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

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.