## 1 Introduction  
Implant-borne prostheses are a safe and predictable option in oral rehabilitation [1]. Several bone microarchitectural factors have been associated with the failure of osseointegration, such as low bone quality, thin cortical bone, and sparse trabecular bone [2; 3]. It has been established that poor recipient bone quality is an important risk factor in early implant failure [4; 5; 6]. Bone quality assessment prior to or at the time of implant placement influences the clinician's decision-making regarding the choice between submerged or non-submerged implant placement and determines the protocol for prosthetic loading [7; 8; 9]. Therefore, predicting bone quality at dental implant recipient sites prior to surgery is essential for the clinician. postoperatively, microarchitectural evaluation of dental implant recipient bone through histological and microcomputed tomography (microCT) analysis of bone core biopsy samples allows the researcher to determine the risk factors involved in implant failure and evaluate the clinical performance of bone graft materials. Histological examination of bone core biopsy samples harvested during dental implant placement may help identify underlying pathologies in cases of implant failure [10; 11]. There is no consensus in the literature regarding the exact definition of bone quality [12]. The term bone quality incorporates the degree of mineralization, cortical bone thickness [13], and trabecular bone morphology [14; 15]. Hounsfield unit (HU) measurements on computed tomography (CT) reconstructions, insertion torque resistance, and resonance frequency analysis (RFA) are the most prevalent methods to evaluate bone quality before and during implant placement [7; 8; 16; 17; 18; 19]. Cone beam computed tomography (CBCT) has become the standard imaging method in the dento-maxillofacial region for the preoperative assessment of dental implant recipient bone anatomy and quantity. However, gray-level measurements performed on CBCT reconstructions are not reliable for the assessment of bone quality [20; 21]. MicroCT analysis has been successfully applied to assess the microarchitecture of bone core biopsy samples obtained from the implant bed to evaluate the bone quality of dental implant recipient bone following surgery [6; 10; 13; 16; 22; 23; 24; 25; 26]. Studies show that the results of micromorphometric analysis conducted with microCT correlate highly with histomorphometric measurements, with it being considered as the gold standard for the structural analysis of trabecular bone and implant osseointegration [25; 26; 27; 28]. However, microCT analysis may be more reliable in cases where micromorphometric parameters are to be calculated to assess the true three-dimensional microarchitecture [22; 23; 24; 29]. Conventional histomorphometry provides information on tissues and cellularity [30]. Nevertheless, histomorphometry is usually carried out on representative slices and provides little information on three-dimensional structures [22]. MicroCT is a non-destructive modality to evaluate the three-dimensional structure of trabecular bone in high resolution [31; 32]. However, microCT reconstruction is based on the radiodensity of structures and provides no information on cellularity. Manual or semiautomatic thresholding is the most widespread method to differentiate between bone and soft tissue. Recently, artificial intelligence (AI) has been applied for pathological and medical image analysis with adequate sensitivity and specificity [33; 34]. In dentistry, AI has been successfully applied for caries diagnostics, the detection of apical periodontitis and odontogenic cysts, orthodontic diagnosis and treatment planning, the identification of the inferior alveolar nerve, the segmentation of teeth and jaw bones, airway modeling, sinus mucosa thickening, dental implant type recognition, and degenerative changes in the temporomandibular joint [35; 36; 37].