Artificial Intelligence in Teleradiology

Dr Arjun Kalyanpur

Artificial Intelligence in Teleradiology

A Report, based on the Keynote Speech delivered
– By –

Dr. Arjun Kalyanpur, Chief Radiologist and CEO, Teleradiology Solutions
at Artificial Intelligence in Radiology 2018 Symposium, November 10, 2018
Organized by Telerad Tech and Image Core Lab

Dr Arjun Kalyanpur began his speech recalling how Teleradiology Solutions for the past few years has been engaged in the field of artificial intelligence (AI) with the health-IT company Telerad Tech and drug trial company Image Core Lab. He also¬ congratulated Telerad Tech for launching MammoAssist – its AI software for breast cancer detection and also for establishing a new dedicated AI lab facility at the same time.

Dr. Kalyanpur noted that his company Teleradiology Solutions had reached an inflection point in its journey in the direction of artificial intelligence and it was an opportune moment for their company to be part of an event like this and be amongst the distinguished speakers in the field of AI.

AI and Teleradiology – synergistic and disruptive technologies

As per Dr. Kalyanpur, both AI and Teleradiology are very closely linked to each other and are extremely synergistic. They both address the same fundamental clinical issues about shortages of radiologists and both provide technology-enabled solutions for the same clinical issues.

Revisiting their journey of how teleradiology brought them to the current point where AI is seen as the direction forward, Dr. Kalyanpur said that the drivers that set them on this path 15 /16 years ago was the shortages of radiologists with most of them being focused in metros, and emergency radiology being the most severely affected area and still continues being one today.

He noted, that the numbers of radiologists haven’t changed much in the past 20 years and that there was still a dramatic shortage of radiologists the world over. Drawing up a scenario, Dr. Kalyanpur highlighted that even a developed country like the US with the best radiologist to population ratio, considered itself of being in the deficit with India being in the middle of the curve somewhere, whereas Tanzania on the other end had 1 radiologist for over 1 million population.

AI is important for addressing the increasing burden of diseases

He cautioned that, there is an increase in chronic diseases, due to progressively unhealthy lifestyles and living conditions, all of which required repeated imaging at multiple times in a patient’s life, leading to an increase in the number of images and their complexity in studying them. He added that in the 123 years, between the first X-Ray being produced in Roentgen’s Lab, till today, advancements have led to a dizzying array of imaging modalities of extremely high resolution, in all shades and hues being available to the radiologists, increasing work for them analyzing the data.

Understanding the gaps, Dr. Kalyanpur noted that, 15 years ago teleradiology evolved as a solution to these factors delivering the value proposition of not needing to have a radiologist at every hospital. It was technology driven solution with work flow and efficiency being at the core. It also had a quality benefit because the images were being brought to the most qualified radiologist, spawning the term “nighthawk”. The company was serendipitously at the forefront in the sector, providing night shift emergency services to hospitals at different parts of the world resulting in better performance and better quality services.

Emphasizing again, Dr. Kalyanpur said that, both teleradiology and AI were essentially disruptive technologies. Having been through and seen the evolution of teleradiology, he felt that, AI was seeing the same disruption as they had witnessed in teleradiology. Citing similarities between the two, he noted, that the resistance pattern was similar, and AI also faced the same concerns and fears that teleradiology had previously faced. Depending on how one would see them, both could either be seen as a threat or as a benefit. The same drivers were present for both innovations. Since both are IT enabled solutions and follow Moore’s law principles, one could only expect them to be faster, more effective and cheaper in the coming years. He also noted that both had the potential to transform healthcare by simultaneously increasing radiologist quality and productivity – a double whammy in terms of their benefit and impact on healthcare.

Key trends in radiology

Describing radiology as a quantitative science, Dr. Kalyanpur said that radiology today was beyond a simple image description. Studying a few trends in radiology prevailing today, Dr. Kalyanpur chose to highlight 3 of them, sub-specialization being one. Further, he noted that there was an increase in focus on report turn-around time as all of medicine was becoming metrics- driven. Turnaround time was becoming the basis of judging the performance of a radiologist or a radiology group. He also pointed out that the evolution of cloud-based workflows also had made a tremendous impact in the field.

Citing stroke as an example, Dr. Arjun said that, in the 15 years that they had been practicing teleradiology the specialty had evolved to the point where it was no longer just a matter of looking at the CAT scan of the brain and determining whether it was a stroke or not. Today they had a CT angiogram or a perfusion CT to analyze as well, increasing the number of imaging modalities available to assist in clinical decision making. Quantitative imaging today helped in quantifying the size of the stroke, determine the collateralization of the brain and the impact on patient outcome. Therefore, radiology was no longer just a diagnostic science but had evolved into being a prognostic tool while Quantitative imaging was available in every sphere – Whether it was tumor detection, tumor analysis or quantification of vascular disease.

Looking at it from the operational standpoint, Dr. Kalyanpur noted that turnaround time was another factor that entities needed to be conscious of. He mentioned that, every tele-radiologist in the organisation practiced medicine under the scrutiny of the stopwatch. Citing it as a challenge, he said that, finding a fine balance between quality and speed was important as the faster a radiologist reads an image, the greater was the chance of him/her missing a finding. As per him, teleradiology had made the important aspect of work flow possible by allowing decentralized reporting and being a cloud-based solution, a radiologist could read for hospitals in any part of the world, with the understanding that compliance with licensure and certification was adhered too.

Making a radiologist more efficient and productive

Taking a step back and speaking on his role as a radiologist, Dr. Kalyanpur said that, usually a radiologist detected and quantified the lesion, analyzed the finding, communicated and recommended the follow up. Tackling the criteria on making the radiologist more efficient and productive, he suggested making the process of reporting, the work flow smooth by providing all the relevant information to the radiologist and assisting them by creating a templated report. Speaking on the productivity, Dr. Kalyanpur said that, the average productivity of a radiologist in the world was between 6-10 RVUs (relative value unit) an hour. That could probably be increased by about 15-20% by using these techniques. However, AI could potentially double the productivity of a radiologist, thereby helping in solve the radiologist shortage issue of half the world.

Highlighting the larger issue of radiologist errors, Dr. Kalyanpur mentioned that as per some studies, 3-5% of radiologist reports contain errors, such as for example small bleeds in the brain, small clots in the lungs or very subtle fractures in the spine. Considering that a billion scans were performed around the world every year, it meant that around 30 million scans would have reporting errors – a gravely concerning huge number. He opined that radiologist accuracy and patient outcome could be improved by allowing AI to detect these findings.

Predicting on the estimates on the size of the industry, Dr. Kalyanpur said that, the initial figures in teleradiology suggested it being a billion dollar industry, however it had grown beyond 4 billion currently and he was confident that it would further grow. Similarly, he was optimistic about AI growing beyond the current 2 billion in the future and wished best of luck to Telerad Tech for its endeavours.

Developing AI in radiology

Speaking on the importance of developing AI in radiology, Dr. Kalyanpur said that, it was important to have communication between radiologists, scientists and engineers. This would enable data with labelled information help in developing the tools and would require radiologists to validate them once they were developed to confirm that they actually worked. He suggested that this could be integrated into the workflow most effectively by integrating it into the teleradiology workflow which allowed these technologies to be deployed at scale across the globe, where the entire world population of radiologists could be the consumer.

The synergies between teleradiology and AI was that at every stage of the AI cycle teleradiology had a valuable role to play and offer – whether it was data acquisition from around the world (as many as over 200 hospitals in 20 countries) forming a huge repository for analysis, testing an algorithm in a teleradiology environment and the teleradiology workflow to be used as a distribution tool for AI software.

Simplifying it, Dr. Kalyanpur noted that, teleradiology could be seen as being a shopping mall, in which each AI algorithm was an individual store or teleradiology could be an I-phone on which each individual app resided and allowed the consumer to connect with the product. He expressed his satisfaction to note that Telerad Tech, apart from developing its own software algorithm, was also partnering with other entities in the same space. And allowed their workflow to be used as a distribution module for teleradiology algorithms or for AI algorithms to radiologists around the world allowing all possibilities of detection, quantification triage for smaller lesions and detection for the larger lesions.

In his concluding remarks, Dr. Kalyanpur said that, it was all about perspective and focus. Quoting radicals such as Professor Hinton, who opined that since AI would have taken over in the future, training of radiologists should be stopped because we wouldn’t need them anymore on one hand to moderates such as Professor Langlot, Stanford, on the other who believed that radiologists who used AI will replace radiologists who wouldn’t in the future. He suggested that it would be wiser to have a pragmatic approach since it was an evolutionary area. He was of the view that one needs to learn to adapt and understand the space so that one could utilize it better. Citing Darwin he wrapped up his speech by saying that only the most responsive to change would survive over the more stronger or the more intelligent.