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Review
. 2024 Jan 7;13(2):344.
doi: 10.3390/jcm13020344.

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review

Natalia Kazimierczak  1 , Wojciech Kazimierczak  1   2 , Zbigniew Serafin  2 , Paweł Nowicki  1 , Jakub Nożewski  3 , Joanna Janiszewska-Olszowska  4
Affiliations
Review

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review

Natalia Kazimierczak et al. J Clin Med. .

Abstract

The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.

Keywords: CBCT; artificial intelligence; cephalometric analysis; deep learning; orthodontics; radiology; skeletal age; treatment planning.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Simplified AI diagram.
Figure 2
Figure 2
Part of the automatic diagnostic report from a CBCT scan was obtained prior to orthodontic treatment on a 24-year-old male. The software automatically identified the absence of teeth 18 and 28, as well as changes in the remaining teeth, primarily consisting of attrition and the presence of dental fillings. The program has recommended further consultations as necessary.
Figure 3
Figure 3
Sample of automatic cephalometric landmark tracings performed using CephX (A) and WebCeph (B) on an 18-year-old male. The results of Downs cephalometric analysis superimposed on tracings (B). Measurements outside the standard range marked in red and with asterix *.
See this image and copyright information in PMC

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