This is work-in-progress artificial intelligence app on Fujifilm's mobile digital radiography system to immediately detect pneumothorax (a collapsed lung), The AI highlights the area of interest to show the location to the technologist and attending physician in a unit before the image is even uploaded to the PACS for a read by a radiologist. The technology also can flag the study for an immediate read in the PACS worklist for confirmation by a human. This technology is from a third-party and will be offered on Fujifilm's REiLI AI platform. Applications like this that have immediate impact on critical patient care saw a lot of interest at RSNA 2019. Photos by ITN Editor Dave Fornell.
Radiology artificial intelligence (AI) was again the hottest topic at the 2019Radiological Society of North America (RSNA)annual meeting in December. AI was a primary theme in the larger booths in the north and south expo floors, as well as on the new third expo floor dedicated AI showcase.
The separate AI show floor did not make many AI vendors happy. Many wanted theartificial intelligenceshowcase on the same level as the other expo halls to reduce the shuttling between the floors for meetings. RSNA organizers pointed out to one startup that due to the sheer number of AI exhibitors, they had to give the showcase its own space. The RSNA app listed 176 companies, large and small, under the artificial intelligence/machine learning category. The AI showcase had 136 companies, 38 of which were first-time exhibitors.
Here are our key takeaways on AI at RSNA19.
• Most of the larger OEMs are also infusing AI into their solutions and few large vendors remain skeptical about the hype of AI.
• Mature AI startups want to “walk the talk” on delivering value, proving it by using publications with their academic partners. Some are expanding their focus beyond just image analysis to provide value-added services around the care continuum.
• Newer image analysis startups continue to emerge. We wonder how many will succeed?
• Leverage of natural language processing is picking up with larger companies and at least one new startup utilizing it for different use cases in radiology, beyond the image analysis area.
• The market for AI development support tools is seeing activity, though solutions developed by health systems would, of course, be limited for their own use.
AI is integrated into GE Healthcare's new Venue Go point-of-care ultrasound system (POCUS) to automate image measurements and track them over time with repeat imaging of the same patient. It is an example of the large imaging vendors integrating AI into their products.
Support for Developing Your Own AI Algorithms in Radiology
这是一个很有趣的领域，它正在保持自己的地位。之前有几家初创公司在数据提取、匿名化、注释、AI算法训练和验证等方面提供帮助。然后是英伟达的克拉拉人工智能辅助注释解决方案，今年进一步加强了克拉拉FL，联邦学习解决方案，利用英伟达在边缘计算的专业知识。今年，RSNA还成立了一家新公司HOPPR，帮助卫生系统内部开发AI解决方案。飞利浦的IntelliSpace是人工智能发展领域的另一个例子。
Medical Imaging Analysis AI
There are too many AI startups to keep track of. Some articles quote industry players saying we will see the beginning of consolidation of these companies in the 12-18 months. Several new startups are probably taking the narrow view of image analysis. The mature ones are beginning to look broader across the patient journey to see where else they can make an impact.
Others, like Aidoc, want to convince potential customers of the value of their solution in a way that doctors understand hard, clinical evidence published in peer-reviewed journals, in partnership with some of the bigger names in healthcare. Their evidence on reduction in length of stay (LOS), efficiency gains and improvements in turn-around-time (TAT) are equally important, apart from solution accuracy, which most of the industry tends to focus on. At the end of the day, it is the ROI for the hospital that matters the most, especially since a majority of AI solutions do not have reimbursements yet, and some might never have them. This is a refreshing perspective that most startups must consider to be truly successful.
Apart from these efforts to rise above the hype of AI, another approach that caught our attention was the focus on impactful solutions for regions that lack resources in the developing world. Enabling public health screenings in areas where radiologists are probably hundreds of miles away, such as Africa and some Asian countries is an impactful use of AI image analysis—advanced algorithms designed to help catch diseases early. Qure.AI is a startup that has focused efforts on tuberculosis screening, but interestingly enough, it has built a way to make an AI algorithm work without internet connectivity or a PACS. Using a small plug-in device that contains the AI algorithm, it can be used in very remote regions.
Qure。AI提供了一个小插件盒，可以在不需要连接互联网或PACS的偏远地区使用肺AI检测应用程序。这种技术正在非洲和菲律宾的流动DR单位用于结核病筛查项目。立即确定结核病患者对于防止感染的进一步传播至关重要，因为几乎不可能在几周或几个月后在贫民窟或偏远农业地区找到患者。
Artificial Intelligence Marketplace and Enterprise Imaging Approaches
IBM Watson和Arterys是继Blackford Analysis、TeraRecon的Envoy AI和Nuance之后进入该市场的两家新公司。人工智能市场就像应用程序商店，医疗机构可以从一个供应商那里购买人工智能。最初的三架是在2017年RSNA上发射的。例如，IBM可以将其人工智能技术应用到各个领域(不仅仅是放射领域)，并将其整合起来，以构建更大的价值主张。
TeraRecon's Envoy AI marketplace offers apps from several third-party artificial intelligence startup companies in one location. This is an example of AI available through Envoy to automate cardiac MRI analysis. There was no human intervention in the contouring here of the myocardium of the left ventricle or the outline of the right ventricle.
虽然市场为医院带来的核心价值主张是避免了与每个AI供应商签订多个合同和安装多个设备的需要，但现在企业成像供应商也有类似的主张。整车厂积极提供类似的解决方案，通过构建自己的算法，并在现有解决方案更好的地方进行合作。这一新兴方法的主要受益者之一可能是MaxQ.AI。该中风解决方案供应商不仅可以在所有五个市场上购买，还可以在通用电气医疗保健的爱迪生平台和富士胶片的REiLI上购买。
AI to Make Imaging Systems Into Intelligent Machines
There have been efforts to provide intelligent applications closer to the modality for better patient positioning, selection of the optimal protocol and faster image reconstruction. The newest diagnostic imaging player, United Imaging, is also leveraging its AI initiatives to drive this concept. Beyond last year’s announcement in China about its uAI initiative, the vendor is introducing the first applications in the U.S. market, pending FDA 510(k) clearances. This includes United's DELTA, a deep learning enhancement to its uCT (computed tomography) 7 series; HYPER Deep Learning Reconstruction in the routine PET/CT workflow of its uMI 550 system; and AI-assisted compressed sensing (ACS) full-coverage image acceleration for its uMR 780 system. We believe this will also be another area to watch.
Radiologists Funding AI Startups
最后但并非最不重要的是这种有趣的方法。Bold Brain Ventures本身就是一家独特的风险投资公司，是由两位放射科医生创立的放射科AI投资基金，旨在鼓励其他放射科医生也投资AI初创公司。我们的目标是通过放射科医生的输入来指导人工智能解决方案的开发，同时也帮助放射科医生采用人工智能。两位创始人已经亲自投资了一些创业公司，其中大部分都在人工智能展会上。他们中的一些人还在一起合作，进一步加强AI解决方案的开发。
Our main prediction for this space in 2020 is "survival of the fittest" for the number of AI startups that continue to grow each year. Explore further insights here:http://frost.ly/3xj.
关于作者:Siddharth (Sid) Shah和Srikanth Kompalli是医疗保健市场研究公司Frost & Sullivan的转型健康项目经理。
August 11, 2022
