Tech has made incredible medical advances in recent years, not because of fancy medical practices or new surgical methods, rather, because of better analysis. Being able to analyze on larger scales and volumes of data with new metrics has led to detecting problems more accurately, and thankfully more and more healthtech companies are joining the cause. One such company VoxelCloud is a leading company providing automated medical image analysis services and diagnosis assistance all through cloud-based platform that leveraged AI and Deep Learning. Serving hospitals and gaining more and more data to help medicine, Voxel Cloud currently covers lung cancer, retinal diseases, coronary heart disease and plans to expand.
LA TechWatch chatted with Dr. Xiaowei Ding about the Startup and discussed their most recent round of funding.
Who were your investors and how much did you raise?
We are pleased to announce that we have raised $8M in series A financing led by Sequoia Capital in May 2017. The funding that we raised in this round, on top of the $5.5M angel investment we received in September 2016, will be used to expand our programs to cover more clinical areas, establish further collaboration with leading medical institutions in China and in the US, pursue regulatory filings, and marketing. We are also rapidly expanding our team.
Tell us about your product or service.
In brief, we provide automated medical image analysis services and clinical decision support services for clinical practitioners. Our premium algorithms offer more accurate, efficient, and accessible personalized medical image analysis.
Specifically our service will include at least three dimensions. First, we provide service directly to healthcare providers (such as hospitals, medical centers, etc.) through clinical workflow integrated cloud-computing solutions. Second, we partner with existing hardware and software vendors to provide AI services. Third, we plan to build a platform to enable third party medical developers to develop their own applications much easier through our medical knowledge graph API. With this API, developers can work more efficiently because we have developed the basic algorithms and collected massive amount of anonymized training data. Our medical image knowledge graph will be offered though this API. Further extension of our business will leverage heterogeneous data sources in synergy with the existing imaging data.
Currently, our program covers lung cancer, retinal diseases, and coronary heart disease.
What inspired you to start the company?
We started VoxelCloud in 2015 in Los Angles. Actually, VoxelCloud is an extension of my research program, which I had started during my PhD at UCLA.
I have been passionate about medical industry since I was a child because both my grandmother and grandfather were doctors in a leading hospital in China. So after I finished my bachelor degree in computer science, I decided to apply my computer science background to the medical industry. During my PhD research in the computer science lab at UCLA, I explored the possibilities that medicine could benefit from the integrating computer sciences. Later on, I worked at Cedars-Sinai Medical Center as a research assistant, and at that time I finally found the way to make a difference in the industry. Since then my long-term research on artificial intelligence in medicine has evolved into this startup.
In my research, I found that before 2013, AI in medicine research programs has been limited by the amount of data, and there were no good machine-learning algorithms to apply that clinical data. Then I thought I could contribute to the industry with better models and better algorithms. Through deep learning theories, VoxelCloud could improve the efficiency of medical diagnosis for medical practitioners, because the machines could help with analyzing medical images if you teach them how.
After one-year of investigation and survey, I cofounded the company the startup aimed at providing medical images analysis and diagnosis assistance for clinical practices. With artificial intelligence and cloud computing technologies, we could teach machines to understand medical data and boost a doctor’s efficiency and diagnostic confidence.
How is it different?
For an AI company, collecting qualified data is the most important thing. We have the access to vast amount of structured, labeled medical data through our collaboration with leading hospitals, which remains a considerable bottleneck in the industry for developing more accurate algorithms.
Second, our team has strong research background in AI and medicine, and they are experts in the industry. For example, our chief scientist, Demetri Terzopoulos is Chancellor’s Professor of Computer Science at UCLA, and he holds the rank of Distinguished Professor and directs the UCLA Computer Graphics & Vision Laboratory. He is also the fellow of top organizations including ACM and IEEE. Our vice president of R&D, Jiaming Liang is an associate Professor of Biomedical Informatics and Computer Science at Arizona State University. I am a research assistant professor in the UCLA Computer Science Department. 80% of our team are technology workers. We know our stuff.
Third, we work closely with clinical practitioners and leading institutes such as ASU, UCLA, NIH, etc. Even earlier in the product development process, clinical practitioners have been involved to guide our development. The collaboration makes sure our service can really solve clinical problems and satisfy clinical needs.
Our company is a balanced combination of research and commercial applications, which gives us a unique advantage in the market. I believe with our strong academic research background and large clinical data, we could understand the industry better and solve clinical problems that are necessary.
What market you are targeting and how big is it?
We are targeting the global AI market in medicine. According to Mordor Technologies, the global AI in medicine market was valued at $1.123 billion in 2016 and is expected to reach a market value of $9.10 billion by the year 2022. This is definitely a rapid growing market and we are excited to be part of it.
What’s your business model?
First, we provide service directly to healthcare providers (such as hospitals, medical centers, etc.) through clinical workflow integrated cloud-computing solutions.
Second, we partner with existing hardware and software vendors to provide AI service.
Third, we build a platform to enable third party medical developers to develop their own applications much easier through our medical knowledge graph API. With this API, developers could work more efficiently because we have developed the basic algorithms and collected massive amount of anonymized training data from scratch. Our medical image knowledge graph will be offered though this API.
Further extension of our project will leverage heterogeneous data sources in synergy with the existing imaging data.
How will tech affect how the medical profession is perceived?
I believe that the medical diagnosis process should be accurate and objective. With the development of AI, the medical profession could provide more accurate, efficient and accessible medical diagnosis under the assistance of machines. We strive to develop the technologies that enable a doctor to carry his mission with more confidence, more focus and a wider reach.
What was the funding process like?
The funding process was very smooth. We received several letters of intent and we chose Sequoia Capital, because as a credible venture capital firm, Sequoia Capital has invested in several AI startups and we believe Sequoia Capital has the ability to not only fund us but also provide us guidance and recourse that are needed to help us succeed.
What are the biggest challenges that you faced while raising capital?
Actually, we didn’t face any challenges while raising capital. We are in a hot industry that has attracted a large amount of venture capital, and most investors believe the potential of AI in medicine in the future. We have a strong team and solid business model, so instead of finding random investors, we were just waiting for the perfect match.
What factors about your business led your investors to write the check?
I think there are many reasons. First of all, our investor is very optimistic about the potential of artificial intelligence in medicine, and we are heading to the right direction.
Second, we are one of the leaders in this field in both China and US. Our team has strong research capabilities and we are a research-oriented startup that has developed stably over the last year.
Third, we also work closely with clinical practitioners to ensure our products satisfy clinical needs. Actually, we have already have paying costumers. I think investors can tell that we have the ability to convert academic research into commercial success.
What are the milestones you plan to achieve in the next six months?
We plan to seek regulatory approval and clinical trial in the next six months. Those are our next milestones to accomplish.
What advice can you offer companies in Los Angeles that do not have a fresh injection of capital in the bank?
So my advice is that, first, trust yourself. Because the investment climate in Los Angeles is very good and there are many aggressive investors, you have a relatively better change to be funded here in LA.
Second, focus on solving problems and choose the right direction. I always believe that a Startup should focus on solving a problem instead of looking for capital. From my experience, if a Startup is founded because the founders want to solve a real problem in the world and probably they could, the startup will have a higher possibility to get funding and success.
Where do you see the company going now over the near term?
We hope to produce our products in a large scale and our brand will be recognized in the medicine industry over the near term.
Where is the best place in LA to watch the sunset?
Our office happen to be the best place for sunset.
