Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking ability.The early detection of ASD ...Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking ability.The early detection of ASD is essential for delivering effective,timely interventions.Various facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early stage.Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)model.Our model achieved a detection accuracy of 87.7%and performed better than other established ASD detection models.Therefore,this model could facilitate the early detection of ASD in clinical practice.展开更多
Autism spectrum disorder(ASD)is a neurological disorder in which a significant number of children experience a developmental regression characterized by a loss of previously-acquired skills and abilities.Loss of neuro...Autism spectrum disorder(ASD)is a neurological disorder in which a significant number of children experience a developmental regression characterized by a loss of previously-acquired skills and abilities.Loss of neurological function in ASD,as observed in affected children who have regressed,can be explained as neurodegeneration.Although there is research evidence of neurodegeneration or progressive encephalopathy in ASD,the issue of neurodegeneration in ASD is still under debate.Evidence of neurodegeneration in the brain in ASD includes:(1)neuronal cell loss,(2)activated microglia and astrocytes,(3)proinflammatory cytokines,(4)oxidative stress,and(5)elevated 8-oxo-guanosine levels.The evidence from this review suggests that neurodegeneration underlies the loss of neurological function in children with ASD who have experienced regression and loss of previously acquired skills and abilities,and that research into treatments to address the issue of neurodegeneration in ASD are warranted.展开更多
文摘Autism spectrum disorder(ASD)is a challenging and complex neurodevelopment syndrome that affects the child’s language,speech,social skills,communication skills,and logical thinking ability.The early detection of ASD is essential for delivering effective,timely interventions.Various facial features such as a lack of eye contact,showing uncommon hand or body movements,bab-bling or talking in an unusual tone,and not using common gestures could be used to detect and classify ASD at an early stage.Our study aimed to develop a deep transfer learning model to facilitate the early detection of ASD based on facial fea-tures.A dataset of facial images of autistic and non-autistic children was collected from the Kaggle data repository and was used to develop the transfer learning AlexNet(ASDDTLA)model.Our model achieved a detection accuracy of 87.7%and performed better than other established ASD detection models.Therefore,this model could facilitate the early detection of ASD in clinical practice.
文摘Autism spectrum disorder(ASD)is a neurological disorder in which a significant number of children experience a developmental regression characterized by a loss of previously-acquired skills and abilities.Loss of neurological function in ASD,as observed in affected children who have regressed,can be explained as neurodegeneration.Although there is research evidence of neurodegeneration or progressive encephalopathy in ASD,the issue of neurodegeneration in ASD is still under debate.Evidence of neurodegeneration in the brain in ASD includes:(1)neuronal cell loss,(2)activated microglia and astrocytes,(3)proinflammatory cytokines,(4)oxidative stress,and(5)elevated 8-oxo-guanosine levels.The evidence from this review suggests that neurodegeneration underlies the loss of neurological function in children with ASD who have experienced regression and loss of previously acquired skills and abilities,and that research into treatments to address the issue of neurodegeneration in ASD are warranted.
文摘目的评价感音神经性听力损失患者(耳蜗病变)与听神经病谱系障碍患者听觉系统的频率选择特异性,对比说明不同类型听力损失对听觉系统频率选择特异性的影响。方法使用测量心理物理调谐曲线(psychophysical tuning curve,PTC)的方法评价频率选择特异性,即当存在一个纯音信号,其频率和强度保持不变,加入另一个窄带噪声,噪声的中心频率和强度均发生变化,通过改变噪声的中心频率与强度掩蔽纯音信号,由此获得的窄带噪声中心频率与其强度的关系曲线。PTC越窄,尖部越尖锐说明频率选择特异性越好,使用测试频率与PTC曲线最小强度上10 d B的曲线宽度的比值(Q10dB)评价PTC曲线的尖锐程度。选取听力正常受试者11名,感音神经性听力损失受试者14例,听神经病谱系障碍患者17例,测量3个组受试者双耳在500Hz和1000Hz处的心理物理调谐曲线。结果听力正常受试者平均Q10dB结果为3.4±0.9,感音神经性听力损失受试者平均Q10d B结果为1.8±0.4,听神经病谱系障碍受试者平均Q10d B结果为3.5±1.0。听神经病谱系障碍患者组Q10 d B结果与听力正常组Q10dB结果间无显著性差异(P>0.05),而感音神经性听力损失组Q10dB结果与听力正常组(F=34.90,P<0.001)和听神经病谱系障碍组Q10dB结果间均存在显著性差异(F=31.09,P<0.001)。结论耳蜗病变会导致频率选择特异性障碍,而听神经病谱系障碍患者听觉系统的频率选择特异性可能基本正常。
