A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis

TL;DR

This paper introduces a comprehensive nailfold capillary dataset and an automated deep learning pipeline for analyzing nailfold capillaries, achieving high precision and accuracy, which can significantly aid medical diagnosis and research.

Contribution

The study provides the first large-scale dataset and an end-to-end automated analysis pipeline for nailfold capillaries, enhancing diagnostic accuracy and research capabilities.

Findings

Achieved sub-pixel measurement precision

89.9% accuracy in detecting abnormalities

Validated pipeline against clinical reports

Abstract

Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillary.