New App known as Dermofit, developed by the University of Edinburgh, United Kingdom is helping healthcare professionals learn to analyse and identify skin cancer at an early stage and decrease needless specialist referrals.
Dermofit, has been developed to assist train non-specialist physicians, nurses, and medical students to better recognize various skin lesions and growths along with their relevant diagnoses, using interactive, cognitive training methods and a comprehensive image reference library.
This new app launched in June 2016, Dermofit is currently in use in a variety of medical training settings all around the globe, assisting healthcare professionals build a greater understanding of the variety of visual characteristics that distinct skin lesions have, and the way that these can be utilized to identify benign cases from more serious examples of skin cancer.
Dermofit was 1st developed by Prof. Jonathan Rees, grant chair of dermatology at the University of Edinburgh, who came out with the idea to build a digital resource to assist medical professionals accurately determine malignant and harmless skin lesion and skin growths at an early phase.
The outcome of 4 years of R&D by the University of Edinburgh, Dermofit offers trainee doctors, nurses, and other non-specialist healthcare professionals with digital resources that enable them to develop their capability to properly identify particular skin lesion types and, as an outcome, enhance the accuracy with which they can figure out skin cancer diagnoses.
Algorithms group skin lesion photos depending on color and texture
In the case of suspected skin cancers – which include malignant melanoma, squamous cell or basal cell carcinoma – the need for prompt referral to a specialist for evaluation and treatment is necessary. However, in most cases, these referrals are usually needless.
“30 % of doctors will automatically send a sufferer to a healthcare facility if they have signs of a skin growth,” says Prof. Rees. “But the proof is that the great majority of individuals who are seen and referred do not have skin cancer or anything serious at all.
Resources that can provide non-specialist care practitioners with the abilities essential to more accurately recognize these various types of skin growth and lesion can therefore be incredibly valuable, in terms of enhancing the quality of care offered to sufferers and also decreasing costs for care providers.
Dermofit utilizes algorithms that automatically categories library photos of skin lesions depending on their color and texture properties.
Selecting from a library of over 1,300 images, the Dermofit app will take the user to additional sets of similar lesion kinds to demonstrate the distinction in lesions that may look identical but are from distinct skin lesion classes. Other modules enable users to further build and test their skills of recognition and diagnosis.
Bob Fisher, who is an expert in computer vision and assisted design the computer algorithms for the app, adds: “Dermofit comprises of a photo collection of skin lesions to assist inform experts to diagnosis more efficiently.”
Practitioners can simply click on the image of a lesion of interest which then results in further similar lesions. As lesions are chosen, additional sets of identical lesions are shown. This gives understanding with the various skin lesion kinds and allows users to distinguish between lesions that look similar, but that are from various skin lesion classes,” he says.
A quickly developing area of medical training
The use of cognitive teaching tools is a quickly developing area of medical training, as it enables healthcare specialists to build the essential skills that are needed to more perfectly diagnose and treat sufferers within risk-free digital environments.
Offering healthcare professionals with training tools like Dermofit assists them obtain skills that would otherwise need years of practical experience.