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A System for the Early Diagnosis of Skeletal Orthodontic Malocclusions

Project description

Orthodontic diagnosis via an app

In orthodontics, malocclusions are divided into different classes based on the relationship between the upper and lower dental arches. Class III deformities are associated with a forward protrusion of the lower jaw relative to the upper jaw. Early detection, as early as age 7, is beneficial for planning effective intervention and reducing the complexity of treatment. However, this is not always possible as symptoms are subtle. Scientists of the EU-funded orthomobile project have developed an app that screens profile photos for preliminary diagnosis, informing parents and aiding doctor-patient interaction. Promising preliminary results of the prototype in Class III classification make it a candidate for researchers to bring it to the international market.

Objective

Orthodontics can correct deformities in the maxillofacial region by making growth modifications with orthopedic devices. Skeletal orthodontic deformities are divided into three; Class I, Class II, and Class III. The ideal age of intervention for these three classifications is different. Class III problems should be detected at age 7, but diagnosis is difficult even for the orthodontist at this age when symptoms are not yet discernible to the human eye. Even when the cephalometric film taken with an X-ray is analyzed, it may not be noticed by people. In addition, it is not possible to have an orthodontic examination for children all around the world before the age of 7. The main tasks carried out by our application include screening for the preliminary diagnosis by identifying symptoms in the profile photo; informing the parents; directing to the doctor, and facilitating the doctor-patient interaction. For Class III, the accuracy rate of correctly classified images was found to be 81.66%. The current prototype was created using statistical data from the Turkish population, and an application that can extract various skeletal characteristics from any given data training set is currently being developed. We intend to expand to the international market and create a training model for various races once we can diagnose Class I and Class II problems with the same accuracy.

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Coordinator

KEDI MOBIL UYGULAMA ANONIM SIRKETI
Net EU contribution
€ 75 000,00
Address
KOTEKLI MAH DENIZLI YOLU BLV TEKNOPARK SITESTI B BLOCK 4B IC KAPI 14
48000 Mugla
Türkiye

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Ege Aydın Muğla
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Links
Total cost
No data