Diabetes mellitus is a chronic condition marked by insufficient insulin utilization or production, causing metabolic dysregulation. If not controlled, it can cause serious complications that affect major organ systems such as the cardiovascular and ocular systems. Early diagnosis is essential for timely interventions to ensure effective glycemic control and lower the risk of complications. The present study introduced a robust and comprehensive dataset derived from nail-fold capillaroscopy. This dataset, which includes imaging and some video data from 126 individuals diagnosed with type 2 diabetes mellitus (T2DM) alongside 76 healthy controls, consisted of 3283 images obtained from diabetic subjects and 3412 images from non-diabetic participants. All images were acquired at 390× high magnification with a high resolution of 640×480 pixels, including corresponding video data, ensuring a thorough and detailed dataset for our study. The dataset was organized into two analytical tracks. The first track focused on nail-fold morphology, classifying images into four types: hairpin, crossing, tortuous, and bushy. After filtering out duplicates and low-quality images, 1279 images were selected for analysis. The second track involved binary classification for the detection of diabetes, differentiating healthy individuals from people with diabetes. This utilized the complete dataset and the augmented versions created by combining multiple images into composite formats to enhance feature representation. Additionally, we evaluated several benchmark deep-learning models such as Vision Transformers (ViT) and Convolutional Neural Networks (CNNs) for morphological classification and diabetes detection tasks. This analysis illuminated current model performance, highlighted challenges, and paved the way for future research opportunities. Importantly, this dataset is critical and holds significant potential for advancing non-invasive automated diagnostic methodologies in diabetes-related nail-fold capillary research, offering a promising future for diabetes research. The dataset is available at: https://huggingface.co/datasets/HanaNguyen/Capillary-Dataset. The github: https://github.com/urgonguyen/Capillarydataset.git.

Vision transformers (ViT) perform remarkably on large datasets but often underperform compared to convolutional neural networks (CNNs) when trained from scratch on smaller datasets. This study aimed to improve the accuracy of ViT in diabetic patient classification with the nail-fold capillaries dataset. It involved preprocessing images to extract relevant features and, unlike previous methods that used a single image, it combined multiple images into a composite for simultaneous analysis of video frames. The results demonstrated a substantial 27.53% improvement, with the F1 score increasing from 32.26% to 59.79% when using the proposed model training from scratch. Additionally, the F1 score also increased by 3.93% and achieved 98.56% with the pre-trained model using our proposed method which integrated multiple nail fold capillaroscopy images and employed ViT. Detailed analysis revealed that merging the nine images into a 3×3 format instead of a panoramic format yielded optimal results.

It is crucial to diagnose diabetes in the early phase because it can cause serious complications if not treated early. Nailfold capillaries are capillaries that are located under the proximal nail fold area. Studies have shown that different types of abnormal patterns of nail fold capillaries can be important factors when diagnosing various diseases or conditions. Therefore, this study proposes a system that can detect the nailfold capillary morphology of type 2 diabetes mellitus in a non-invasive way utilizing an object detection model. We trained an object detection model YOLOv5 with bi-directional feature pyramid network (Bi-FPN) as the neck part, which is an improved version of the open-source object detector YOLOv5 to detect capillaries and their corresponding morphology types including hairpin, crossing, bushy, and tortuous capillaries with mAP@50 0.74. We expect the proposed system to aid in the early diagnosis of diabetes in the future.