Oral Presentations
Z Yasen, K Logishetty, T Edwards, L Gundle, M Moffat, A Owens, R Gregson, E Jones, K Edwards, H Breed, J Cubitt, S Sehdev, A Gomindes, ES Adeeko, C Khatri, J Carlos, I Ahmed, T Ward, J Leverington, L Debenham, A Metcalfe, J Ward, A Kapasi, C Uzoigwe, D Barlow, A McMurtrie

TL;DR
This paper presents a convolutional neural network for detecting hip dysplasia in radiographs and discusses VR and 3D-printed tools for surgical training.
Contribution
A CNN model for hip dysplasia detection and a 3D-printed surgical simulator with cortico-medullary differentiation for hand fracture training.
Findings
The CNN model showed potential for accurate hip dysplasia detection using LCEA and Ai measurements.
3D-printed simulators improved tactile feedback for surgical training and were upcycled to reduce waste.
VR simulation reduced anxiety and increased confidence in surgical trainees compared to traditional methods.
Abstract
Early detection of hip dysplasia is pivotal for optimal patient management, yet diagnosis is often delayed due to the subtlety of radiographic signs. This research introduces a convolutional neural network (CNN) model, harnessing the power of artificial intelligence (AI), with the aim to refine and enhance diagnostic accuracy in detecting adult hip dysplasia from plain film radiographs. This study utilises 1,000 anterior-posterior pelvic radiographs, partitioned into training (700), validation (100), and testing (200) datasets. Through the Unity software, each radiograph underwent systematic manual labelling of essential bony landmarks by multiple assessors, laying the groundwork for the CNN training phase. Our CNN focuses on reliably computing the lateral centre edge angle (LCEA) and acetabular index (Ai) measurements, and subsequently categorising radiographs into normal or…
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Taxonomy
TopicsOsteomyelitis and Bone Disorders Research · Vascular Anomalies and Treatments · Dysphagia Assessment and Management
