Feature | Artificial Intelligence | February 03, 2016 | Greg Freiherr

Artificial Intelligence May Hold Key to Radiology's Future

Artificial intelligence in radiology

Radiologists want a bigger role in healthcare, one that allows them a say in patient management, ideally one that goes from diagnosis to therapy follow-up. They will get it only if they can demonstrate their involvement adds clinical value. 

Improving patient outcomes is one route to this goal. Artificial intelligence (AI) may be the vehicle.

AI holds the potential for improved diagnosis. A San Francisco-based start-up called Enlitic is already pursuing this opportunity.

In the weeks leading up to RSNA 2015, Enlitic sent software engineers to about 80 medical imaging centers in Australia and Asia, bringing with them a deep learning algorithm designed for use on picture archiving and communication systems (PACS). The company hopes that this algorithm eventually will become smart enough to identify the signs of disease in every imaging modality in the centers, including magnetic resonance (MR), computed tomography (CT), ultrasound, X-ray and nuclear medicine.

Meanwhile, IBM is grooming Watson Health to help physicians make diagnoses. Intelligent machines may one day take the reins during the exam itself, optimizing scan protocols on the fly to home in on pathology. Tapping into steams of imaging data, Watson might look for signs of disease and adjust scan parameters to optimize data acquisition. 

But are smart machines what radiology needs? Are they even practical for use in the United States?

Intelligent machines will encounter a major hurdle at the U.S. Food and Drug Administration (FDA). As the first of their kind, these machines will lack the “predicate” devices needed to be regulated under the FDA’s 510(k) system. To get a feel for the enormity of this challenge, one needs to look only at how difficult it has been for companies making computer-aided detection algorithms.

There is an alternative, however, one that turns groups of human experts into super experts at diagnosis. The algorithm creates a form of artificial intelligence, called swarm AI, that helps radiologists form a consensus. The brainchild of Louis Rosenberg, Ph.D., and his company Unanimous AI, swarm AI has already proven effective in radiological applications.

In one study, published in the Public Library of Science,1 a collective intelligence of radiologists reduced false positives and false negatives when interpreting mammograms. This swarm AI overcame “one of the fundamental limitations to decision accuracy that individual radiologists face,” the authors concluded. The study demonstrated that this swarm intelligence could improve mammography screening and has the potential to improve many other types of medical decision-making, “including many areas of diagnostic imaging.”

In another study, a dozen radiologists increased their ability to diagnose skeletal abnormalities correctly. The researchers concluded at the ninth international conference on swarm intelligence in 2014 that the “algorithm’s accuracy in distinguishing normal versus abnormal patients was significantly higher than the radiologists’ mean accuracy.”

In developing the algorithm, Rosenberg borrowed a page from nature’s playbook wherein a species can accomplish more by participating in a flock, school, colony — or swarm — than they can individually. Unanimous AI offers the unique infrastructure, he says, by which people can form intelligent swarms.  

You can argue that the real world won’t allow the luxury of bringing groups of radiologists together to develop a consensus on every case. Rosenberg retorts that every imaging study will not require such a consensus. Routine cases, he says, could be interpreted by individual radiologists. When stumped by a complex case, radiologists could tag it for later study by the swarm. This might improve the accuracy of diagnoses, while empowering team members and streamlining the patient care process.

Regardless of whether machine- or human-based aids are leveraged, radiology needs such aids. Never has improving performance been so important to its future.

Read the 2017 article "How Artificial Intelligence Will Change Medical Imaging."

Watch the VIDEO “Examples of Artificial Intelligence in Medical Imaging Diagnostics.” 

Watch the VIDEO “Development of Artificial Intelligence to Aid Radiology,” an interview with Mark Michalski, M.D., director of the Center for Clinical Data Science at Massachusetts General Hospital, explaining the basis of artificial intelligence in radiology.

 

Reference:

Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist.” PLoS ONE 10(8): e0134269. doi:10.1371/journal.pone.0134269

Editor’s note: This column is the culmination of a series of four blogs by industry consultant Greg Freiherr on Machine Learning and IT systems. The blogs, “Diagnostic AI: By the People, of the People, for the People,” “Will the FDA Be Too Much for Intelligent Machines?,” “Smart Scanners: Will AI Take the Controls?” and “Will Smart Medical Machines Take Us to the Eve of Destruction?,” can be found here. Greg Freiherr has reported on developments in radiology since 1983. He runs the consulting service, The Freiherr Group. Read more of his views on his blog at www.itnonline.com. 

Related Content

#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2

Getty Images

Feature | Coronavirus (COVID-19) | April 03, 2020 | By Melinda Taschetta-Millane and Dave Fornell
In an effort to keep the imaging field updated on the latest information being released on coronavirus (COVID-19), th
Varian received FDA clearance for its Ethos therapy in February 2020. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Varian received FDA clearance for its Ethos therapy in February 2020, shown here displayed for the first time at ASTRO 2019. It is an adaptive intelligence solution that uses onboard AI in the treatment system to take the cone beam CT imaging on the system, compare it to the treatment plan and deliver an entire adaptive treatment plan in a typical 15-minute treatment time slot, from patient setup through treatment delivery.

Feature | Treatment Planning | April 03, 2020 | Dave Fornell, Editor
The traditional treatment planning process takes days to create an optimized radiation therapy delivery plan, but new
An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal.

An example of Philips’ TrueVue technology, which offers photo-realistic rendering and the ability to change the location of the lighting source on 3-D ultrasound images. In this example of two Amplazer transcatheter septal occluder devices in the heart, the operator demonstrating the product was able to push the lighting source behind the devices into the other chamber of the heart. This illuminated a hole that was still present that the occluders did not seal. Photo by Dave Fornell

Feature | Radiology Imaging | April 02, 2020 | By Katie Caron
A new year — and decade — offers the opportunity to reflect on the advancements and challenges of years gone by and p
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus

Getty Images

Feature | Coronavirus (COVID-19) | April 02, 2020 | Jilan Liu and HIMSS Greater China Team
Information technologies have played a pivotal role in China’s response to the novel coronavirus...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 the company is now offering a suite of AI solutions Vuno Med-LungQuant and Vuno Med-Chest X-ray for COVID-19, encompassing both lung X-ray and computed tomography (CT) modalities respectively all at once
News | Artificial Intelligence | April 02, 2020
April 2, 2020 — In the face of the COVID-19 pand
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 The Chinese start-up company Infervision launches its AI-based solution InferRead CT Lung Covid-19 also in Europe
News | Artificial Intelligence | March 31, 2020
March 31, 2020 — Lung infections generated by the coronavirus can be detected in...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 Doctor in our hospital is using this intelligent system for accurate diagnosis

Doctor in our hospital is using this intelligent system for accurate diagnosis. (Photo: Business Wire)

News | Artificial Intelligence | March 31, 2020
March 31, 2020 — The Intelligent Evalua...
#COVID19 #Coronavirus #2019nCoV #Wuhanvirus #SARScov2 behold.ai has developed the artificial intelligence-based red dot algorithm which can identify within 30 seconds abnormalities in chest X-rays. Wellbeing Software operates Cris, a widely used UK radiology Information System (RIS), which is installed in over 700 locations
News | Artificial Intelligence | March 31, 2020
March 31, 2020 — Two British companies at the leading edge of medical imaging technology are working together on a pl