Department of Emergency Medicine, Columbia University Vagelos College of Physicians & Surgeons, New York Presbyterian Morgan Stanley Children's Hospital, NY, NY, United States of America.
Oregon Health & Science University, Portland, Oregon, United States of America.
PLoS One. 2024 Aug 27;19(8):e0309109. doi: 10.1371/journal.pone.0309109. eCollection 2024.
Severe pneumonia is the leading cause of death among young children worldwide, disproportionately impacting children who lack access to advanced diagnostic imaging. Here our objectives were to develop and test the accuracy of an artificial intelligence algorithm for detecting features of pulmonary consolidation on point-of-care lung ultrasounds among hospitalized children.
This was a prospective, multicenter center study conducted at academic Emergency Department and Pediatric inpatient or intensive care units between 2018-2020. Pediatric participants from 18 months to 17 years old with suspicion of lower respiratory tract infection were enrolled. Bedside lung ultrasounds were performed using a Philips handheld Lumify C5-2 transducer and standardized protocol to collect video loops from twelve lung zones, and lung features at both the video and frame levels annotated. Data from both affected and unaffected lung fields were split at the participant level into training, tuning, and holdout sets used to train, tune hyperparameters, and test an algorithm for detection of consolidation features. Data collected from adults with lower respiratory tract disease were added to enrich the training set. Algorithm performance at the video level to detect consolidation on lung ultrasound was determined using reference standard diagnosis of positive or negative pneumonia derived from clinical data.
Data from 107 pediatric participants yielded 117 unique exams and contributed 604 positive and 589 negative videos for consolidation that were utilized for the algorithm development process. Overall accuracy for the model for identification and localization of consolidation was 88.5%, with sensitivity 88%, specificity 89%, positive predictive value 89%, and negative predictive value 87%.
Our algorithm demonstrated high accuracy for identification of consolidation features on pediatric chest ultrasound in children with pneumonia. Automated diagnostic support on an ultraportable point-of-care device has important implications for global health, particularly in austere settings.
严重肺炎是全球导致儿童死亡的主要原因，尤其对那些无法获得先进诊断成像的儿童造成了不成比例的影响。本研究旨在开发和测试一种人工智能算法，用于检测住院儿童床旁肺部超声中肺实变的特征，并评估其准确性。
这是一项前瞻性、多中心研究，于 2018 年至 2020 年在学术急诊部和儿科住院部或重症监护病房进行。研究纳入了年龄在 18 个月至 17 岁之间、疑似下呼吸道感染的儿科患者。使用飞利浦手持式 Lumify C5-2 换能器和标准化协议进行床边肺部超声检查，从 12 个肺区采集视频循环，并对视频和帧级别的肺部特征进行标注。将来自受影响和未受影响肺区的数据按照患者水平进行划分，分为训练集、调优集和验证集，用于训练、调优超参数，并测试用于检测实变特征的算法。从患有下呼吸道疾病的成年人中收集的数据被添加到训练集中以丰富数据集。使用来自临床数据的阳性或阴性肺炎的参考标准诊断来确定算法在检测肺部超声上的实变的视频级性能。
来自 107 名儿科患者的数据产生了 117 个独特的检查，并为算法开发过程提供了 604 个阳性和 589 个阴性的实变视频。该模型用于识别和定位实变的整体准确率为 88.5%，敏感度为 88%，特异性为 89%，阳性预测值为 89%，阴性预测值为 87%。
我们的算法在识别肺炎儿童床旁肺部超声中的实变特征方面具有很高的准确性。在超便携的床旁设备上实现自动诊断支持对全球健康具有重要意义，尤其是在资源匮乏的环境中。
