基于机器学习的自闭症谱系障碍脑成像诊断

G. Venkatasubramanian
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引用次数: 0

摘要

尽管在过去几十年里进行了研究,但客观诊断精神疾病的可靠、可复制和临床可翻译的标志物仍有待确定。生物异质性(部分原因是环境相互作用进一步加剧了复杂的遗传基础)、当代诊断标准/临床症状评分与尖端神经科学观察结果之间的潜在不匹配,以及类似的其他因素,使生物标志物的识别成为一项艰巨的挑战。在自闭症谱系障碍(ASD)等儿童期疾病的背景下,这一挑战变得更加难以解决,尤其是因为检查发育中的大脑更加复杂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning-based Diagnosis of Autism Spectrum Disorder Using Brain Imaging
In spite of research over the past decades, robust, replicable, and clinically translatable markers to objectively diagnose psychiatric disorders are yet to be ascertained. Several factors such as biological heterogeneity (partly due to the complex genetic basis that is further compounded by environmental interactions), the potential mismatch between contemporary diagnostic criteria / clinical symptom scores and findings that emanate from cutting-edge neuroscience observations, and similar others have made the identification of biomarkers a daunting challenge. This challenge becomes much harder to solve in the context of disorders of childhood onset such as, autism spectrum disorders (ASD) especially because of the additional complexity of examining the developing brain.
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