1st supervisor: Pablo Lamata, King’s College London
2nd supervisor: Simone Speggiorin, Guy’s and St Thomas’ NHS Foundation Trust
3rd supervisor: Nidhin Laji, Congenita
Industrial links: Congenita
One in 100 children are born with heart defects, of which a quarter will require surgery. Congenital heart diseases present as a spectrum of anatomical malformation, each consisting of infinite combinations of defects unique to the patient, and this presents an immense challenge to cardiac surgeons. In dealing with newborns (or neonates), these highly specialized surgeons must operate on atretic great vessels (abnormal vessels circulating blood in and out of the heart) that are only millimeters thick – smaller than the equipment itself. In great vessel reconstructions, such as the Norwood procedure, donor tissue (or homograft) patches are used to repair the shape of the vessels to improve blood flow. Patch dimensions must be optimized to account for an almost infinite array of patient anatomies. Misshapen patches can have dire consequences for patients: cut too small they can result in obstruction; cut too large they can result in aneurysm. This places a heavy burden on surgeons with profound effects on morbidity and mortality.
The project’s objective si to improve the surgical outcomes in congenital surgical procedures by an optimization and automation of the decision making process related to the personalization of surgical technique.
Rather than catering for specific diseases, the scope of the project will be to produce a toolkit of planning models catered to individual anatomical defects. These include: atrial septal defects, ventricular septal defects, valve atresia and great vessel patch reconstruction.
The candidate will be expected to (1) develop methods to segment the relevant anatomy with minimal user input; (2) build a library of cases from retrospective data, recreating the decisions taken by surgeons; (3) develop algorithms to propose optimal anatomic and functional surgical reconstructions of the congenital defects; (4) develop and test strategies for an optimal intra-operative visualization of the outcome, exploring two main alternatives: 3D printing or augmented reality.
The general requirements for such solutions will be: (1) to support decision making processes, both pre- and intra- operatively, so that the surgeon can make better technical decisions in the heat of the moment; (2) to operate at millimeter accuracy to cater for unforgivingly small margins for error.
The project is framed in the exciting phase of transferring ideas from the academic environment to a young and dynamic spin-out. The student will thus be supervised by an academic from the CMIB team at KCL, a pediatric surgeon from Evelina Hospital, and the entrepreneur that has launched Congenita Ltd.
This PhD project will be one of the keystones in fulfilling the mission of improving survival rates for children undergoing corrective congenital surgery. Envisioned workflows will require close multidisciplinary collaboration with Congenita’s technical lead, clinician champions, and business development team. The post will thus be an opportunity to build the R&D foundations for an early stage MedTech company. If the candidate demonstrates a good fit, we hope they will stay on as a product lead, with potential for equity ownership in the company.