12 January 2021

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Artificial intelligence in the validation of cervical vertebral maturation stages

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In the latest issue (December 2020) of the American Journal of Orthodontics, a study was published aiming to develop an artificial neural network (ANN) model for cervical vertebral maturation (CVM) analysis and validate the model's output with the results of human observers.

Artificial intelligence (AI) can be described as a branch of computer science aimed at designing systems that can perform tasks that require human intelligence. AI should have the capacity to evaluate information received from sources and decide how accurate it is, deal with incomplete or inaccurate information, and thus manage the sources, so this is the goal of the researchers.

In treating skeletal misalignments of the jaws, the ability to individualize and estimate the bone growth rate is important for achieving better treatment outcomes. Although evaluation of hand–wrist radiographs is the most conventional method used to determine skeletal age, one of the main drawbacks of this technique is the increased dose of ionizing radiation to the patient. Determining skeletal development by evaluating changes in the size and shape of the cervical vertebrae was first proposed by Lamparski, after which several subsequent studies concluded that the cervical vertebral maturation (CVM) method was effective for individual growth estimation and identification of the mandibular growth spurt. 

Accurate determination of the direction and amount of mandibular growth is critical in cases in which growth modifications are planned—especially using functional appliances. This makes it necessary for the methods used to determine skeletal maturation and growth spurts in such cases to provide reliable results that can be reproduced by clinicians.

A total of 647 lateral cephalograms were selected from patients with 10-30 years of chronological age. New software with a decision support system was developed for manual labeling of the dataset.

As result, this study demonstrated that the developed ANN model performed close to, if not better than, human observers in CVM analysis. As conclusion from the authors, by generating new AI applications and new algorithms, automatic classification of CVM with AI may eventually replace the conventional evaluation methods used in orthodontic practice and forensic medicine.