Ienghong Kamonwon, Cheung Lap Woon, Gaysonsiri Dhanu, Apiratwarakul Korakot
Department of Emergency Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand.
Accident & Emergency Department, Princess Margaret Hospital, Kowloon, Hong Kong, China.
Emerg Radiol. 2025 Apr;32(2):241-246. doi: 10.1007/s10140-025-02319-4. Epub 2025 Feb 14.
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.
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.
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).
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.
肺部超声中的B线一直是检测肺水肿的关键线索。然而,将B线与其他伪像区分开来具有挑战性,尤其是对于新手床旁超声(POCUS)从业者而言。本研究旨在确定使用人工智能自动检测B线(自动B线)对检测肺水肿的有效性。
对2023年1月至2024年6月在急诊科接受治疗的呼吸困难患者进行回顾性研究。评估超声记录和电子急诊科病历中自动B线在检测肺水肿方面的敏感性、特异性、阳性似然比和阴性似然比。
纳入了66例最终诊断为肺水肿的患者,其中54.68%的患者肺部超声B线呈阳性。自动B线的敏感性为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),特异性为63.09%(95%CI:0.58 - 0.69)。
自动B线在诊断新手POCUS从业者的肺水肿方面具有高度敏感性。医生与人工智能的临床整合增强了诊断能力。