裂的不可靠性:从使用医生和人工智能的呼吸声研究中的见解
Chun-Hsiang Huang1, Chi-Hsin Chen1, Jing-Tong Tzeng2
1Department of Emergency Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu City, Taiwan, R.O.C.
NPJ primary care respiratory medicine
|October 15, 2024
概括
呼吸道声音分析仍然具有挑战性,特别是在裂时. 虽然人工智能 (AI) 是有前途的,但与喘息相比,准确识别裂需要进一步的研究,以便可靠的医疗决策.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 不一致的呼吸评估在物理评估中构成了重大挑战.
- 准确地解释呼吸声音对于有效的诊断和治疗至关重要.
研究的目的:
- 为了评估各种呼吸声音的差异识别能力.
- 为了比较人类医生和人工智能 (AI) 模型在呼吸声音解释中的诊断性能.
主要方法:
- 一项前性研究涉及五名医生对来自福尔摩萨呼吸声档案的11,532个呼吸声文件进行标签.
- 开发6个AI呼吸声解释模型.
- 两个额外的医生重新标记了不一致的标签,最终的标签由多数投票决定.
- 使用灵敏度,特异性和接收器操作特征曲线 (AUROC) 下的区域来评估人类和人工智能的性能.
主要成果:
- 对于喘息,医生和人工智能模型都表现出高灵敏度 (89.5%与86.0%) 和特异性 (96.4%与95.2%).
- 对于裂,两组都显示出高灵敏度 (93.9%对80.3%),但特异性较差 (56.6%对65.9%).
- 对医生和AI来说,与喘息相比,对裂的识别观察到较低的AUROC值.
结论:
- 精确识别裂,即使在人工智能协助下,与喘息相比仍然具有挑战性.
- 目前,裂纹在医疗决策中表现出不可靠性.
- 需要进一步的研究来改善裂纹检测和临床实用性.
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