• New evidence published this week by leading medical journal JAMA shows that our AI technology, DERM, is able to identify melanoma with a similar accuracy as skin cancer specialists.
  • In a study of over 1,500 lesions, DERMcould identify melanoma lesions, with over half in them in the earliest stage of malignancyNew data shows DERM, our AI skin cancer diagnosis tool, could be as accurate as clinical specialist

DERM, a CE marked medical device, was developed as a tool to aid clinicians in identifying 11 common skin lesion types, including melanoma.

The study, which is the first of its kind to explore the use of AI in dermatology,  evaluated the performance of DERM’s assessment  of over 1,500 skin lesions seen in skin cancer clinics at seven hospitals across the UK, led by the Royal Free London NHS Trust.

The prospective study recruited over 500 patients with at least one skin lesion that was determined, by a skin cancer specialist, to be sufficiently concerning that a biopsy was warranted. These lesions and known benign lesions were photographed by two smartphones and a digital camera, all with a special lens attachment. DERM analysed the images and produced a classification that was compared to the histopathologically-confirmed diagnosis. 

DERM, our AI platform, achieved an AUROC of between 91.8% and 95.8% with different cameras. When DERM was set to achieve 100% sensitivity (correctly identifying all the cases of melanoma), it had a specificity of 65%. Clinical experts identified all the melanoma lesions, and achieved an AUROC of 77.8% and a specificity of 69.9%.

The incidence of melanoma is increasing faster than any other form of cancer, and it is responsible for the majority of skin cancer deaths. Patients in whom melanoma is diagnosed at stage I have more than a 95% chance of survival compared with 8-25% with a stage IV diagnosis, highlighting the importance of early and accurate diagnosis. In this study, more than half of the melanoma diagnoses were either “in situ,” meaning stage 0, or less than 1 mm deep, emphasising the role that AI could play in the critical detection of thin or early-stage lesions.

The study was primarily aimed to understand the clinical accuracy of the algorithm. In demonstrating that artificial intelligence technology could play a role in identifying lesions with a high likelihood of melanoma, the study indicated that the development of low-cost screening methods, such as AI-based services, could transform patient diagnosis pathways, enabling greater efficiencies throughout health care services. .

Dr. Harpreet Sood, is an NHS GP and an advisor to global health and technology companies. He was previously Associate Chief Clinical Information Officer and Senior Fellow to the CEO of NHS England and commented:

“With an increasing number of skin-related consultations and referrals in the NHS, this technology provides a potential tool for clinicians to better manage more dermatology cases in the community, enable appropriate referrals and reduce variation in diagnosis and management of skin-related cases.”

Guy Boersma, Managing Director of Kent Surrey Sussex Academic Health Science Network and AI Lead for the national AHSN Network, also said:

“AI offers lots of promise to the health and care sector, with a clinical workforce struggling to keep up with demand from a growing and ageing population. Image recognition technology is particularly promising, offering ‘augmented insight’ and what Eric Topol calls ‘the gift of time’ to busy clinicians. Increasing the research evidence-base for AI generally, and image-recognition diagnostics in particular, is a national and international priority which will bring the potential benefits of AI technology to NHS patients faster.”

Skin Analytics Founder, Neil Daly commented:

“At Skin Analytics we’re extremely proud to have this pivotal study into the effectiveness and accuracy of DERM published in a prestigious publication. We hope this real-world evidence will go some way to demonstrating that, when used as a tool to support physicians, AI has the potential to improve access to assessments, reduce the burden of skin cancer on the NHS, and ultimately ensure that more people are diagnosed earlier and can access life-saving treatment sooner.”