The responses have been given by Suthirth Vaidya, Co-Founder at Predible Health
All around the world, people are facing unprecedented challenges and uncertainties as a result of the COVID-19 pandemic. As InnovatioCuris (IC), we are always on a lookout for healthcare innovations that are affordable and provide quality care. In the wake of this need of the hour, InnoHEALTH magazine scouted and interviewed some innovative startups to build an army of health transformers to mobilize and address this global health crisis.
Kritika Arora and Varsha Prasad interviewed Suthirth Vaidya, Co-Founder at Predible Health on behalf of InnoHEALTH magazine.
By interviewing Oncostem Diagnostics, we aim to understand and review the role of AI as a decisive technology to analyze, prepare us for prevention, and fight with COVID-19 (Coronavirus) and other such pandemics.
Reasons behind the company name, any story behind it?
Predible comes from predictions that are credible. It is basically a shortened form of credible predictions through the use of Artificial Intelligence. Healthcare is all about credibility, credibility through which a doctor is looked up as a doctor. So, fundamentally to core guidance of principles about what predictions should be, one key adjective should be credible to describe it. Predible health is essentially credible-predictions.
What made you start the company? Tell us briefly about your journey?
Mr Suthirth Vaidya shared with us that he along with Mr Abhijith Chundru started the company right from IIT Madras in 2016. In 2015, both worked on research projects that they built without deep learning for neuroimaging, and this was basically from 2014-15. Very early on, they discovered methods to use deep learning for 3D imaging. He also shared that they won the Research Challenge Award organized by John Hopkins University in New York. This was for the detection of a particular condition called Multiple Sclerosis on brain Magnetic Resonance Imaging (MRI) and was also covered in the media. From that point, 2014-15, they realized they have this great technology that works for 3D MRI images, but there are so many more problems in the radiology field where this technology is applicable. Given that large amount of data is the crucial ingredient for really powerful algorithms,they figured India would be at an advantage. Basically, the point that they figured out the technology at the right time, as well as the fact that there is a lot of data in India, lead them to start the company.
How is your company using AI? Tell us about the specialisations where it is being used and problems that you are solving?
The application of AI in the field of radiology is mainly the core competence and hence dealing primarily with the analysis of images like X-Rays, CT-scans, and MRI. The current flagship product called LungIQ is basically the analysis of Lung CT images like 3D lung images. The Algorithms can detect multiple conditions, such as early cancer detection, COPD quantification, and detection of interstitial lung diseases. And the way the organization approaches products is really helping pulmonologists deliver better care and how they need to add in the workflow so that radiologists can deliver the analysis in their everyday life. Therefore, not only that AI is offering the knowledge that can benefit patients, but that we can all communicate better and use this information in everyday practice, both for radiologists and the specialist.
Explain us a typical day in office, how does an AI expert spend their day? Tell us some under the hood details?
As you already know, building and developing good AI and designing AI Algorithms is 20% of the work and about 80% of the work is on Data and Data Curation. And on a typical day, AI specialist job is going to be more like the data scientist looking at data and finding a few different types of cases like which algorithms don’t perform well and trying to find some of those data, getting it back into training and trying to move the algorithm to higher and higher accuracy levels. Right now, the organisation has COVID-19 Algorithms and every day they see more and more different types of cases of COVID-19 patients that they are trying to figure out and rigorously test and see if the algorithms are robust to all kinds of patients we see. Mr Vaidya shared that as soon as they see the case where they struggle, they seek to go to find more similar cases, and write to the radiologists with whom they work, and understand the data and problems. It’s always about finding gaps and sampling data better so that the algorithm works better.
Tell us the challenges you have faced and are facing in development/implementation of AI?
The difficulty in implementing AI is that it’s such a new area, and the moment you go to a hospital or a doctor and use those two terms, the kind of response you get is right from enthusiasm to interest to scepticism. “And, basically, we ‘re trying not to think too much about the fact that we’re using AI, we ‘re not going to make it appear that we’re using AI under the hood in any discussion until it’s practically asked. The picture of the product is the value that we are offering. Much of our conversations with doctors are about what the product really provides in terms of quantification, precision, and how it fits with the workflow and things like that” says Mr. Suthirth. Integrating these things into enterprise-wide systems was initially a kind of challenge, ensuring that the tools built to fit into the clinical workflow without having to jump to too many screens. They have had the initial challenge of providing access to a vast volume of data from hospitals addressing data privacy concerns, data protection issues and, in particular, India does not have very specific rules or legislation on data privacy and data sharing. Mr. Suthirth also said that they ensure they have committee approvals and institutional board approvals, and all of this before they can have access to massive hospital data.
How can you overcome some of these challenges? Do you want to share any instances where in the past your team was able to overcome any challenges?
In terms of challenges, where the company started 4 years ago, so many open source databases are readily available today. If you want to build a chest X-Ray Algorithm or an instance now you have literally hundreds of thousands to three hundred thousand chest X-Rays available to start training more instantly. So, the obstacles to starting something and joining the market have finally gone down. But, if you want to get the data from big hospitals, you also have to go through the process, so you need a clinical champion to work with you and get professional approvals from the management team. Yeah, sadly, there’s no easy way out other than being able to use existing open-source data to get started, but what’s more, it is still all about the customer values that you offer and ensuring from day one that you have seamless integration into the workflow. It’s simply a matter of talking to hospitals and doctors before you go out and develop the product, says Mr. Suthirth.
How reliable are these AI tools from a clinical perspective, tell us about the regulatory approvals, which stage are you in currently?
From a clinical point of view, it depends on how you actually position the product. For example, any of the reports bypassing the signature of a radiologist is never recommended. So, the radiologist has to control all the results that are generated and sign on the reports before approval or they go out in the hands of patients or specialists. It is therefore very important to keep radiologists in control of all reports and not to bypass how the product is delivered to the hospital. It’s a critical point on how to design and implement validation tests for these Algorithms as well because what you really have to aim for is that AI really increases the doctor and makes him quicker and doesn’t require him to spend more time or lead to unnecessary biases. To that, the organization has done multi-reader studies where they compare the studies and give them to radiologists to read with AI assistance. He also shared that they are working with the regulatory bodies to obtain a CE-mark for the products.
Share the customer benefit with your solution, and the role AI will play?
Taking the current example; the start-up is working in the COVID-19 scenario, and what they are doing in this COVID-19 is if you look at the CT scan and send it to them, they ‘re going to measure the severity of the disease in the lungs. So now, if you do the RT-PCR check, you’ve been told if you have COVID-19 (yes or no) but you don’t really know how much your lung is compromised and what extent and level you are until you do the radiology clinical examination. However, if you feed it to predible’s solution, it will read the lungs and give you a percentage of how much your lung is affected. It will help the doctor track more overnight how the patient’s condition progresses and whether it deteriorates and determine whether or not he needs to be admitted to I.C.U. or can be discharged. So, it offers a percentage, a more objective way to track the success of the study. Examples can be replaced by many chronic respiratory conditions such as idiopathic pulmonary fibrosis, COPD, the solution can study lung overtime patterns such as how these chronic patients perform, quantify how their lung functions over these windows, and how much better they get.
Are there any general issues associated with AI products and services?
To this, he said, as such there are no general problems. Everyone’s doing AI these days, and there are a lot of positive and better solutions. Some hospitals and clinics may not know how to pick the best solutions. Having a lot of players helps the industry move forward, and the people who are trained, know what to look for. Apart from market education, clinicians and doctors need to understand how AI works and how AI needs to get into the workflowOf course it will take some time for early doctors to use it and speak about it after sharing their experiences, sharing it with communities, and slowly trickling to more sites. That is the process of market acceptance, so all just have to wait for it as time passes.
What differentiates you from your competitors?
Working in the lung space and pulmonary space; actual competition is not really the other radiology AI provider. Presently, we are not seeking the fight/competition with others. The market adoption of AI, in general, itself is so minuscule today that it is too early to be in a competition, says Mr. Suthirth.
Where do you see your organisation in 10 years? Any brief message for our readers?
Saying “Ten years is a long time, and the clinical operating system is where we want to go” Mr. Suthirth continued that they ‘re looking at one specialty lung pulmonology, and they’re looking to help them better assimilate information on radiology by using AI over time, which they want to expand into many specialties, and want to be able to use many more data sources beyond radiology. He also said that “We want to build the suite of digital products that could be enterprise and mobile-first, that doctors can consume tomorrow. We want to create medical products that make up the hospital’s footprint, help them simplify the clinical process, provide quality treatment – both evidence-based and personalized patient care. The data elements are how we are able to use all the data and how we are able to gather evidence-based data to give each person better treatment. This is the perfect structure that we want to create.”
We hope our readers get a great insight about the current trends and future of AI and how Predible Health is working during this COVID-19 crisis.
Interviewed by Kritika Arora and Varsha Prasad