Abstract：
OBJECTIVE: Standard chest radiographs are a poor diagnostic tool for pediatric foreign body aspiration. Machine learning may improve upon the diagnostic capabilities of chest radiographs. The objective is to develop a machine learning algorithm that improves the diagnostic capabilities of chest radiographs in pediatric foreign body aspiration.
METHODS: This retrospective, diagnostic study included a retrospective chart review of patients with a potential diagnosis of FBA from 2010 to 2020. Frontal view chest radiographs were extracted, processed, and uploaded to Google AutoML Vision. The developed algorithm was then evaluated against a pediatric radiologist.
RESULTS: The study selected 566 patients who were presented with a suspected diagnosis of foreign body aspiration. One thousand six hundred and eighty eight chest radiograph images were collected. The sensitivity and specificity of the radiologist interpretation were 50.6% (43.1-58.0) and 88.7% (85.3-91.5), respectively. The sensitivity and specificity of the algorithm were 66.7% (43.0-85.4) and 95.3% (90.6-98.1), respectively. The precision and recall of the algorithm were both 91.8% with an AuPRC of 98.3%.
CONCLUSIONS: Chest radiograph analysis augmented with machine learning can diagnose foreign body aspiration in pediatric patients at a level similar to a read performed by a pediatric radiologist despite only using single-view, fixed images. Overall, this study highlights the potential and capabilities of machine learning in diagnosing conditions with a wide range of clinical presentations.
METHODS: 3 Laryngoscope, 2024.
METHODS: This retrospective, diagnostic study included a retrospective chart review of patients with a potential diagnosis of FBA from 2010 to 2020. Frontal view chest radiographs were extracted, processed, and uploaded to Google AutoML Vision. The developed algorithm was then evaluated against a pediatric radiologist.
RESULTS: The study selected 566 patients who were presented with a suspected diagnosis of foreign body aspiration. One thousand six hundred and eighty eight chest radiograph images were collected. The sensitivity and specificity of the radiologist interpretation were 50.6% (43.1-58.0) and 88.7% (85.3-91.5), respectively. The sensitivity and specificity of the algorithm were 66.7% (43.0-85.4) and 95.3% (90.6-98.1), respectively. The precision and recall of the algorithm were both 91.8% with an AuPRC of 98.3%.
CONCLUSIONS: Chest radiograph analysis augmented with machine learning can diagnose foreign body aspiration in pediatric patients at a level similar to a read performed by a pediatric radiologist despite only using single-view, fixed images. Overall, this study highlights the potential and capabilities of machine learning in diagnosing conditions with a wide range of clinical presentations.
METHODS: 3 Laryngoscope, 2024.
摘要：
目的：标准胸片是小儿异物吸入的不良诊断工具。机器学习可以改善胸部射线照片的诊断能力。目的是开发一种机器学习算法，该算法可提高小儿异物吸入中胸部X光片的诊断能力。
方法：本回顾性研究，诊断性研究包括2010年至2020年可能诊断为FBA的患者的回顾性图表回顾.提取正面胸部X光片，已处理,并上传到GoogleAutoMLVision。然后针对儿科放射科医生评估开发的算法。
结果：本研究选择了566例疑似诊断为异物吸入的患者。收集了一千六百八十八张胸片图像。放射科医生诊断的敏感性和特异性分别为50.6%(43.1-58.0)和88.7%(85.3-91.5),分别。算法的灵敏度和特异度分别为66.7%(43.0-85.4)和95.3%(90.6-98.1),分别。该算法的准确率和召回率均为91.8％，AuPRC为98.3％。
结论：使用机器学习增强的胸部X光片分析可以诊断儿科患者的异物吸入，其水平与儿科放射科医生的阅读水平相似，尽管仅使用单视图，固定图像。总的来说,这项研究强调了机器学习在诊断具有广泛临床表现的疾病方面的潜力和能力.
方法：3喉镜，2024.
方法：本回顾性研究，诊断性研究包括2010年至2020年可能诊断为FBA的患者的回顾性图表回顾.提取正面胸部X光片，已处理,并上传到GoogleAutoMLVision。然后针对儿科放射科医生评估开发的算法。
结果：本研究选择了566例疑似诊断为异物吸入的患者。收集了一千六百八十八张胸片图像。放射科医生诊断的敏感性和特异性分别为50.6%(43.1-58.0)和88.7%(85.3-91.5),分别。算法的灵敏度和特异度分别为66.7%(43.0-85.4)和95.3%(90.6-98.1),分别。该算法的准确率和召回率均为91.8％，AuPRC为98.3％。
结论：使用机器学习增强的胸部X光片分析可以诊断儿科患者的异物吸入，其水平与儿科放射科医生的阅读水平相似，尽管仅使用单视图，固定图像。总的来说,这项研究强调了机器学习在诊断具有广泛临床表现的疾病方面的潜力和能力.
方法：3喉镜，2024.
