An Interpretable Vision Transformer as a Fingerprint-Based Diagnostic Aid for Kabuki and Wiedemann-Steiner Syndromes

2026-02-06

AI Summary

This study addresses the frequent underdiagnosis of Kabuki syndrome and Wiedemann-Steiner syndrome, which arises from overlapping clinical phenotypes and limited accessibility to genetic testing, by proposing the first application of an interpretable Vision Transformer (ViT) for fingerprint-assisted diagnosis of rare genetic disorders. The model achieves AUC scores of 0.80, 0.73, and 0.85—and F1 scores of 0.71, 0.72, and 0.83—in three tasks: distinguishing each syndrome from healthy controls and differentiating between the two syndromes. Through attention visualization, the model highlights discriminative fingerprint regions, thereby enhancing diagnostic transparency and clinical trustworthiness. These results demonstrate the feasibility and innovative potential of AI-driven fingerprint screening for early identification of rare diseases.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: AI for Accessibility

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Kabuki syndrome (KS) and Wiedemann-Steiner syndrome (WSS) are rare but distinct developmental disorders that share overlapping clinical features, including neurodevelopmental delay, growth restriction, and persistent fetal fingertip pads. While genetic testing remains the diagnostic gold standard, many individuals with KS or WSS remain undiagnosed due to barriers in access to both genetic testing and expertise. Dermatoglyphic anomalies, despite being established hallmarks of several genetic syndromes, remain an underutilized diagnostic signal in the era of molecular testing. This study presents a vision transformer-based deep learning model that leverages fingerprint images to distinguish individuals with KS and WSS from unaffected controls and from one another. We evaluate model performance across three binary classification tasks. Across the three classification tasks, the model achieved AUC scores of 0.80 (control vs. KS), 0.73 (control vs. WSS), and 0.85 (KS vs. WSS), with corresponding F1 scores of 0.71, 0.72, and 0.83, respectively. Beyond classification, we apply attention-based visualizations to identify fingerprint regions most salient to model predictions, enhancing interpretability. Together, these findings suggest the presence of syndrome-specific fingerprint features, demonstrating the feasibility of a fingerprint-based artificial intelligence (AI) tool as a noninvasive, interpretable, and accessible future diagnostic aid for the early diagnosis of underdiagnosed genetic syndromes.

Problem

• Kabuki syndrome
• Wiedemann-Steiner syndrome
• dermatoglyphics
• diagnostic aid
• rare genetic disorders

Innovation

• Vision Transformer
• fingerprint-based diagnosis
• interpretable AI
• rare genetic syndromes
• dermatoglyphics