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A System for the Early Diagnosis of Skeletal Orthodontic Malocclusions

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 i... see more

31/07/2024

KEDI MOBIL UYGULAM...

75K€

Project Budget: 75K€

Project leader

KEDI MOBIL UYGULAMA LIMITED SIRKETI No se ha especificado una descripción o un objeto social para esta compañía.

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2024-07-31

HORIZON-EIE-2022-SCALEUP-02-02: Women TechEU Expected Outcome:Project results are expected to contribute to the following expected outcomes:

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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2 Participantes

KEDI MOBIL UYGULAMA LIMITED...

75.00K€ | Leader

KEDI MOBIL UYGULAMA ANONIM S...

75.00K€ | participant

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Project duration: 12 months Date Start: 2023-07-30
End date: 2024-07-31

Project leader

KEDI MOBIL UYGULAMA LIMITED SIRKETI No se ha especificado una descripción o un objeto social para esta compañía.

Project Budget 75K€

participation deadline Sin fecha límite de participación.

Project description 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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