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目的

针对传统医疗设备故障检测方法响应时间长、漏检率高、误报率高等问题,提出一种基于人工智能的医疗设备故障自动检测技术,以提高故障检测的准确性与实时性。

方法

采用卷积神经网络(convolutional neural network, CNN)与双向长短期记忆网络(bidirectional long short-term memory, Bi-LSTM)结合注意力机制的深度学习模型,实现设备运行数据的空间特征和时间序列特征的同步提取;构建“端-边-云”三层协同架构,在端侧实现毫秒级异常初筛。

结果

模型在测试集上的故障检出率达95.7%,单次推理时间仅450 ms;相比传统检测方法,人工智能自动检测技术故障检出率提升25.40个百分点,误报率从23.8%降至6.2%,平均响应时间从47.5 min缩短至8.3 min,设备年均停机时间减少75.60%,维护成本降低40.30%。

结论

该技术显著提升了医疗设备故障检测的准确性与实时性,具有良好的临床应用与推广价值。

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基于人工智能的医疗设备故障自动检测技术研究
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实验室检测 | 创新应用 2026,4(6): 42-45
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实验室检测 | 创新应用 2026, 4(6): 42-45
基于人工智能的医疗设备故障自动检测技术研究
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周少晖*
作者信息
  • 安徽省泾县医院医学工程部,宣城 242500

通讯作者:

周少晖,工程师,主要研究方向为医疗设备、医院信息化建设。E-mail:
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出版时间: 2026-03-23
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目的

针对传统医疗设备故障检测方法响应时间长、漏检率高、误报率高等问题,提出一种基于人工智能的医疗设备故障自动检测技术,以提高故障检测的准确性与实时性。

方法

采用卷积神经网络(convolutional neural network, CNN)与双向长短期记忆网络(bidirectional long short-term memory, Bi-LSTM)结合注意力机制的深度学习模型,实现设备运行数据的空间特征和时间序列特征的同步提取;构建“端-边-云”三层协同架构,在端侧实现毫秒级异常初筛。

结果

模型在测试集上的故障检出率达95.7%,单次推理时间仅450 ms;相比传统检测方法,人工智能自动检测技术故障检出率提升25.40个百分点,误报率从23.8%降至6.2%,平均响应时间从47.5 min缩短至8.3 min,设备年均停机时间减少75.60%,维护成本降低40.30%。

结论

该技术显著提升了医疗设备故障检测的准确性与实时性,具有良好的临床应用与推广价值。

人工智能  /  医疗设备  /  故障诊断  /  深度学习
周少晖. 基于人工智能的医疗设备故障自动检测技术研究. 实验室检测, 2026 , 4 (6) : 42 -45 .
2026年第4卷第6期
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  • 首发时间:2026-05-14
  • 出版时间:2026-03-23
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    安徽省泾县医院医学工程部,宣城 242500

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周少晖,工程师,主要研究方向为医疗设备、医院信息化建设。E-mail:
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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