Adoption of Artificial Intelligence (AI) for Melanoma Cancer Diagnosis Growing

The adoption of Artificial Intelligence (AI), combined with large image database, is showing promising results in distinguishing malignant from benign moles and other non-cancerous skin conditions.

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Researchers in the US, Germany, and France recently conducted a study using artificial intelligence known as deep learning Convolutional Neural Network (CNN). Researchers compared the performance with 58 international dermatologists and found that CNN missed very few melanomas and misdiagnosed benign moles less often than dermatologists. Along with performing image analysis, the network is capable of teaching itself to improve its performance by using a machine learning process.

The process that CNN used in the research is based on an algorithm developed by Google which allows AI to differentiate between thousands of different objects. However, according to some experts, more work and research is necessary to realize the full potential of this technology for skin cancer diagnosis.

Non-Invasive Techniques Show Promising Results in Skin Cancer Diagnostics

Non-invasive skin cancer diagnostics methods are being used on a large scale for skin cancer diagnosis. Although biopsy is considered to be the best way for skin cancer diagnosis, it is leading to a number of complications including infection, scarring, and bleeding. Hence, researchers are focusing on developing less invasive or non-invasive methods for skin cancer diagnosis.

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For instance, Pigmented Lesion Assay (PLA) test by DermTech uses sticky adhesive patch to collect skill cells from lesion to diagnose melanoma and clinical trials found that PLA has high sensitivity and very low probability of missing melanoma. Meanwhile, ongoing technological advancements in optical imaging techniques is also resulting in development of new tools in diagnosis and treatment of skin cancer.

As a part of non-invasive medical devices and techniques, researchers are focusing on techniques of dermoscopy and advanced non-invasive imaging techniques including optical coherence tomography and reflectance confocal microscopy. Meta-analysis performed in clinical settings demonstrated that dermoscopy has improved diagnostic accuracy of pigmented and non-pigmented lesions. In the near future, physicians would be able to recognize tumor patterns using dermoscopy technique.

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Reflectance confocal microscopy that enables real-time examination of skin tumors has been proven as an excellent method for evaluating melanocytic lesions. Using reflectance confocal microscopy, dermatologist may be able to avoid biopsy of benign tumor. Researchers have also developed Speckle-Variance optical coherence tomography (SV-OCT) capable of detecting vascular changes taking place in melanocytic lesions.