论文标题
自闭症可以被诊断出患有AI吗？
Can autism be diagnosed with AI?
论文作者
论文摘要
带有深度学习模型的放射学模型在计算机辅助诊断中变得很受欢迎，并且在许多临床任务方面的表现优于人类专家。具体而言，基于人工智能（AI）的放射分组模型正在使用医学数据（即图像，分子数据，临床变量等）来预测诸如自闭症谱系障碍（ASD）之类的临床任务。在这篇综述中，我们总结并讨论了用于ASD分析的放射线技术。目前，ASD的有限的放射组工作与脑厚度的形态特征的变化有关，这与纹理分析不同。这些技术基于成像形状特征，可以与预测ASD的预测模型一起使用。这篇评论探讨了基于ASD的放射线学的进展，并简要描述ASD以及用于在ASD和健康对照组（HC）受试者之间进行分类的当前非侵入性技术。使用AI，还将描述使用深度学习技术的新的放射组模型。为了考虑深入CNN的纹理分析，建议将更多的研究与各种MRI站点上的其他验证步骤集成在一起。
Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks like Autism Spectrum Disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and Healthy Control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.
