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Related Concept Videos

Skin Cancer01:30

Skin Cancer

4.0K
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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Updated: Jun 12, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Skin Cancer Detection in Diverse Skin Tones by Machine Learning Combining Audio and Visual Convolutional Neural

Bruce N Walker1, Travis Wayne Blalock2, Rebecca Leibowitz2

  • 1School of Psychology and School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA.

Oncology
|September 23, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence with audio-visual data accurately diagnosed skin cancer in both fair and dark skin tones. This dual-mode technology shows promise for improving skin cancer detection accessibility and effectiveness.

Keywords:
Deep learningMedical visualizationPreventive medicineSkin cancerSkin of colorSonification

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Skin cancer (SC) is prevalent in fair skin (FS) but diagnosed at later stages in dark skin (DS), leading to poorer outcomes.
  • Machine learning diagnostic accuracy for SC is typically lower in DS compared to FS.
  • A novel risk intervention technology combining visual and sonified data (pixel to sound) was developed to enhance SC detection.

Purpose of the Study:

  • To evaluate the efficacy of a dual audio-visual artificial intelligence (AI) classifier in diagnosing skin cancer across different skin tones.
  • To compare the diagnostic performance of AI in fair skin (Fitzpatrick I-III) versus dark skin (Fitzpatrick IV-VI).

Main Methods:

  • A retrospective study utilized biopsy-validated smartphone images analyzed by a dual audio-visual convolutional neural network.
  • Sixty Fitzpatrick I-III and seventy-two Fitzpatrick IV-VI skin lesion images were compared.
  • Diagnostic performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

Main Results:

  • The AI classifier achieved comparable diagnostic performance across skin tones, with AUCs of 0.858 for FS and 0.856 for DS.
  • Sensitivity for FS and DS was 84.4% and 79.6%, respectively (p=NS).
  • Specificity for FS and DS was 84.2% and 85.3%, respectively (p=NS).

Conclusions:

  • The dual-modality AI classifier effectively identifies skin cancer in both fair and dark skin tones with similar accuracy.
  • Sonification of malignant signs shows potential for improving skin cancer diagnosis, even with smartphone-captured images.
  • This technology could contribute to more effective and accessible healthcare for skin cancer detection.