REFLECTANCE Confocal Microscopy (RCM) is revolutionising dermatological imaging by providing non-invasive, detailed images of skin tissues. Unlike confocal fluorescence microscopy, which is limited to ex vivo applications, RCM can be used both in vivo and ex vivo, offering unparalleled versatility in clinical settings. By enabling visualisation of skin structures up to 250 μm beneath the surface, RCM has emerged as a superior diagnostic tool, particularly for skin cancers like melanoma and carcinoma.
RCM’s development was driven by a need for non-invasive diagnostic methods. Advances in lasers, detectors, and image processing have enhanced its capabilities, allowing dermatologists to map skin structures at the cellular level. This “optical biopsy” reduces the need for invasive procedures and improves diagnostic accuracy, with RCM achieving over 70% accuracy in identifying skin cancers. When combined with dermoscopy, RCM reduces unnecessary biopsies and helps delineate tumour margins pre-surgery, improving patient outcomes.
Despite its promise, RCM interpretation remains challenging due to a lack of widespread training and the complexity of the images. Artificial intelligence (AI) is addressing these issues by providing tools to enhance image analysis. Machine learning models, particularly convolutional neural networks, have demonstrated impressive accuracy in classifying skin lesions and segmenting pigmented areas within RCM images. For instance, recent studies achieved classification accuracies exceeding 85%, showcasing AI’s potential to augment dermatological diagnostics.
AI also aids in image enhancement, feature detection, and disease prediction. By identifying key structures like the dermal–epidermal junction, AI facilitates more accurate and efficient assessments. Moreover, synthetic image generation by AI helps train models, overcoming data scarcity. These advancements pave the way for real-time AI-assisted decision-making in clinical practice, streamlining diagnosis and treatment planning.
The integration of RCM with AI is transforming dermatology, offering improved accuracy, reduced invasiveness, and enhanced patient care. Collaboration among researchers, clinicians, and AI developers will be crucial for refining these tools. As technology advances, AI-powered RCM systems promise to revolutionise dermatological workflows, making diagnoses faster, more accurate, and personalised.
Katie Wright, EMJ
Reference
Aksoy S et al. Advanced artificial intelligence techniques for comprehensive dermatological image analysis and diagnosis. Dermato. 2024; 4(4):173-186. https://doi.org/10.3390/dermato4040015
