American Board of Orthodontics: Time for change

Chung, Chun-Hsi; Tadlock, Larry P.; Barone, Nicholas; Pangrazio-Kulbersh, Valmy; Sabott, David G.; Foley, Patrick F.; Trulove, Timothy S.; Park, Jae Hyun; Dugoni, Steven A.

doi:10.1016/j.ajodo.2017.12.005

Cited by 14 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A total of 16,221 orthodontic images were obtained from the included patients. Of these, we excluded blurred images (106) as well as other photographs and radiographs (296) that did not meet the requirements of the American Board of Orthodontists [ 18 ]. We included a total of 14,399 orthodontic images in the internal dataset and 1,420 orthodontic images in the external dataset.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Classifying and Archiving Orthodontic Images

Guo

Lin

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

One of the main requirements for orthodontic treatment is continuous image acquisition. However, the conventional system of orthodontic image acquisition, which includes manual classification, archiving, and monitoring, is time-consuming and prone to errors caused by fatigue. This study is aimed at developing an effective artificial intelligence tool for the automated classification and monitoring of orthodontic images. We comprehensively evaluated the ability of a deep learning model based on Deep hidden IDentity (DeepID) features to classify and archive photographs and radiographs. This evaluation was performed using a dataset of >14,000 images encompassing all 14 categories of orthodontic images. Our model automatically classified orthodontic images in an external dataset with an accuracy of 0.994 and macro area under the curve of 1.00 in 0.08 min. This was 236 times faster than a human expert (18.93 min). Furthermore, human experts with deep learning assistance required an average of 8.10 min to classify images in the external dataset, much shorter than 18.93 min. We conclude that deep learning can improve the accuracy, speed, and efficiency of classification, archiving, and monitoring of orthodontic images.

show abstract

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Classifying and Archiving Orthodontic Images

Guo

Lin

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

Time for change

Vaden

2018

American Journal of Orthodontics and Dentofacial Orthopedics

View full text Add to dashboard Cite

Letters to the editor* Time for change "A merican Board of Orthodontics: Time for change "1 was published in the March 2018 issue of the AJO-DO. As a former director of the American Board of Orthodontics and one who continues to participate in the Board examination once a year, I strongly support the scenario-based examination that will soon be initiated by the American Board of Orthodontics. My support is based on my 27 years of experience as a director or examiner. The "average orthodontist" who has been in practice for 10 to 15 years and never pursued certification faces significant challenges with his or her practice. Orthodontists working in some environments (dental service organizations, for example) might not have the opportunity to treat the patients necessary to become certified. The new examination format will eliminate many barriers, so that a graduate of an orthodontic program accredited by the Commission on Dental Accreditation who has successfully completed the Board's written examination can participate in the clinical examination-no matter the clinical situation. The scenario-based examination will attract all eligible orthodontists-those in practice, educators, and orthodontists who have just graduated from a residency program. The Initial Certification Examination (ICE) experienced the following challenges. * The viewpoints expressed are solely those of the author(s) and do not reflect those of the editor(s), publisher(s), or Association.

show abstract