## 1 Introduction  
Wound healing is an essential process for restoring tissue integrity after trauma. While large skin wounds often heal with hypertrophic scarring, resulting in displacement and reduced joint mobility, these adverse healing outcomes were not generally observed in oral mucosa, which generally heals faster than skin [1]. Several studies have identified differences between oral and cutaneous wound healing [2; 3; 4; 5]. It has been observed that the faster wound closure in the oral mucosa compared to that in skin could be due to the presence of saliva, a faster immune response, and increased extracellular matrix remodeling. In particular, during remodeling, oral wounds show less contraction and better restoration of tissue architecture in terms of collagen structure [1]. However, in comparison to skin, much less is known about the kinetics of remodeling during oral mucosa healing [6]. In particular, understanding the organizational kinetics of the area surrounding the transgingival part of dental implants would ensure more accurate monitoring of the healing process, with favorable effects in terms of implant osseointegration and long-term success. Until now, tissue tension during wound healing has been attributed to cellular forces produced by tissue-resident (myo-)fibroblasts alone; more recently, the storage of tensile forces in the extracellular matrix has been shown to have a significant, hitherto overlooked, contribution to macroscopic tissue tension. In particular, it has been shown that the organization of collagen fibrils is related to tissue contraction, indicating a mechanical contribution of the collagen fibrils to tension in the contraction process [7; 8]. In this direction, reorganization of the soft tissues in the early stages following the placement of the dental implant was recently imaged using high-resolution tomography based on synchrotron radiation, discovering how the collagen bundles of the newly formed connective tissue intertwine impart tensile strength to the entire peri-implant soft tissue. It was, therefore, hypothesized that good organization of collagen bundles may reduce the risk of early bone resorption by reducing the infiltration of inflammatory cells [9]. However, the limitation of the previous synchrotron study was related to the fact that conventional techniques of segmentation of the tomographic image are based on the discrimination of grey levels, i.e., the physical density of the tissues. Therefore, conventional segmentation techniques fail to discriminate the orientation of the collagen fibers, as they all have the same physical density. In recent years, artificial intelligence (AI) has been applied to different types of images (MRI [10], tomography [11; 12], X-ray and ultrasound [13]) and for different purposes (detail detection, object recognition and noise reduction [14]). This allowed the various disciplines to interface with new methodologies and to analyze images and data with a support they had not previously considered [15]. In particular, AI has also established itself as a support tool in dentistry [16], especially in facilitating diagnosis and treatment planning [17]. Recently, AI has been used to improve image interpretation in 2D and 3D dental radiology [18], identifying dental caries [19] and automatically segmenting anatomical structures such as maxilla, mandible and teeth [20; 21]. In this demonstrative study, for the first time to the authors' knowledge, AI was applied directly on highly resolved images acquired by synchrotron tomography to automatically segment collagen bundles of the peri-implant connective tissue. We trained the algorithm starting from available data, the so-called training data, and then we made it acquire the ability to predict new information, called test data.