目的探讨基于APP的延续护理在小儿脑瘫中的应用效果。方法选取2017年1月—2019年12月在厦门市妇幼保健院/厦门大学附属妇女儿童医院住院的68例脑瘫患儿作为研究对象,按照随机数字表法分为研究组(34例)和对照组(34例),对照组给予常规出...目的探讨基于APP的延续护理在小儿脑瘫中的应用效果。方法选取2017年1月—2019年12月在厦门市妇幼保健院/厦门大学附属妇女儿童医院住院的68例脑瘫患儿作为研究对象,按照随机数字表法分为研究组(34例)和对照组(34例),对照组给予常规出院宣教及出院后的电话回访,研究组在此基础上加用移动医疗APP进行延续护理。对比两组随访6个月后临床疗效和干预前后精细运动功能评定量表(peabody development motor scales2,PDMS-2)评分、粗大运动功能评估量表(gross motor function measure,GMFM)评分、Gesell发育量表(Gesell developmental schedules)评分、生活活动能力评分量表(activities of daily living,ADL)评分。结果出院前,两组MAS评分比较差异无统计学意义(P>0.05),随访6个月后,研究组MAS评分显著低于对照组,差异有统计学意义(P<0.05);出院前,两组Gesell发育测试评分、PDMS-2、GMFM评分比较差异无统计学意义(P>0.05),随访6个月后,研究组Gesell发育测试评分、PDMS-2、GMFM评分均明显高对照组,差异有统计学意义(P<0.05);两组出院前的ADL评分比较差异无统计学意义(P>0.05);随访6个月后,研究组患儿在社会活动、焦虑、抑郁、精神表现、日常生活活动方面评分显著低于对照组,差异有统计学意义(P<0.05)。结论基于APP进行小儿脑瘫的延续护理具有良好的治疗效果,可有效改善患儿肌张力,促进患儿运动功能的恢复,促进患者发育,提升患儿智力,改善患儿精神状态,提高其生活活动能力。展开更多
In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and pathogens.Among the commonly used approaches,convolutional neural networks have provi...In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and pathogens.Among the commonly used approaches,convolutional neural networks have provided results with high accuracy.The purpose of this work is to present an effective and practical system capable of seg-menting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural networks.The proposed approach consists of two stages:a semantic segmentation stage with severity calculation and a symptom lesion classification stage.Each stage was tested separately,highlighting the positive and negative points of each one.We obtained very good results for the severity estimation,suggesting that the model can estimate severity values very close to the real values.For the biotic stress classification,the accuracy rates were greater than 97%.Due to the promising results obtained,an App for Android platform was developed and imple-mented,consisting of semantic segmentation and severity calculation,as well as symptom classification to assist both specialists and farmers to identify and quantify biotic stresses using images of coffee leaves acquired by smartphone.展开更多
文摘目的探讨基于APP的延续护理在小儿脑瘫中的应用效果。方法选取2017年1月—2019年12月在厦门市妇幼保健院/厦门大学附属妇女儿童医院住院的68例脑瘫患儿作为研究对象,按照随机数字表法分为研究组(34例)和对照组(34例),对照组给予常规出院宣教及出院后的电话回访,研究组在此基础上加用移动医疗APP进行延续护理。对比两组随访6个月后临床疗效和干预前后精细运动功能评定量表(peabody development motor scales2,PDMS-2)评分、粗大运动功能评估量表(gross motor function measure,GMFM)评分、Gesell发育量表(Gesell developmental schedules)评分、生活活动能力评分量表(activities of daily living,ADL)评分。结果出院前,两组MAS评分比较差异无统计学意义(P>0.05),随访6个月后,研究组MAS评分显著低于对照组,差异有统计学意义(P<0.05);出院前,两组Gesell发育测试评分、PDMS-2、GMFM评分比较差异无统计学意义(P>0.05),随访6个月后,研究组Gesell发育测试评分、PDMS-2、GMFM评分均明显高对照组,差异有统计学意义(P<0.05);两组出院前的ADL评分比较差异无统计学意义(P>0.05);随访6个月后,研究组患儿在社会活动、焦虑、抑郁、精神表现、日常生活活动方面评分显著低于对照组,差异有统计学意义(P<0.05)。结论基于APP进行小儿脑瘫的延续护理具有良好的治疗效果,可有效改善患儿肌张力,促进患儿运动功能的恢复,促进患者发育,提升患儿智力,改善患儿精神状态,提高其生活活动能力。
文摘In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and pathogens.Among the commonly used approaches,convolutional neural networks have provided results with high accuracy.The purpose of this work is to present an effective and practical system capable of seg-menting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural networks.The proposed approach consists of two stages:a semantic segmentation stage with severity calculation and a symptom lesion classification stage.Each stage was tested separately,highlighting the positive and negative points of each one.We obtained very good results for the severity estimation,suggesting that the model can estimate severity values very close to the real values.For the biotic stress classification,the accuracy rates were greater than 97%.Due to the promising results obtained,an App for Android platform was developed and imple-mented,consisting of semantic segmentation and severity calculation,as well as symptom classification to assist both specialists and farmers to identify and quantify biotic stresses using images of coffee leaves acquired by smartphone.
