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Melonoma Detection Algorithm

Value Proposition

Melanoma is one of the most commonly-diagnosed cancers in the United States, with more than 76,000 cases diagnosed annually. The metastastic form of melanoma is particularly difficult to treat and carries a high mortality rate, making early detection and treatment paramount for preventing morbidity and mortality. Current methods for early detection of melanoma involve regular screening of skin lesions by trained physicians, where concerning lesions are excised and sent for confirmatory biopsy. Unfortunately, the vast majority of skin lesions are not malignant, meaning that the examining physician must detect the rare, malignant lesions against a significant background of normal moles on the skin. This technology uses images obtained from total-body photography to evaluate and prioritize potentially malignant lesions for examination by the physician, thus improving detection while reducing patient distress.


This technology provides a computational method for the analysis of total body skin images to detect skin lesions with high risk of malignancy. Using a clinically validated scoring algorithm, this software analyzes the images based on preset parameters and predicts the risk of melanoma with high specificity and sensitivity, which can aid clinicians in making the decision of whether to perform a confirmatory biopsy.


Through integration with existing platforms for total body image acquisition, this technology assists the examining physician in prioritization of skin lesions without requiring the purchase of additional equipment, thereby increasing productivity and accuracy while reducing costs for providers and patients alike.

Duke File (IDF) Number



  • Grichnik, James


    • Patent Number: 7,415,143
    • Title: Methods and Systems for the Detection of Malignant Melanoma
    • Country: United States of America

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School of Medicine (SOM)

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