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Use of a Dual Artificial Intelligence Platform to Detect Unreported Lung Nodules.

使用双重人工智能平台检测未报告的肺结节。

  • 影响因子:1.48
  • DOI:10.1097/RCT.0000000000001118
  • 作者列表:"Yen A","Pfeffer Y","Blumenfeld A","Balcombe JN","Berland LL","Tanenbaum L","Kligerman SJ
  • 发表时间:2021-03-01
Abstract

OBJECTIVE:To investigate the performance of Dual-AI Deep Learning Platform in detecting unreported pulmonary nodules that are 6 mm or greater, comprising computer-vision (CV) algorithm to detect pulmonary nodules, with positive results filtered by natural language processing (NLP) analysis of the dictated report. METHODS:Retrospective analysis of 5047 chest CT scans and corresponding reports. Cases which were both CV algorithm positive (nodule ≥ 6 mm) and NLP negative (nodule not reported), were outputted for review by 2 chest radiologists. RESULTS:The CV algorithm detected nodules that are 6 mm or greater in 1830 (36.3%) of 5047 cases. Three hundred fifty-five (19.4%) were unreported by the radiologist, as per NLP algorithm. Expert review determined that 139 (39.2%) of 355 cases were true positives (2.8% of all cases). One hundred thirty (36.7%) of 355 cases were unnecessary alerts-vague language in the report confounded the NLP algorithm. Eighty-six (24.2%) of 355 cases were false positives. CONCLUSIONS:Dual-AI platform detected actionable unreported nodules in 2.8% of chest CT scans, yet minimized intrusion to radiologist's workflow by avoiding alerts for most already-reported nodules.

摘要

目的: 研究双AI深度学习平台在检测6毫米或更大的未报告肺结节方面的性能,包括计算机视觉 (CV) 算法来检测肺结节,阳性结果通过口述报告的自然语言处理 (NLP) 分析过滤。 方法: 回顾性分析5047例胸部ct扫描及相应报告。CV算法阳性 (结节 ≥ 6毫米) 和NLP阴性 (结节未报告) 的病例被输出供2名胸部放射科医师审查。 结果: CV算法在6毫米例中的1830例 (36.3%) 中检测到5047或更大的结节。根据NLP算法,放射科医师未报告19.4% 例 ().专家评审确定139例病例中有39.2% 例 (355) 为真阳性 (占所有病例的2.8%)。36.7% 例中有355例 () 是不必要的警告-报告中的模糊语言混淆了NLP算法。24.2% 例中86例 (355) 为假阳性。 结论: 双AI平台在2.8% 的胸部ct扫描中检测到可操作的未报告结节,但通过避免大多数已报告结节的警报,最小化了对放射科医生工作流程的入侵。

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影响因子:2.68
发表时间:2021-02-01
DOI:10.1080/14656566.2020.1814255
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发表时间:2021-03-24
DOI:10.1007/s11033-021-06299-9
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