopmental disorders. However, future clinical implementation still requires methodologically rigorous research

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpsyt.2026.1697185.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Artificial_intelligence_support_for_diagnosis_of_neurodevelopmental_disorders_during_childhood_an_umbrella_review_docx/31798924）。