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Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease

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Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) can analyze nail images for disease diagnosis. This review synthesizes AI applications for nail lesion analysis, overcoming clinical adoption barriers for accessible healthcare.

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artificial intelligencedeep learningdermatologyexplainable AImachine learningnail image analysisnon-invasive diagnosispoint-of-care diagnosticssmartphone-based health monitoring

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Human nails are an underutilized biological substrate for digital health diagnostics.
  • Nail pathologies serve as non-invasive biomarkers for systemic diseases like anemia, diabetes, and melanoma.
  • Current dermatological reviews lack a specific focus on AI-driven nail lesion analysis.

Purpose of the Study:

  • To provide a comprehensive synthesis of image analysis techniques incorporating AI/ML for nail lesion diagnosis.
  • To focus specifically on diagnostic and screening applications related to nail pathologies.
  • To bridge the gap between clinical dermatology, AI, and mobile health for nail-based diagnostics.

Main Methods:

  • Review of technological modalities including smartphone imaging, dermoscopy, and Optical Coherence Tomography.
  • Analysis of image processing techniques such as color correction, segmentation, and region cropping.
  • Evaluation of diagnostic models ranging from classical methods to deep learning, including explainable AI (XAI) and federated learning.

Main Results:

  • Detailed descriptions of AI applications for specific nail diseases.
  • Discussion of impediments to clinical application: data scarcity, skin type variations, annotation errors, and adoption challenges.
  • Emphasis on emerging solutions like XAI, federated learning, and smartphone-based diagnostics.

Conclusions:

  • AI-enabled nail analysis holds potential for scalable, equitable, and trustworthy medical diagnostics.
  • Interdisciplinary innovation is crucial to transition AI nail analysis from prototypes to routine healthcare.
  • Advocacy for AI-driven nail analysis in global screening initiatives and clinical practice.