A Critical Review on the 3D Cephalometric Analysis Using Machine Learning

Abstract

Machine learning applications have momentously enhanced the quality of human life. The past few decades have seen the progression and application of machine learning in diverse medical fields. With the rapid advancement in technology, machine learning has secured prominence in the prediction and classification of diseases through medical images. This technological expansion in medical imaging has enabled the automated recognition of anatomical landmarks in radiographs. In this context, it is decisive that machine learning is capable of supporting clinical decision support systems with image processing and whose scope is found in the cephalometric analysis. Though the application of machine learning has been seen in dentistry and medicine, its progression in orthodontics has grown slowly despite promising outcomes. Therefore, the present study has performed a critical review of recent studies that have focused on the application of machine learning in 3D cephalometric analysis consisting of landmark identification, decision making, and diagnosis. The study also focused on the reliability and accuracy of existing methods that have employed machine learning in 3D cephalometry. In addition, the study also contributed by outlining the integration of deep learning approaches in cephalometric analysis. Finally, the applications and challenges faced are briefly explained in the review. The final section of the study comprises a critical analysis from which the most recent scope will be comprehended.

AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning

“…There is an AI functionality that determines the quality of 2D cephalometric X-rays, which could eliminate lower-quality X-rays from being further evaluated due to a possible distortion of the analysis [56]. On top of that, machine learning has found use in both lateral and 3D cephalogram analysis to provide ever-improving quality in landmark localisation [57,58].…”

“…Nowadays, the question is not whether CBCT scans are accurate, but how automated processes can aid professionals in landmark detection, skeletal classification, scan analysis and CBCT data management [57,58,60,61]. Based on current research, it has been concluded that AI can be of great use in assessing mandibular shape asymmetry as well as in the screening of upper airways to measure multiple parameters [62,63].…”

Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset

“…Cephalometric analysis plays a critical role in the accurate treatment planning of malocclusions [ 1 ] as well as in guiding orthodontic treatment strategies and possible followup orthognathic surgeries [ 2 , 3 ]. By quantifying the severity of skeletal discrepancies and identifying specific anatomical features that contribute to malocclusions, cephalometric analysis helps orthodontists and oral surgeons to tailor personalized treatment plans.…”

In silico approach towards neuro‐occlusal rehabilitation for the early correction of asymmetrical development in a unilateral crossbite patient

“…In this sense, for instance, the unmineralized state of bones physically complicates the model reconstruction due to the low grey gradient between the different parts and overlapping intensities 63 . Fortunately, some recent advances in 3D cephalometric methods, 64 filters for image processing, 65 tissues engineering, 66 and computerised occlusal analysis systems 67 have improved the computational models' development and therefore, the knowledge about the craniofacial system growth.…”