AI-Enhanced 7-Point Checklist for Melanoma Detection Using Clinical Knowledge Graphs and Data-Driven Quantification

2024-07-23

arXiv.org

AI Summary

Traditional seven-point checklist (7PCL) methods can only differentiate melanoma from nevi, failing to address clinical complexity involving coexisting benign mimickers; they also employ rigid attribute weights and lack explicit modeling of clinical correlations. To overcome these limitations, we propose a hybrid knowledge- and data-driven diagnostic framework: (1) a Clinical Knowledge Topological Graph (CKTG) explicitly encodes intra- and inter-attribute relationships among 7PCL features; (2) a Gradient Diagnosis strategy with Dynamic Diagnostic Weighting (GD-DDW) emulates clinicians’ staged reasoning—“observe signs first, then discriminate”—via multi-stage inference; and (3) integration of multimodal skin imagery, an adaptive-receptive-field graph neural network, and gradient-guided dynamic weight allocation. Evaluated on the EDRA dataset, our model achieves a mean AUC of 85%, substantially improving early detection accuracy while delivering interpretable, quantifiable attribute importance scores grounded in clinical relevance.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Multi-modal Vision
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The 7-point checklist (7PCL) is widely used in dermoscopy to identify malignant melanoma lesions needing urgent medical attention. It assigns point values to seven attributes: major attributes are worth two points each, and minor ones are worth one point each. A total score of three or higher prompts further evaluation, often including a biopsy. However, a significant limitation of current methods is the uniform weighting of attributes, which leads to imprecision and neglects their interconnections. Previous deep learning studies have treated the prediction of each attribute with the same importance as predicting melanoma, which fails to recognize the clinical significance of the attributes for melanoma. To address these limitations, we introduce a novel diagnostic method that integrates two innovative elements: a Clinical Knowledge-Based Topological Graph (CKTG) and a Gradient Diagnostic Strategy with Data-Driven Weighting Standards (GD-DDW). The CKTG integrates 7PCL attributes with diagnostic information, revealing both internal and external associations. By employing adaptive receptive domains and weighted edges, we establish connections among melanoma's relevant features. Concurrently, GD-DDW emulates dermatologists' diagnostic processes, who first observe the visual characteristics associated with melanoma and then make predictions. Our model uses two imaging modalities for the same lesion, ensuring comprehensive feature acquisition. Our method shows outstanding performance in predicting malignant melanoma and its features, achieving an average AUC value of 85%. This was validated on the EDRA dataset, the largest publicly available dataset for the 7-point checklist algorithm. Specifically, the integrated weighting system can provide clinicians with valuable data-driven benchmarks for their evaluations.

Problem

• Enhancing melanoma diagnosis using 7-point checklist limitations
• Integrating clinical knowledge graphs with neural systems
• Improving accuracy via multimodal feature extraction

Innovation

• Integrates clinical knowledge graphs with neural systems
• Uses gradient diagnostic strategy with data-driven weights
• Leverages dual-attention for multimodal feature extraction