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Prediction of facial growth for children with cleft lip and palate using 3D data mining and machine learning


   Department of Computer Science

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  Dr Richard Jensen  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society. 

The UK Research and Innovation (UKRI) fully-funded scholarships cover the full cost of tuition fees, a UKRI standard stipend of £15,921 per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.

Closing date for applications is 12 February 2022. For further information on how to apply please click here and select the "UKRI CDT Scholarship in AIMLAC" tab.

Project Overview

Approximately 150 children are born in England and Wales each year with complete unilateral cleft lip and palate (cUCLP). Despite improvements in clinical outcomes in the UK over the past 15 years, between 20-25% children with cUCLP have poor facial growth compared to 3% of the non-cleft Caucasian population. Poor facial growth results in poor aesthetic appearance and poor dental occlusion which can negatively impact on a child's psychosocial development with long-lasting effects. It is not clear why only some children with cUCLP have poor growth, nor why facial growth outcomes vary between surgeons and centres. A number of explanations have been advanced including extrinsic factors such as poor surgery in cleft palate repair during infancy, surgical technique and timing, and intrinsic factors such as the congenital absence of the upper lateral incisor, or the shape of the infants' upper arch, indicating a genetic cause. The relationship of the upper dental arch to the lower arch reflects mid-face growth and can be assessed as early as 5 years using the 5-year index. Children with cleft lip and palate in the UK have been treated in regional specialist centres since 2000 and facial growth is routinely assessed between the ages of 5 and 6 years in this way. It is also routine for cleft centres to take and keep a dental model of the upper arch of infants with cUCLP before they have any surgery.

This project would involve the development of techniques for both 3D data mining and machine learning for the scanned models of infants with cUCLP, in order to determine which features are most predictive of facial growth outcome and if a predictive model can be learned. The maxillary arch models taken from infants prior to their first surgical procedure will be used along with the 5-year index score to develop models via machine learning and identify important regions. In particular, the identification of an intrinsic neonatal arch shape that is predictive of detrimental facial growth would give an opportunity to explain prognosis and manage expectations more easily with parents. It would also facilitate research on the development of new techniques for earlier treatment of poor facial growth and more personalised care for individual patients.

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