Deep Lens is an AI-driven digital pathology company based in Ohio. They’re currently working with Worldwide Clinical Trials to fast-track patient enrollment into clinical trials. This week, they announced a successful Series A financing round of $14 million.
“Since our inception, we’ve benefited from a tremendous group of investors, which now includes the world class team at Northpond Ventures,” said Deep Lens co-founder and CEO Dave Billiter in a press release statement. “This Series A financing is further validation of the value of our industry-changing approach to digital pathology in delivering the right cancer diagnoses faster and accelerating oncology trial recruitment and timelines.”
We wrote about Deep Lens last October after they exited stealth mode and obtained seed equity funding. We caught up again with Dave Billiter and Simon Arkell, co-founder and President, respectively, to gain insight into their funding and future goals towards a better digital pathology service.
Ben Ouyang, Medgadget: How did you find the investors for the Series A financing?
Deep Lens: The global oncology trials market is approaching $65 billion and Northpond Ventures had been looking at the market. Their analysts completed their review of the market and reached out to us. They realized we were the best vendor in the space because we have a business model that can give us critical mass with pathologists. We can leverage that footprint for its data and have access to patients that can be identified for clinical trials.
Medgadget: What will the money help you do?
Deep Lens: The financing will aid in us expanding our software development and data science teams, as well as expand marketing and sales headcount. We will be developing more models and methods based upon tumor types and different therapies. We will continue to add more features to the platform to support the workflow of pathologists, clinical research coordinators, principal investigators, and biopharma.
The AI methods we have already created have proven to surpass all published literature in terms of accuracy. We will develop more of those and embed them in VIPER so that they can be used to improve pathology uptake and drive further adoption of digital pathology. Without going into our proprietary methods in too much detail, what we can say is that VIPER will be used to identify and match patients to appropriate clinical trials much sooner than any current methods, thereby helping trial sponsors identify more patients and improve recruitment rates, and compress timelines for clinical trials.
Medgadget: Have there been any developments in the tech since we spoke last year?
Deep Lens: Absolutely. We have added more logic based on a tumor type and AI methods into the workflow of VIPER. We have developed AI that has proven to literally exceed any published literature for tumor identification models in breast cancer and lymphoma. We have also completed enhancements to the image viewer within VIPER.
Billiter: The screenshot is of the VIPER 3.0 version, which shows us embedding AI methods in the workflow of case reviews. The AI method identified in this image is of our Cell Counting algorithm where the annotations indicate the cell nuclei. The darker circles of what we have identified in our AI is the actual cell nuclei, and the light purple is the cytoplasm of the cell contained within the cell membrane. This is one of several methods that we are providing to pathologists in their user account of VIPER. The algorithms that we are integrating into the workflow of VIPER will be utilized for both solid and liquid tumors. The VIPER platform is a multi-purpose solution and is utilized by pathologists for establishing ground truth and then enables AI models and methods to be developed in an iterative fashion. All of this information is tracked in the VIPER database which enables a deeper understanding to produce our AI.
Medgadget: What advice do you have for our readers in getting a technology going?
Deep Lens: I would recommend establishing a continuous feedback loop from the end-users and to be extremely focused on the product market fit. We understand this process because VIPER was developed over a period of 10 years and was used by collaborating pathologists on dozens of global studies. We iterated on their feedback and provided a product that found product market fit.
Medgadget: What’s the biggest hurdle you’re facing right now?
Deep Lens: Hiring. We are rapidly expanding our team and finding the right individuals as fast as possible to support scaling our operation. We are looking across a number of industries for software developers, AI data scientists, AI developers, sales, marketing and business development personnel.
Related announcement: Deep Lens Closes $14 Million Series A Financing to Further Advance Digital AI Pathology Platform for Clinical Trial Recruitment
Link: Deep Lens…