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DermaSensor uses pulses of light, spectroscopy, and machine learning to assess lesions’ cellular and sub-cellular characteristics.
Physician identifies the lesion of concern on their patient
Physician gently touches the small device tip to the lesion, taking instant recordings at different locations of the lesion
Light reflects and scatters from particles in the cells of the skin tissue back to DermaSensor's spectral sensor
The proprietary DermaSensor algorithm will analyze the reflected photons and provides an immediate "High Risk" or "Low Risk" result
DermaSensor uses Elastic Scattering Spectroscopy (ESS), a process which evaluates how photons scatter when reflected off of different cellular structures. Malignant lesions have different cellular and sub-cellular structures than benign lesions, scattering light differently. Elastic Scattering Spectroscopy (ESS) has been validated in 50+ publications on clinical studies that demonstrate its ability to effectively distinguish malignant tissue from benign tissue, including in skin. In multiple studies, ESS has been shown to be comparable to histopathologic assessment in the analysis of cellular microscopic structure. 6 7 8
While Americans have much better access to Primary Care Physicians (PCPs), early diagnosis in primary care is hampered by limited consultation time and limited dermatology training. Physicians will be able to use DermaSensor to better assess skin lesions and determine whether patients need additional clinical care.
Patients wait 32.3 days, on average, for an appointment with a Dermatologist. The total treatment costs for melanoma in situ is ~$4,648 while Stage IV melanoma is ~$159,808. DermaSensor will allow dermatologists and their care team to more quickly and effectively evaluate lesions for cancer. Detecting cancer at an earlier stage can save lives and save the system and the patient money.
Retail clinic use has doubled in the last 5 years. Patients turn to retail clinics due to convenience, transparency of cost, and efficient service. DermaSensor will allow patients to visit retail clinics with their skin lesion concerns and allow retail clinicians to effectively evaluate lesions.
An adjunctive tool, also known as a clinical decision support tool, is not meant to replace clinicians’ decision making. DermaSensor will provide clinicians with additional information about a lesion for them to consider in their clinical decision. An adjunctive tool is not a diagnostic so will not provide information on the exact type of potential skin cancer (e.g. melanoma), nor does it make any decision for the clinician.
DermaSensor will use pulses of light, spectroscopy, and machine learning to assess lesions’ cellular and sub-cellular characteristics.
DermaSensor will quickly assess information below the skin surface, giving physicians greater confidence and performance in their decision-making.
DermaSensor could improve early detection of skin cancer and reduce the number of unnecessary biopsies and referrals.
Patients will view DermaSensor as a supplemental and valuable addition to the skin care they already receive from physicians.
The noninvasive nature of DermaSensor will be unintimidating to patients and reassure them that they are receiving high-quality care.
The machine-learning algorithm is derived from thousands of samples of pathologically-verified lesions and is professionally trained by expert data scientists.