Effect of auto-adaptive metal artifact reduction (aMAR) program in cone-beam computed tomography on assessing pre-implant bone levels: Correspondence

This letter concerns the publication on "Effect of autoadaptive metal artifact reduction (aMAR) program in cone-beam computed tomography on assessing preimplant bone levels. 1 " The introduction of adaptive metal artifact reduction (aMAR) algorithms in cone-beam computed tomography (CBCT) represents a significant advancement in dental imaging.However, this research lacks a thorough exploration of the specific algorithms and techniques used in aMAR.Although the study by Abesi 1 describes the use of an aAMR program, the specific construction algorithms and processing steps employed by the software are not described in the Materials and Methods section; their inclusion would enhance methodological transparency and reproducibility.A more detailed comparison with existing metal artifact reduction strategies could help improve understanding of the algorithm's effectiveness. 2,3Furthermore, this study could benefit from statistical validation of the algorithm's performance in a variety of clinical scenarios, as a single technique may not be sufficient in a variety of them.As highlighted in previous clinical evaluations, statistical validation across diverse implant sites and patient populations provides a stronger basis for generalization. 4 more comprehensive assessment of the potential of aMAR algorithms could provide a clearer understanding of their advantages over traditional MAR methods.One significant disadvantage of aMAR techniques is their reliance on the quality of the first picture capture.If raw CBCT images are noisy or the patient's posture is not appropriate, even the most complex post-processing features may fail to yield an accurate model image.Furthermore, the performance of aMAR algorithms varies depending on the type of metal present and its spatial arrangement in the photos.Such diversity may