在马拉维,人工智能算法用于解释患有肺炎住院儿童的肺部声音
Nadia E Hoekstra1,2, Maganizo B Chagomerana3,4, Zachary H Smith2,5
1Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
一个人工智能 (AI) 系统准确地识别了严重肺炎儿童的异常肺部声音,改善了诊断. 这种人工智能支持的数字听觉显示了全球儿童健康的前景.
科学领域:
- 医疗技术 医疗技术 医学技术
- 人工智能的人工智能
- 儿童肺炎的发生
背景情况:
- 肺炎是全球五岁以下儿童死亡的重要原因之一.
- 目前世界卫生组织 (WHO) 对肺炎的诊断指南缺乏具体性.
- 传统的听力镜在资源有限的环境中对精确的肺部听觉提出了实施挑战.
研究的目的:
- 开发和评估一个支持人工智能 (AI) 的数字听觉系统,用于诊断儿童的严重肺炎.
- 评估AI算法在分析肺部声音的准确性,与专家医生解释相比.
主要方法:
- 2-59个月的儿童患有严重的肺炎在马拉维被录取.
- 肺部的声音被记录在六个胸部位置使用数字耳语镜.
- 人工智能算法对肺部声音记录的分析与医生倾听小组进行了比较.
主要成果:
- 人工智能算法和听力小组在胸部位置 (kappa=0.7) 的肺声分类上达到了83.1%的共识.
- 人工智能和专家组之间的患者级协议为91.6% (kappa=0.8).
- 在患者水平上检测异常的肺声音方面,AI表现出高灵敏度 (96.3%) 和特异性 (66.7%).
结论:
- 人工智能肺声分类算法在儿童重症肺炎中识别异常肺声时显示出高准确度.
- 这种人工智能系统对肺炎诊断的非特异性临床发现提供了潜在的改进.
- 未来的工作重点是训练人工智能识别无法解释的录音和各种异常的肺声音模式.
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