MedicalResearch.com Interview with:
Philipp Tschandl, MD PhD, Priv.Doz.
Department of Dermatology
Medical University of Vienna
MedicalResearch.com: What is the background for this study? What are the main findings?
Response: Dermatoscopy is a non-invasive imaging technique, where the surface of the skin is rendered translucent and additional important morphologic features become visible from deeper layers. This is achieved through use of immersion fluid or cross-polarised light – equivalent to the effect when using a pair of goggles to look underwater, or polarised sunglasses to reduce glare on glass surfaces. After the first description of “Dermatoskopie” almost 100 years ago by a German dermatologist (Johann Saphier), this technique has evolved to a successful, low-cost, state-of-the-art technique for clinical skin cancer detection in the last decades.
Convolutional neural networks (“CNN”) are powerful machine learning methods, and frequently applied to medical image data in the recent scientific literature. They are highly accurate for basic image classification tasks in experimental settings, and found to be as good as dermatologists in melanoma recognition on clinical or dermatoscopic images. In this study we trained a CNN to diagnose non-pigmented skin lesions (where melanomas are only a minority) through analysis of digital images, and compared the accuracy to >90 human readers including 62 board-certified dermatologists. This study expands knowledge in the following ways compared to previous work:
– We applied the network for the detection of non-pigmented skin cancer, which is far more common in the (caucasian) population than melanoma.
– We created a prediction model that combines analysis of a dermatoscopic and clinical image (“cCNN”) which is able to further increase diagnostic accuracy.
– We compared accuracy not only to experts, but users with different level of experience Continue reading