Among skeletal deformities, Class III is the one that best time for the treatment is the pre-adolescent growth period. Diagnosis and treatment in this period continue to be a complex orthodontic problem. Class III malocclusion is especially difficult to treat with braces frequently requiring surgical intervention after pubertal growth spurt. In addition, delayed recognition of the problem will yield to significant functional, aesthetic and psychological concerns. In this study, we proposed a comparative analysis of three predictive models to predict Class III malocclusion: deep learning algorithm, machine learning algorithm and a rule-based algorithm. For this analysis, we collected a novel profile image data set along with their formal diagnosis from 435 orthodontics patients. The most successful method among the three was the machine learning method with an accuracy of %76.

The authors have declared no competing interest.

This study was funded by TUBITAK-TEYDEB

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRB# 54022451-050.05.04 Orthodontics at Bezmialem Vakif University Faculty of Dentistry. IRB approval (IRB# 54022451-050.05.04) was obtained from Bezmialem Vakif University to use profile image data of real patients who visited Bezmialem Vakif University in our project. All patients parents approved the use of videos and photos of their children by signing a consent form as part of this study. All consent forms are available in digital format.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

All data produced in the present study are available upon reasonable request to the authors.