A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis
Source abstract
Abstract
Background: Researchers have noted that the advent of artificial intelligence (AI) heralds a promising era, with potential to significantly enhance diagnostic and predictive abilities in clinical settings. The aim of this meta-analysis is to evaluate the discrepancies in identifying anatomical landmarks between AI and manual approaches. Methods: A comprehensive search strategy was employed, incorporating controlled vocabulary (MeSH) and free-text terms. This search was conducted by two reviewers to identify published systematic reviews. Three major electronic databases, namely, Medline via PubMed, the Cochrane database, and Embase, were searched up to May 2024. Results: Initially, 369 articles were identified. After conducting a comprehensive search and applying strict inclusion criteria, a total of ten studies were deemed eligible for inclusion in the meta-analysis. The results showed that the average difference in detecting anatomical landmarks between artificial intelligence and manual approaches was 0.35, with a 95% confidence interval (CI) ranging from −0.09 to 0.78. Additionally, the overall effect between the two groups was found to be insignificant. Upon further analysis of the subgroup of cephalometric radiographs, it was determined that there were no significant differences between the two groups in terms of detecting anatomical landmarks. Similarly, the subgroup of cone-beam computed tomography (CBCT) revealed no significant differences between the groups. Conclusions: In summary, the study concluded that the use of artificial intelligence is just as effective as the manual approach when it comes to detecting anatomical landmarks, both in general and in specific contexts such as cephalometric radiographs and CBCT evaluations.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dental Radiography and Imaging · Medical Imaging and Analysis · Artificial Intelligence in Healthcare and Education
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Orthodontics, Graduate School of Clinical Dental Science, The Catholic University of Korea, Seoul 06591, Republic of Korea
Department of Orthodontics, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Medical Library, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Department of Oral and Maxillofacial Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Department of Periodontics, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
Dental Implantology, Graduate School of Clinical Dental Science, The Catholic University of Korea, Seoul 06591, Republic of Korea
Department of Medicine, Graduate School, The Catholic University of Korea, Seoul 06591, Republic of Korea
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