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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Screening for Melanoma Modifiers using a Zebrafish Autochthonous Tumor Model

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Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients.

Madeleine E Crawford¹, Kiyana Kamali¹, Rachel A Dorey¹

¹Division of Clinical Dermatology and Cutaneous Science, Department of Medicine, Dalhousie University, Halifax, NS, Canada.

Journal of Cutaneous Medicine and Surgery

|December 29, 2023

Summary

Artificial intelligence (AI) shows promise as a skin cancer screening tool for flagging potential melanomas in self-referred patients. While AI has limitations, it can aid in timely diagnosis and improve access to care for suspicious skin lesions.

Keywords:

artificial intelligence convoluted neural networks deep learning dermoscopy machine learning melanoma melanoma detection melanoma screening

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Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

Early detection of melanoma is crucial for timely medical care.
Self-referred patients with concerning skin lesions require efficient diagnostic pathways.

Purpose of the Study:

To assess the feasibility of using artificial intelligence (AI) to identify potential melanomas in patients concerned about skin lesions.
To evaluate AI's performance in flagging suspicious lesions compared to dermatologists.

Main Methods:

Patients with skin lesions of concern were recruited and lesions scanned using the FotoFinder System.
AI software analyzed lesion images, and results were compared to evaluations by experienced dermatologists.
Suspicious lesions identified by AI or dermatologists were surgically excised for analysis.

Main Results:

Seventeen malignancies, including 10 melanomas, were confirmed.
AI did not flag six melanomas, which presented as diagnostically challenging atypical melanocytic proliferations.
AI's diagnostic capability for malignancy was found to be non-inferior to dermatologists evaluating dermoscopic images.

Conclusions:

AI can function as a practical aid in skin cancer screening for self-referred patients.
Despite technical and diagnostic limitations, AI inclusion in screening programs can enhance timely diagnosis of skin cancer.
AI offers valuable support in providing prompt access to diagnosis for individuals with concerning skin lesions.