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Related Experiment Videos

An educational machine learning demonstration framework for plastic surgeons using open datasets.

Kian Daneshi1, Yasona Neocleous2, Abigail G-Medhin3

  • 1School of Population Health and Medicine, University of Sheffield, Sheffield, UK; Department of Bioengineering, Imperial College London, London, UK.

Journal of Plastic, Reconstructive & Aesthetic Surgery : JPRAS
|February 28, 2026
PubMed
Summary

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This summary is machine-generated.

Plastic surgeons can now build and deploy AI tools for melanoma detection using open datasets and machine learning. This framework, DermAI-Melanoma, empowers clinicians with practical data science skills for improved diagnostic capabilities.

Area of Science:

  • Dermatology and Plastic Surgery
  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Limited practical engagement with data science in dermatology and plastic surgery.
  • Open datasets enable clinicians to explore machine learning without specialized infrastructure.
  • DermAI-Melanoma is an open data demonstration framework for plastic surgeons using melanoma classification.

Purpose of the Study:

  • Demonstrate reproducible training and deployment of deep learning models for melanoma classification.
  • Provide a didactic case study for plastic surgeons to engage with data science.
  • Showcase the use of public datasets for AI development in surgery.

Main Methods:

  • Utilized the SIIM-ISIC 2020 melanoma dataset with patient-level stratification to prevent data leakage.
Keywords:
Data scienceDatasetsDeep learningKaggleMachine learningPlastic surgery

Related Experiment Videos

  • Applied image preprocessing including resizing, color balancing, and augmentation.
  • Trained two convolutional neural networks: EfficientNet-B3 and MobileNetV3-Small, deploying them via TensorFlow.js.

    • Main Results:

    • EfficientNet-B3 achieved 97% test accuracy, detecting 92% of melanomas.
    • MobileNetV3-Small achieved 94% accuracy, with <5 MB storage and <2s smartphone deployment.
    • Model performance is comparable to dermatologist benchmarks in melanoma detection literature.

    Conclusions:

    • DermAI-Melanoma empowers plastic surgeons to build transparent, deployable AI tools using open data.
    • Similar frameworks can foster education, research, and innovation in plastic surgery.
    • Embracing open data sharing and cross-disciplinary collaboration is crucial for advancing AI in surgery.