Interview with Alauddin Bhuiyan, CEO and Chief Scientist, iHealthScreen at NYC Health Tech
Can you discuss your background and how you became interested in preventing eye disease? Tell us the story of how this led you to iHealthScreen?
My Ph.D. focuses on modeling retinal vascular structure into an efficient data structure using color retinal imaging. This work provided me the opportunity to join at the Centre for Eye Research Australia (CERA) as a post-doctoral research scientist. CERA is one of the top eye research institutes where I worked on retinal image analysis and image-based disease diagnosis. In 2011, I joined at Australian E-Health Research Centre, Commonwealth Scientific and Industrial Research Organization (CSIRO) which is the number one research Institute in Australia and the inventor of WiFi Technology. At CSIRO, I worked as a research fellow and scientist and studied telemedicine-based tools for diagnosing age-related macular degeneration (AMD) and diabetic retinopathy (retinal diseases). From these positions, I observed that the current technologies focus on the diagnosis and treatment, which cannot regenerate the vision. Meaning, the person is legally blind and cannot be reversed. However, if we can diagnose the diseases at an early stage, we can prevent the incident of these diseases by regular treatment and lifestyle changes.
In 2014, I was awarded the Endeavour Fellowship (Australia Award) and joined as a visiting scholar at Harvard School of Medicine where I was working on 3D SD-OCT image-based biomarker for incident of glaucoma. Following this position, I joined the Department of Computer Science at New York University as an Assistant Professor in 2015 and continued my research on image-based retinal disease diagnosis. However, I was looking for opportunities to study and develop an early diagnosis tool for early screening and prediction of retinal and systemic diseases. With this mission, I started my company in May 2015 and received my first NIH SBIR grant in 2016 for early screening of age-related macular degeneration. Following my second NIH SBIR grant on stroke prediction in January 2017, I have decided to take a full-time position with iHealthScreen.
How would you describe your technology to the layperson? What is it? How does it work?
iHealthScreen (iHS) Inc. has developed iPredictTM, a telemedicine-based HIPAA compliant software product for automated screening and prediction of individuals at risk of developing retinal diseases such as late age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, and systemic diseases such as cardiovascular heart disease and stroke.
iPredictTM will use an individual’s retinal image and socio-demographic parameters to facilitate mass screening for these diseases through an artificial intelligence (AI) platform to prevent blindness, death, and disability. We have determined, through an NIH-sponsored market research study, that the primary care doctors, optometrists, and ophthalmologists are the potential customers of iPredictTM.
How long did it take to make the idea for iHealthScreen into reality? How is large scale screening of eye disease changing the field of healthcare?
I have been working on this idea since 2015. The automated AI-based software tool can make mass screening and early diagnosis of these diseases through primary care and other clinical settings. Early diagnosis will help prevent the onset of long-term diseases with the implementation of timely treatment and lifestyle changes. NIH research showed that 90% of the diabetic retinopathy and glaucoma (total 9M cases in the USA) cases are preventable by early treatment, and 20% of late AMD cases are preventable by only taking vitamin and mineral supplements and exercise regularly. NIH reports also showed that 4 out of 10 patients do not visit their ophthalmologists even though it is recommended by their primary care physicians. Therefore, iHealthScreen aims to take the screening tool to the patient by implementing it in primary care and other clinical settings. This will facilitate a highly effective mass screening and prevent these sight threatening diseases.
What has been the hardest part about transitioning from being a computer scientist into the business world?
I think the hardest part is to go out to the streets to discover our customers. Once you can determine who your customers are, the next step is how you can acquire them. In short, the transition from academic researcher to an entrepreneur. I think I was lucky to be part of the NIH I-Corp program which trained us extensively to determine the customer segment, eLAB NYC program which trained us to get acquainted with becoming a successful entrepreneur, J&J acceleration program which provides a good platform for the necessities for the entrepreneurs and Start Up NY program, which provides the 10 tax free business opportunities and other facilities. All this support has helped in leading iPredict that is now being used by two primary care clinics, and two ophthalmology centers under Mount Sinai Hospital for pilot studies.
Why NYC? Why do you think it’s becoming a leader in the health-tech sector?
We all know that NYC is the heart of the business world. We have access to investors and other successful entrepreneurs. Also, it is easy to travel around the world from NYC. Recently, NYC has also started a number of initiatives such as Start UP NY program, ESD acceleration program and many other tech initiatives along with the investor connections. Therefore, it is becoming a hub of health-tech world day by day.
What do you believe to be the greatest challenges for health-tech? And the greatest opportunities? How do you think technology can help?
I think the greatest challenge is to validate the idea/technology with the appropriate health data. It is also time-consuming to bring a product into the market, which is very difficult for startups to stay in the race. There are many complicated regulations related to health data that sometimes makes it complicated to access the data that is needed. However, we can make the data access regulation easier by using encryption or other deciphering technologies to make the data available – one example could be the deidentification of personal data by using digital signature. The good news is once you validated the product, you can help people and potentially a huge market around the world (which we believe we have).
How can health tech help marginalized communities? Do you see health-tech and public health working more closely in the future to solve some of the issues in disadvantaged communities?
I believe health tech can facilitate high precision and low-cost disease diagnosis and treatment. This will eventually help low-income communities with lowering healthcare costs and increase affordable healthcare to all. Yes, easy and precise regulated access to the public health systems will encourage more health tech companies for research and development, and come up with better and efficient solutions, and provide expert and automated diagnosis of various diseases when we do not have enough specialist doctors. This is the future of medicine: easy and affordable access for everyone and everywhere!
What is the best advice you have been given?
In 2009, at the MICCAI conference, Imperial College in London, a group of young scientists (~30) met with Peter Mansfield, the 2003 Nobel Laureate for inventing MRI technology. There he was telling his story in a closed-door meeting. He advised ‘If you can do/try things over and over, – you can be the best!’ This stuck with me because this was his approach to what he had been doing for many years.