# Comparison of Manual and Automated Capillary Morphometry Measurements in Oral Mucosa: A Pilot Study
## 1 Introduction  
The microvascular network of the oral mucosa is a vital component of oral and systemic health, playing a key role in nutrient delivery, immune response, and tissuerepair. Capillaries are particularly sensitive to pathological changes, and alterations in their morphology--such as variations in caliber, length, and density--are often early indicators of both localized and systemic diseases. For instance, conditions such as diabetes, hypertension, and smoking-related disorders have been linked to significant changes in capillary structure, making the assessment of microvascular health a critical aspect of clinical diagnosis and monitoring [1; 2; 3; 4; 5; 6]. In this context, the ability to accurately and efficiently measure capillary parameters is essential for advancing our understanding of microvascular pathologies and improving patient outcomes. In recent years, automated capillary analysis has gained significant traction in medical research, particularly in the assessment of nailfold capillaroscopy, where both commercial software solutions (e.g., Capillary.io) [7] and research grade software [8; 9] are widely used for quantifying microvascular parameters in systemic sclerosis and other connective tissue diseases [10; 11; 12; 13]. These tools have demonstrated high reproducibility and efficiency in clinical settings, reducing the dependency on manual measurements. However, similar advancements in oral mucosa capillaroscopy remain scarce. The oral mucosa presents unique challenges, such as higher mobility, variable tissue transparency, and complex capillary architectures, which complicate the development of automated analysis tools. To date, no commercial software exists for oral mucosa capillaroscopy, leaving manual methods as the primary approach for microvascular assessment in this region. This gap underscores the pioneering role of our neural network-based software, which represents the first automated solution specifically designed for oral mucosa capillaroscopy. Given these limitations, traditionally the evaluation of capillary morphology in oral mucosa has relied on manual methods, which involve direct measurement and analysis by trained observers. While these approaches have provided valuable insights [14], they are inherently limited by their time-consuming nature, susceptibility to human error, and inter-observer variability. These limitations become particularly pronounced in large-scale studies or clinical settings, where the need for rapid and reproducible measurements is paramount. In recent years, a software capable of automatically measuring the parameters of each individual frame of videocapillaroscopy has been developed at the University of Palermo, in collaboration with the Odontostomatological Clinic of the Paolo Giaccone University Hospital [15]. This software utilizes a neural network specifically trained for this purpose [16]. At the end of the brief processing operation, both the average values for all frames in the video and the values for each individual frame are provided. This approach undoubtedly ensures an extraordinarily high level of data homogeneity compared to the manual method, where the best available frame was selected and measurements were performed only on that single frame. A static image often fails to capture all the information present even in just 1 s of video, where greater variability can be observed. The lack of operator dependence and the repeatability, synonymous with objectivity, significantly elevate the quality standard of this methodology, both in clinical practice and research (Figure 1). This study compares the two methods, the manual and the automatic one. Using videocapillaroscopy, we conducted a detailed analysis of capillary parameters across four distinct anatomical sites in ten healthy subjects: the right buccal mucosa, left buccal mucosa, lower labial mucosa, and upper labial mucosa.