本文摘译自Medical Desing & OUTSOURCING

2020年6月18日 以下为详文

美国GE Healthcare 公司的官员认为，该公司新推出的胸腔护理套件产品（Lunit Insight CXR的八种AI算法集合）可以帮助临床医生对抗COVID-19。

GE网站截图

据GE公司称，新推出的AI套件可以快速分析胸部X线检查结果并标记异常，供放射科医生复查。它能识别潜在的肺炎，即COVID-19症状；它还可以突出显示结核病、肺结节和其他放射性的发现。

GE Healthcare公司CEO Kieran Murphy说：“这场大流行疫情已证明，提供医疗护理方面更为核心的技术是数据、分析、AI和互联。对于GE Healthcare公司而言，这意味着继续推进智能健康并提供创新技术。这一新产品就是最新示例，说明了X射线和AI是如何在新冠病毒威胁中支持最高标准的患者护理的。”

通过胸腔护理套件产品，放射科能读取到一份PACS中已标记的报告，医生们可以快速发现异常评分，针对八种中的每一个可能的异常、图像叠加和书面位置描述，来帮助快速诊断和治疗。

牛津大学放射学教授、欧洲胸科影像学会2020年主席Fergus Gleeson说：“临床医生正在寻找临床验证的方法，为尽早识别症状并确定哪些患者处于更高并发症风险且需要积极监控提供帮助。AI可以帮助识别这些差别，使医院资源能够针对那些住院期间和出院后将需要它们的人。”

原文：

GE Healthcare launches chest X-ray AI to better spot COVID-19

June 18, 2020 By Chris Newmarker Leave a Comment

Officials at GE Healthcare (NYSE:GE) think the company’s new Thoracic Care Suite — a collection of eight AI algorithms from Lunit Insight CXR — could help clinicians in the fight against COVID-19.

The AI suite, launched today, quickly analyzes chest X-ray findings and flags abnormalities for radiologist review, according to GE. It can spot potential pneumonia — a symptom of COVID-19. It can also highlight tuberculosis, lung nodules, and other radiological findings.

“The launch of our Thoracic Care Suite is a part of GE Healthcare’s larger effort to help ensure clinicians and partners on the front lines have the equipment they need to quickly diagnose and effectively treat COVID-19 patients,” GE Healthcare CEO Kieran Murphy said in a news release.

“The pandemic has proven that data, analytics, AI and connectivity will only become more central to delivering care. For GE Healthcare, that means continuing to advance intelligent health and providing innovative technologies. This new offering is the latest example of how X-ray and AI can uphold the highest standard of patient care amidst the most modern of disease threats,” Murphy said.

Radiology through the Thoracic Care Suite can read a flagged report in picture archiving and communication systems (PACS). They can quickly find the abnormality score for each of the eight possible abnormalities, an image overlay and a written location description to help expedite diagnosis and treatment.

“Clinicians are looking for clinically proven methods to help identify symptoms early and determine which patients are at higher risk of complications and need to be actively monitored,” said Fergus Gleeson, professor of radiology at the University of Oxford and the 2020 President of the European Society of Thoracic Imaging. “AI can help identify these distinctions and enable hospital resources to be targeted to those that will need them whilst in hospital and following discharge.”