# Microstructural Evaluation of Dental Implant Success Using Micro-CT: A Comprehensive Review
## 9 Recent Innovations and Future Prospects  
In recent times, there has been a proliferation of micro-CT systems, which now encompass a range of resolutions, from standard (-1 \(\upmu\)m) to high (sub-micron) and ultra-high (below 0.6 \(\upmu\)m). This diversification has significantly enhanced the capacity to visualize and analyze [34]. The application of high-resolution micro-CT enables a comprehensive understanding of the osseointegration dynamics, bone structure, and healing process, as well as the interaction between the bone microstructure and the implant surface [86]. The use of high-resolution imaging (sub-micron) enables the visualization of the bone-implant interface, facilitating the assessment of osseointegration and bone contact at a more detailed level [93]. Furthermore, high-resolution imaging techniques can discern a multitude of microstructural forms that influence osteoconductivity, including pore morphology and surface roughness, which are paramount for efficacious osseointegration [94]. The capacity to examine porous surfaces at the sub-micron level enables researchers to establish a correlation between the surface roughness of the implant and the biological response of the bone in the context of bonding [95]. The advancement of micro-CT imaging technology has enabled a comprehensive investigation of the osseointegration process of diverse materials and their combinations, leading to the development of implant designs that are more beneficial for patients than existing implant designs [96]. The incorporation of artificial intelligence (AI) into micro-CT data analysis for dental implantation will undoubtedly offer substantial advantages, including enhanced image resolution and more precise surgical planning. However, it is important to acknowledge the potential constraints associated with this technology, such as the possibility of inaccuracies and the necessity for high-quality data [97]. AI has the capacity to accurately segment complex anatomical features, such as the mandibular canal, thereby facilitating safer surgical planning and reducing the risk of injury. Furthermore, the implementation of this technology can also mitigate the incidence of human error and expedite the process while reducing costs [98; 99]. The potential of AI in the field of dental implantology has recently been the subject of investigation. The findings indicated that the AI model demonstrated a high degree of accuracy in the recognition of implant types, with accuracy rates ranging from 93.8% to 98% when utilizing periapical and panoramic images. Furthermore, the AI model is capable of predicting implant success with an accuracy range of 62.4% to 80.5% [100]. A randomized controlled trial reported higher implant success rates and superior accuracy in AI-assisted planning in comparison to traditional methods. In dental implant planning using CBCT images, the AI algorithm achieved an overall accuracy of 96% for the lower jaw and 83% for the upper jaw in identifying areas of tooth loss. While the results of this study demonstrate promising potential, further research is necessary to enhance the accuracy, generalizability, and clinical applicability of AI-based approaches in implant dentistry [101]. [PERSON] et al. (2024) showed the utilization of AI in the placement of dental implants has the potential to enhance the precision and efficacy of treatment planning in 20 patients. The results of this study demonstrated that treatment plans generated by AI exhibited a high degree of alignment with those devised by experienced clinicians, with an average discrepancy of less than 1 mm in implant position and two degrees in angulation. This demonstrates that AI is capable of providing highly accurate treatment plans. Furthermore, the AI-assisted planning process also reduces the time required, with an average time of only 10 min compared to 30 min for clinical planning. This efficiency can allow dental professionals to see more patients or allocate time to other important tasks (Tables 2-4).