Guest Post by Adrian Bowling - CEO and Founder of MoleMap

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Kiwis and Aussies love the outdoors, especially in the summer sunshine, but with that comes the highest rate of skin cancer in the world. With melanoma, the most life-threatening, early diagnosis is critical to improving survival rates. The work I’m doing with IBM Research to improve the identification of melanoma has the potential to help clinicians save the lives of over 3,500 people [1] each year across New Zealand and Australia.

Finding melanoma is like finding the needle in a huge haystack. We may have 10s or 100s of moles, freckles and other spots on our skin and it is most likely that none of them are melanoma. Once a melanoma reaches more than 1mm below the surface of the skin it becomes life threatening. The challenge is that these changes can be difficult to spot, as superficial melanomas generally grow slowly, spreading across the skin for months or even years before they present a serious threat.

If detected before it spreads beyond the original tumour the five year survival rate is 98.4%. Once it reaches a nearby lymph node the five year survival rate drops to 62.4% [2].

To save more lives our dermatologists need to see more people! Despite the widespread education campaigns to ‘slip, slop, and slap’, early detection has not been a public health priority. Not all primary care providers are sufficiently trained to undertake increased screening and there simply aren’t enough dermatologists to cover the whole country.

I estimate one million people throughout New Zealand and Australia should be screened regularly but we only see around 50,000 each year.

The critical work we are doing with IBM Research is designed to train artificial intelligence technology to identify signs of melanoma from its very early stages.  Rather than taking over the specialist’s role of identifying suspicious skin lesions, the purpose of an AI system would be to efficiently and effectively sift out the false alarms. Making the ‘haystack’ smaller will help our expert dermatologists to focus on, and spot, the dangerous lesions.

20130912 EC MoleMap 8093

The first step was to train the AI system to help specialists spot skin lesion changes. The AI technology learnt to recognise about three types of skin cancer and 12 benign disease groups from 40,000 images taken from MoleMap’s database of 15 million lesion images and compared that with our expert’s medical diagnosis.

Like a human, the machine’s accuracy with detecting melanoma improves with practice. My brief to IBM Research was to get to a level of accuracy of 80%, similar to what the average dermatologist (they are the experts) achieves. The results from the research so far show a level of accuracy of closer to 95%[3], which is really encouraging.

I’m really excited about the potential of what AI could help us do. I want this AI-enabled technology to reach the wider population, especially regional areas where people are more likely to spend their days working in the sun but are further from our specialists.

MoleMap already has mobile clinics that travel the country in an effort to reach as many people as possible. If AI-enabled technology can help our people work faster, we could catch more deadly melanomas before they do their damage.

Adrian Bowling: “I jointly established MoleMap in 1997 and today we have a very successful melanoma screening programme running across NZ, Australia and the USA. We have developed all of our own technology and can lay claim to having the largest store and forward tele-medicine business (outside radiology) in the world.”

[1] Ministry of Health  (2016) Cancer: New Registrations & Deaths, 2013.
https://www.health.govt.nz/system/files/documents/publications/cancer-new-registrations-deaths-2013-nov16.pdf

Australian Institute of Health and Welfare (2017) Cancer compendium: information and trends by cancer type.

https://www.aihw.gov.au/reports/cancer/cancer-compendium-information-and-trends-by-cancer-type/report-contents/melanoma-skin-cancer-in-australia

[2] https://skincancer.net/basics/prognosis-survival-rates/

[3] Z. Ge, R. Chakravorty, B. Bozorgtabar, A. Bowling, R. Garnavi, et al.”Exploiting Local and Generic Features for Accurate Skin Lesions Classification Using Clinical and Dermoscopy Imaging”, to appear in the proceeding of The 2017 IEEE International Symposium on Biomedical Imaging.


 

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