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DermaScan

Skin Cancer Detection
Mobile Application
Clinical Tool

AI-powered skin cancer detection system designed to increase early melanoma detection with high diagnostic accuracy.

Project Overview

DermaScan is an AI-powered skin lesion analysis system currently in development for dermatology clinics and primary care settings. The platform is being designed to combine advanced computer vision algorithms with mobile imaging technology to enable rapid, accurate assessment of suspicious skin lesions for potential malignancy.

The system is being developed both as a clinician-facing tool for dermatologists and primary care physicians, and as a patient-accessible mobile application with physician oversight. DermaScan aims to improve early-stage detection of skin cancers while optimizing specialist referrals.

Key Capabilities

Multi-modal Analysis

Combines standard RGB imaging with dermoscopic and thermal imaging (when available) to analyze lesion characteristics across multiple visual spectrums.

Mobile Integration

Smartphone-based system with specialized attachment for dermoscopic imaging, enabling use in diverse clinical settings and remote locations with minimal infrastructure requirements.

Real-time Assessment

Delivers results in under 15 seconds, providing immediate risk stratification for suspicious lesions and enabling on-the-spot clinical decision making.

Explainable AI

Provides visual explanations of diagnostic factors using heat maps and feature highlighting to support clinical interpretation and decision-making transparency.

Technical Architecture

DermaScan's architecture consists of several key components:

  • Mobile application for iOS and Android with specialized camera and imaging protocols
  • Optional specialized dermoscopic attachment for enhanced imaging capabilities
  • Edge computing capabilities for on-device processing in low-connectivity settings
  • Cloud-based processing for more complex analyses and model updates
  • HIPAA and GDPR compliant data management system with end-to-end encryption
  • EMR integration modules compatible with major healthcare systems
  • Clinician dashboard for patient management and follow-up tracking

Clinical Applications

Primary Care Application

In Development

DermaScan is being designed for use in primary care clinics where it will serve as a decision support tool for non-dermatologist physicians. In this setting, the system is intended to:

  • Screen suspicious lesions during routine examinations
  • Prioritize dermatology referrals based on malignancy risk
  • Document and track lesions longitudinally for changes over time
  • Provide structured documentation for specialist consultations

Dermatology Practice Integration

In Development

We plan to integrate DermaScan as a complementary diagnostic tool alongside traditional dermoscopy in dermatology practices. In this specialized setting, the system aims to provide:

  • Quantitative analysis of lesion features based on established diagnostic criteria
  • Second-opinion verification for ambiguous presentations
  • Risk stratification to guide biopsy decisions
  • Standardized documentation and image archiving

Validation Approach

Validation Strategy

The system aims to achieve diagnostic performance that is helpful to healthcare providers in correctly identifying and classifying suspicious skin lesions.

Project Status

Development Status

Prototype Phase

Current Version

Version 1.5

Regulatory Planning

FDA Submission Preparation

Development Team

DermaScan Project Team

Technical Documentation

Development Workflow

1

Model Training

Development of robust AI algorithms trained on diverse skin lesion datasets to ensure broad coverage of skin types and conditions

2

System Design

Creation of intuitive interfaces and workflows designed for both specialists and non-specialists in clinical environments

3

Validation Testing

Rigorous validation against gold standard diagnoses and comparison to clinical expert assessments

4

Continuous Improvement

Ongoing algorithm refinement and feature enhancements based on clinical feedback and performance metrics

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