โก Quick Summary
This study introduces a novel methodology for the depth estimation and visualization of dermatological lesions, utilizing advanced techniques such as convolutional neural networks (CNN) and explainable artificial intelligence. The approach achieved an impressive 86% accuracy in classifying lesions as benign or malignant, paving the way for improved clinical diagnostics.
๐ Key Details
- ๐ Methodology: Convolutional neural networks and explainable AI
- ๐งฉ Techniques used: Gradient class activation maps, depth from defocus, Gabor filters
- ๐ Accuracy: 86% in lesion classification
- ๐ค Collaboration: Worked with dermatologists for feedback and clinical applicability
๐ Key Takeaways
- ๐ก Novel approach for estimating lesion depth using 2D images.
- ๐ 3D holograms provide enhanced visualization for dermatologists.
- ๐ Positive feedback from dermatologists indicates potential clinical utility.
- ๐ Malignant lesions show higher concentrations of red spots in deeper skin layers.
- ๐ ๏ธ Red spot analysis measures infection levels through holographic representation.
- ๐ Study conducted at King Edward Memorial Institute in India.
- ๐งช Future evaluations are necessary before clinical implementation.
๐ Background
Accurate depth estimation of dermatological lesions is crucial for effective clinical staging, particularly for malignant lesions that may grow beneath the skin. Traditional methods, such as biopsies, provide clear information but are invasive. This study addresses the need for quick, noninvasive techniques to estimate lesion depth using 2D images, which could significantly enhance diagnostic accuracy and patient care.
๐๏ธ Study
The research involved developing a new methodology for depth estimation and visualization of skin lesions. By employing a convolutional neural network for classification and utilizing explainable AI to localize features, the study aimed to create a comprehensive tool for dermatologists. The integration of computer graphics and depth estimation techniques allowed for the generation of 3D holograms that could aid in diagnosing conditions like melanoma.
๐ Results
The study achieved an 86% accuracy in classifying lesions as benign or malignant. The mapping of CNN outputs to corresponding holograms revealed that malignant lesions exhibited a higher concentration of red spots in both upper and deeper skin layers, indicating a correlation between the qualitative results and the initial classification. This finding underscores the potential of the proposed method in enhancing diagnostic capabilities.
๐ Impact and Implications
The implications of this study are significant for the field of dermatology. By providing a noninvasive method for depth estimation and visualization, dermatologists can make more informed decisions regarding lesion management. The positive feedback from clinical experts suggests that this innovative approach could lead to improved diagnostic tools, ultimately enhancing patient outcomes and streamlining clinical workflows.
๐ฎ Conclusion
This study highlights the transformative potential of combining advanced technologies like AI and computer graphics in dermatology. The ability to accurately estimate and visualize lesion depth could revolutionize how dermatologists diagnose and treat skin conditions. Continued research and evaluation are essential to fully integrate this methodology into clinical practice, but the future looks promising for this innovative approach.
๐ฌ Your comments
What are your thoughts on this groundbreaking approach to dermatological diagnostics? We invite you to share your insights and engage in a discussion! ๐ฌ Leave your comments below or connect with us on social media:
The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study.
Abstract
BACKGROUND: Thus far, considerable research has been focused on classifying a lesion as benign or malignant. However, there is a requirement for quick depth estimation of a lesion for the accurate clinical staging of the lesion. The lesion could be malignant and quickly grow beneath the skin. While biopsy slides provide clear information on lesion depth, it is an emerging domain to find quick and noninvasive methods to estimate depth, particularly based on 2D images.
OBJECTIVE: This study proposes a novel methodology for the depth estimation and visualization of skin lesions. Current diagnostic methods are approximate in determining how much a lesion may have proliferated within the skin. Using color gradients and depth maps, this method will give us a definite estimate and visualization procedure for lesions and other skin issues. We aim to generate 3D holograms of the lesion depth such that dermatologists can better diagnose melanoma.
METHODS: We started by performing classification using a convolutional neural network (CNN), followed by using explainable artificial intelligence to localize the image features responsible for the CNN output. We used the gradient class activation map approach to perform localization of the lesion from the rest of the image. We applied computer graphics for depth estimation and developing the 3D structure of the lesion. We used the depth from defocus method for depth estimation from single images and Gabor filters for volumetric representation of the depth map. Our novel method, called red spot analysis, measures the degree of infection based on how a conical hologram is constructed. We collaborated with a dermatologist to analyze the 3D hologram output and received feedback on how this method can be introduced to clinical implementation.
RESULTS: The neural model plus the explainable artificial intelligence algorithm achieved an accuracy of 86% in classifying the lesions correctly as benign or malignant. For the entire pipeline, we mapped the benign and malignant cases to their conical representations. We received exceedingly positive feedback while pitching this idea at the King Edward Memorial Institute in India. Dermatologists considered this a potentially useful tool in the depth estimation of lesions. We received a number of ideas for evaluating the technique before it can be introduced to the clinical scene.
CONCLUSIONS: When we map the CNN outputs (benign or malignant) to the corresponding hologram, we observe that a malignant lesion has a higher concentration of red spots (infection) in the upper and deeper portions of the skin, and that the malignant cases have deeper conical sections when compared with the benign cases. This proves that the qualitative results map with the initial classification performed by the neural model. The positive feedback provided by the dermatologist suggests that the qualitative conclusion of the method is sufficient.
Author: [‘Parekh P’, ‘Oyeleke R’, ‘Vishwanath T’]
Journal: JMIR Dermatol
Citation: Parekh P, et al. The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study. The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study. 2024; 7:e59839. doi: 10.2196/59839