自动b线检测的诊断性能评估肺水肿在急诊新手护理点超声从业人员。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Emergency Radiology Pub Date : 2025-04-01 Epub Date: 2025-02-14 DOI:10.1007/s10140-025-02319-4
Kamonwon Ienghong, Lap Woon Cheung, Dhanu Gaysonsiri, Korakot Apiratwarakul
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引用次数: 0

摘要

目的:肺超声b线是诊断肺水肿的重要线索。然而,区分b线与其他伪影是一个挑战,特别是对于新手护理点超声(POCUS)从业者。本研究旨在确定利用人工智能自动检测b线(Auto b线)检测肺水肿的有效性。方法:对2023年1月至2024年6月在急诊科治疗的呼吸困难患者进行回顾性研究。对超声文件和电子急诊科病历进行敏感性、特异性、自动b线检测肺水肿的阳性似然比和阴性似然比评估。结果:66例最终诊断为肺水肿的患者入组,其中54.68%的患者肺超声b线阳性。Auto b线的灵敏度为95.6%(95%置信区间[CI]: 0.92-0.98),特异性为77.2% (95% CI: 0.74-0.80)。医生的敏感性为82.7% (95% CI: 0.79-0.97),敏感性为63.09% (95% CI: 0.58-0.69)。结论:自动b线对诊断POCUS新手肺水肿有较高的敏感性。医生和人工智能的临床整合提高了诊断能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.

Purpose: B-lines in lung ultrasound have been a critical clue for detecting pulmonary edema. However, distinguishing B-lines from other artifacts is a challenge, especially for novice point of care ultrasound (POCUS) practitioners. This study aimed to determine the efficacy of automatic detection of B-lines using artificial intelligence (Auto B-lines) for detecting pulmonary edema.

Methods: A retrospective study was conducted on dyspnea patients treated at the emergency department between January 2023 and June 2024. Ultrasound documentation and electronic emergency department medical records were evaluated for sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of auto B-lines in detection of pulmonary edema.

Results: Sixty-six patients with a final diagnosis of pulmonary edema were enrolled, with 54.68% having positive B-lines in lung ultrasound. Auto B-lines had 95.6% sensitivity (95% confidence interval [CI]: 0.92-0.98) and 77.2% specificity (95% CI: 0.74-0.80). Physicians demonstrated 82.7% sensitivity (95% CI: 0.79-0.97) and 63.09% sensitivity (95% CI: 0.58-0.69).

Conclusion: The auto B-lines were highly sensitive in diagnosing pulmonary edema in novice POCUS practitioners. The clinical integration of physicians and artificial intelligence enhances diagnostic capabilities.

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来源期刊
Emergency Radiology
Emergency Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
4.50%
发文量
98
期刊介绍: To advance and improve the radiologic aspects of emergency careTo establish Emergency Radiology as an area of special interest in the field of diagnostic imagingTo improve methods of education in Emergency RadiologyTo provide, through formal meetings, a mechanism for presentation of scientific papers on various aspects of Emergency Radiology and continuing educationTo promote research in Emergency Radiology by clinical and basic science investigators, including residents and other traineesTo act as the resource body on Emergency Radiology for those interested in emergency patient care Members of the American Society of Emergency Radiology (ASER) receive the Emergency Radiology journal as a benefit of membership!
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