<p>Nailfold capillaries, which are located at the base of the fingernail, provide a non-invasive window into peripheral microcirculation and are valuable for the early detection of systemic diseases such as diabetes mellitus and scleroderma, with abnormalities in capillary morphology, such as twisting and enlargement, indicative of vascular damage due to factors such as glucose accumulation. Traditional imaging modalities such as ultrasound and laser imaging have been used to evaluate peripheral blood vessels; however, such methods are often expensive and burdensome. Recently, the use of machine learning with microscopic imaging has shown promise in classifying capillary structures. However, these methods typically require large, annotated datasets, limiting the practicality; thus, we propose a novel approach that uses transfer learning to classify the shape of nailfold capillaries, specifically the presence or absence of capillary twisting. A pre-trained model based on fundus vessel images was fine-tuned for this task, leveraging the structural similarity between retinal and nailfold capillaries. The proposed method was found to achieve accurate and efficient classification even with a limited nailfold dataset, demonstrating its potential for practical clinical use.</p>