人工智能在支持医疗专业人员和护理人员治疗自闭症儿童中的应用
Application of Artificial Intelligence in Supporting Healthcare Professionals and Caregivers in Treatment of Autistic Children
AI总结：
针对自闭症谱系障碍诊断治疗难题，研究开发了分析儿童日常面部与身体表情的精密算法，构建基于Xception和ResNet50V2的深度学习自闭症检测系统，实现高诊断准确率，展现AI在ASD诊疗管理中的变革潜力。
摘要：
自闭症谱系障碍（Autism Spectrum Disorder, ASD）是一种多层面的神经发育疾病，其特征为社交互动困难、沟通障碍以及重复性行为。尽管对ASD的认知已有进展，但由于症状表现的差异性以及对多学科护理方法的需求，其诊断和治疗仍面临重大挑战。本文探究了人工智能（Artificial Intelligence, AI）提升医疗专业人员和护理人员管理ASD能力的潜力。我们开发了一种精密算法，用于分析自闭症儿童和非自闭症儿童在日常活动中的面部和身体表情，进而构建了一个功能强大的基于深度学习的自闭症检测系统。研究表明，AI模型，尤其是Xception和ResNet50V2架构，在诊断自闭症谱系障碍（ASD）方面实现了高准确率。本研究凸显了AI在改善ASD的诊断、治疗和综合管理方面的变革性潜力。我们的研究显示，AI模型，特别是Xception和ResNet50V2架构，在诊断ASD方面表现出高准确率。
英文摘要：
Autism Spectrum Disorder (ASD) represents a multifaceted neurodevelopmental condition marked by difficulties in social interaction, communication impediments, and repetitive behaviors. Despite progress in understanding ASD, its diagnosis and treatment continue to pose significant challenges due to the variability in symptomatology and the necessity for multidisciplinary care approaches. This paper investigates the potential of Artificial Intelligence (AI) to augment the capabilities of healthcare professionals and caregivers in managing ASD. We have developed a sophisticated algorithm designed to analyze facial and bodily expressions during daily activities of both autistic and non-autistic children, leading to the development of a powerful deep learning-based autism detection system. Our study demonstrated that AI models, specifically the Xception and ResNet50V2 architectures, achieved high accuracy in diagnosing Autism Spectrum Disorder (ASD). This research highlights the transformative potential of AI in improving the diagnosis, treatment, and comprehensive management of ASD. Our study revealed that AI models, notably the Xception and ResNet50V2 architectures, demonstrated high accuracy in diagnosing ASD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
