用于基于证据的自闭症谱系障碍筛查的多模态大语言模型

中文摘要 AI 辅助

自闭症谱系障碍（ASD）的临床管理在早期筛查方面面临瓶颈，主要原因是训练有素的专家稀缺，且传统评估工具具有主观性。在此，我们介绍了ASDchat，一种专为基于证据的ASD筛查而设计的多模态大语言模型，该模型以视频、音频和对话作为输入。ASDchat采用双分支架构，其中决策分支生成筛查概率，证据分支生成可追踪的、带时间戳的行为证据，并与标准化临床标准（ADOS-2）保持一致。该模型在来自中国27个中心的1,035名参与者的数据集上进行了训练和评估，该数据集涵盖了典型发育（TD）儿童、ASD儿童以及其他障碍儿童。对于ASD与TD的区分，ASDchat达到了0.953±0.021的受试者工作特征曲线下面积（AUC）。在未用于训练的9个保留中心上，平均AUC为0.932。此外，对行为维度的无监督聚类将ASD病例分为六种具有不同表型特征的亚型，ASDchat为每种亚型提出了干预建议。ASDchat为临床实践中大规模、基于证据的早期ASD筛查提供了一条可行路径。

英文摘要

The clinical management of autism spectrum disorder (ASD) faces a bottleneck in early screening, mainly because trained specialists are scarce and conventional assessment tools are subjective. Here, we introduce ASDchat, a multimodal large language model designed for evidence-based ASD screening, which takes video, audio, and dialogue as input. ASDchat adopts a dual-branch architecture, where the decision branch generates screening probabilities and the evidence branch generates traceable, timestamped behavioral evidence aligned with standardized clinical criteria (ADOS-2). The model was trained and evaluated on a dataset of 1,035 participants from 27 sites in China, which covered typically developing (TD) children, children with ASD, and children with other disorders. For ASD versus TD, ASDchat reached an area under the receiver operating characteristic curve (AUC) of 0.953 $\pm$ 0.021. On 9 held-out sites that were not used for training, the mean AUC was 0.932. Furthermore, unsupervised clustering of the behavioral dimensions split the ASD cases into six subtypes with different phenotypic profiles, and ASDchat suggests an intervention for each subtype. ASDchat provides a feasible path for large-scale, evidence-based early ASD screening in clinical practice.

发表机构

• Zhejiang Normal University（浙江师范大学）
• State Grid Corporation of China（国家电网有限公司）
• Huzhou Normal University（湖州师范学院）
• Zhejiang University of Technology（浙江工业大学）

机构由 AI 辅助整理，请以论文原文为准。