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In this pilot study, we used vibrational optical tomography (VOCT), along with machine learning, to evaluate the specificity and sensitivity of using light and audible sound to differentiate between normal skin and skin cancers. The results reported indicate that the use of machine learning, and the height and location of the VOCT mechanovibrational peaks, have potential for being used to noninvasively differentiate between normal skin and different cancerous lesions. VOCT data, along with machine learning, is shown to predict the differences between normal skin and different skin cancers with a sensitivity and specificity at rates between 78 and 90%. The sensitivity and specificity will be improved using a larger database and by using other AI techniques. Ultimately, VOCT data, visual inspection, and dermoscopy, in conjunction with machine learning, will be useful in telemedicine to noninvasively identify potentially malignant skin cancers in remote areas of the country where dermatologists are not readily available.

cancers

Identification of Cancerous Skin Lesions Using Vibrational Optical Coherence Tomography (VOCT): Use of VOCT in Conjunction with Machine Learning to Diagnose Skin Cancer Remotely Using Telemedicine