As two leading entrepreneurs and investors in health technology companies, Dr. Maurice Ferre (Co-founder and Chairman) and Christopher Dewey (Co-founder) came to believe that skin cancer is primarily a public health issue that could be addressed by improving access to quality skin cancer care. Due to the limited number of dermatologists, their goal was to use cutting-edge spectroscopy and machine learning technologies to improve frontline providers ability to detect skin cancer early.
They founded DermaSensor in 2009 following discussions with Dr. Irving Bigio, Professor of Biomedical Engineering at Boston University and one of the top spectroscopy researchers in the world. Dr. Bigio is the original inventor and patent holder for Elastic Scattering Spectroscopy (ESS), a key technology employed by the DermaSensor device.
Since Professor Bigio's invention of ESS in 1994, there have been several hundreds of publications on ESS and over 30 publications on the use of ESS in clinical studies. DermaSensor began its own skin-specific studies using this technology in 2011 and began its first study on the current commercial device in 2018.
DermaSensor Development History
Strong Results from Five Recent Clinical Study Readouts
Building a Groundbreaking
DermaSensor was founded in 2009 to improve patient access to effective skin cancer assessments. By harnessing the power of elastic scattering spectroscopy (ESS) we created the world's first skin tissue sampling system that uses hundreds of different wavelengths of light to painlessly and non-invasively scan skin lesions. We use the light to detect properties consistent with malignancy at a cellular and subcellular level. It's like an ultrasound, but using light instead of sound.
When it was first developed, ESS technology was the size of a microwave oven. Today, thanks to a decade of our team's engineering advancement, you can hold it in the palm of your hand.
Power of AI
Even with cutting-edge ESS technology, there was no way to assess lesions objectively and efficiently. That's why we developed an algorithm using machine learning. To build it, we collected thousands of dermatopathology-confirmed malignant and benign tissue samples. Then we compared the spectral data to the pathology report, testing and fine-tuning the algorithm in a process that continues today, as we constantly indentify and analyse new data, providing ongoing updates to our DermaSensor users.
Today, thanks to our proprietary DermaSensor algorithm, our device processes spectral data in a matter of seconds, rather than waiting days for a physician or labratory to return results.
1Benvenuto-Andrade C, Manolakos D, Cognetta AB. Safety and Effectiveness of Elastic Scattering Spectroscopy and Machine Learning in the Evaluation of Skin Lesions. Poster Presentation, World Congress of Teledermatology, Dec 5-7th 2021.
2Algorithm v3.0 results have not yet been published for this study, Data on file, DermaSensor Inc.
3Tepedino K, Tablada A, Barnes E, Da Silva, T. Clinical Utility of a Handheld Elastic Scattering Spectroscopy Tool and Machine Learning on the Diagnosis and Management of Skin Cancer by Primary Care Physicians. Poster Presentation, SDPA Fall Conference, Nov 4-7, 2021.
4Melanoma sample size includes highly atypical melanocytic nevi
5PPV for spectral score group 8-10.
6DA III- Hartman, R. Tepedino, K., Fung, MA., McNiff, JM., Grant-Kels, J. Clinical Validation of a Handheld Elastic Scattering Spectroscopic Device in the Evaluation of Lesions Suggestive of Melanoma, Presentation at the American Academy of Dermatologists Annual Meeting, Mar 24-28th, 2022.