本文摘譯自Medical Desing & OUTSOURCING

2020年6月18日?以下為詳文

美國GE Healthcare 公司的官員認為，該公司新推出的胸腔護理套件產品（Lunit Insight CXR的八種AI算法集合）可以幫助臨床醫(yī)生對抗COVID-19。

GE網(wǎng)站截圖

據(jù)GE公司稱，新推出的AI套件可以快速分析胸部X線檢查結果并標記異常，供放射科醫(yī)生復查。它能識別潛在的肺炎，即COVID-19癥狀；它還可以突出顯示結核病、肺結節(jié)和其他放射性的發(fā)現(xiàn)。

GE Healthcare公司CEO Kieran Murphy說：“這場大流行疫情已證明，提供醫(yī)療護理方面更為核心的技術是數(shù)據(jù)、分析、AI和互聯(lián)。對于GE Healthcare公司而言，這意味著繼續(xù)推進智能健康并提供創(chuàng)新技術。這一新產品就是最新示例，說明了X射線和AI是如何在新冠病毒威脅中支持最高標準的患者護理的。”

通過胸腔護理套件產品，放射科能讀取到一份PACS中已標記的報告，醫(yī)生們可以快速發(fā)現(xiàn)異常評分，針對八種中的每一個可能的異常、圖像疊加和書面位置描述，來幫助快速診斷和治療。

牛津大學放射學教授、歐洲胸科影像學會2020年主席Fergus Gleeson說：“臨床醫(yī)生正在尋找臨床驗證的方法，為盡早識別癥狀并確定哪些患者處于更高并發(fā)癥風險且需要積極監(jiān)控提供幫助。AI可以幫助識別這些差別，使醫(yī)院資源能夠針對那些住院期間和出院后將需要它們的人。”

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.”