By Maryam Sadeghi
The early detection of skin cancer is essential to the long-term health of patients. The survival rate is 98 per cent when skin cancer is diagnosed in its early stages, dropping to approximately 16 per cent with diagnosis at later stages. However, studies show that there is a severe shortage of dermatologists, reporting systems are reliant on paperwork, workflows are disorganized and patients are rarely educated on self-detection. In order to provide the highest-level quality of care and streamline services, professionals must be equipped with the latest smart technologies in skin imaging and analytics. Currently, artificial intelligence is making drastic advances, with the potential to match or supersede the diagnosis of a dermatologist. For example, content-based image retrieval empowers us to quickly and efficiently review images that are visually similar to a selected patient’s mole. Statistics can then be extracted to provide the likelihood of lesion malignancy, as well as the potential top diagnoses based on the results of the similar images. This is a valuable tool that supports medical practitioners in their practice by providing valuable data on previous patient cases. Another valuable smart technology is the use of tools such as Evolution Tracker. After submitting images of a lesion from various dates, the software will automatically rotate the images and play them in consecutive order. This allows physicians to organize the images for an individual patient, while supporting them in identifying any lesion changes over time.
New technologies also have potential in the area of education for researchers and medical students, as well as in predictive analytics for better understanding and identifying different skin conditions. In turn, this holds the ability to streamline consultations and thus save valuable time for both the experts and their patients, and save lives.
Maryam Sadeghi is the CEO and co-founder of MetaOptima Technology (metaoptima.com). She completed her PhD in Computing Science at Simon Fraser University in the Medical Image Analysis Laboratory.
