A team in Warwick have developed an artificial intelligence system which could cut the time it takes to assess critical chest x-rays from 11 days to less than three.
Professor Giovanni Montana, chair in data science at Warwick Manufacturing Group (WMG) at The University of Warwick, led a team that developed the system which can recognise radiological abnormalities in chest x-rays.
The system can asses how severe the abnormality is and, therefore, how quickly a patient needs treatment.
They also developed a natural language processing algorithm which can read a radiologist report, understand their finding and automatically prioritise cases based on their severity.
The AI system can be used from the moment a patient is admitted to hospital.
Montana said: “When a patient is admitted to the hospital to have a diagnostic chest x-ray, the image goes into a queue waiting for a radiologist to visually inspect it and write a report.
“The longer a patient waits for a report, the higher the chances that a potentially harmful condition is diagnosed too late.
“Using our technology, we have demonstrated that a radiologist can potentially identify scans presenting critical finding as soon as they are acquired, even before the patient leaves the hospital.
“This can make a tremendous difference in the diagnosis of life-threatening conditions such as lung cancer.”
In the United Kingdom, it is estimated that at any time there are over 300,000 radiographs waiting over 30 days for reporting.
Chest radiographs account for 40 per cent of all diagnostic imaging performed worldwide but the research shows alternative models of care, including AI, could be used to reduce delays.
The team have recently secured funding from the Wellcome Trust
Initiative to continue the project, which they hope will be commercially available this year.
A Digital Health News feature, published in March 2017, looked into how AI is already beginning to reshape radiology imaging and diagnostics at NHS trusts.
沃里克的一个团队开发了一个人工智能系统，可以将评估胸部 X 射线的时间从11天缩短到不到3天。
Warwick 大学 Warwick Manufacturing Group ( WMG )数据科学主任 Giovanni 蒙大拿州教授领导了一个研究小组，该小组开发了该系统，可识别胸部 X 射线中的放射异常。
蒙大拿州表示：“当一名患者被送往医院接受诊断胸部 X 光检查时，图像进入排队等待放射科医生对其进行视觉检查并撰写报告。
在联合王国，据估计，任何时候都有300,000多张 X 线片等待30天以上的报告。
胸部 X 线占全世界所有诊断成像的40%，但研究表明，包括人工智能在内的其他护理模式可用于减少延迟。
该团队最近从 Wellcome Trust Initiative 获得了资金，以继续该项目，他们希望该项目将在今年商业化。
2017年3月发布的《数字健康新闻》( Digital Health News )功能，探讨了人工智能已经开始重塑 NHS 信托公司的放射影像和诊断。