预测自闭症谱系障碍的深度学习方法:系统审查和元分析
Yang Ding1, Heng Zhang1, Ting Qiu2
1Departments of Child Health Care, Wuxi Maternity and Child Health Care Hospital, Affiliated Women's Hospital of Jiangnan University, Jiangnan University, Wuxi, 214002, China.
BMC psychiatry
|October 29, 2024
概括
深度学习 (DL) 在分类儿童自闭症谱系障碍 (ASD) 中显示出高准确性,灵敏度,特异性和AUC均高于0.93.3. 然而,研究异质性限制了当前的发现,需要进一步的临床试验来确定DL的诊断实用性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 发育儿科 发育儿科
背景情况:
- 深度学习 (DL) 在识别儿童自闭症谱系障碍 (ASD) 方面表现有前途.
- 现有研究报告了基于DL的ASD预测的不同准确性.
- 这一元分析旨在巩固关于DL对儿童自闭症的分类准确性的发现.
研究的目的:
- 为了确定深度学习 (DL) 方法对儿童自闭症谱系障碍 (ASD) 的分类准确性.
- 进行对现有的DL研究进行元分析,以识别ASD.
- 在ASD分类中评估DL技术的灵敏度,特异性和AUC.
主要方法:
- 在PubMed,EMBASE,Cochrane图书馆和Web of Science进行了系统的文献搜索,截至2023年4月16日.
- 包括11项涉及9495名儿童的预测试验,研究质量使用QUADAS-2进行评估.
- 使用双变随机效应模型来计算聚合的灵敏度,特异性和AUC.
主要成果:
- 分析显示,在ASD分类中,DL的整体表现很高:灵敏度为0.95,特异性为0.93,AUC为0.98.
- 小组分析表明不同数据集 (例如Kaggle,ABIDE) 之间没有显著的异质性.
- 像ABIDE这样的特定数据集显示出高性能 (灵敏度0.97,特异性0.97).
结论:
- 深度学习技术在ASD分类方面表现出令人满意的诊断性能.
- 包含的研究之间显著的异质性可能会限制这次元分析的整体有效性.
- 进一步的临床试验对于验证DL在ASD诊断中的实际应用至关重要.
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