Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network

After nasal bone fractures, fractures of the mandible are the most frequently encountered injuries of the facial skeleton. Accurate identification of fracture locations is critical for effectively managing these injuries. To address this need, JawFracNet, an innovative artificial intelligence method, has been developed to enable automated detection of mandibular fractures in cone-beam computed tomography (CBCT) scans. JawFracNet employs a 3-stage neural network model that processes 3-dimensional patches from a CBCT scan. Stage 1 predicts a segmentation mask of the mandible in a patch, which is subsequently used in stage 2 to predict a segmentation of the fractures and in stage 3 to classify whether the patch contains any fracture. The final output of JawFracNet is the fracture segmentation of the entire scan, obtained by aggregating and unifying voxel-level and patch-level predictions. A total of 164 CBCT scans without mandibular fractures and 171 CBCT scans with mandibular fractures were included in this study. Evaluation of JawFracNet demonstrated a precision of 0.978 and a sensitivity of 0.956 in detecting mandibular fractures. The current study proposes the first benchmark for mandibular fracture detection in CBCT scans. Straightforward replication is promoted by publicly sharing the code and providing access to JawFracNet on grand-challenge.org

Glycation changes molecular organization and charge distribution in type I collagen fibrils

Funder: Royal Society Newton TrustFunder: China Scholarship Council Cambridge TrustsFunder: SENS Research Foundation; doi: http://dx.doi.org/10.13039/100013772Funder: Raymond and Beverly Sackler Fund for Physics of MedicineFunder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266Abstract: Collagen fibrils are central to the molecular organization of the extracellular matrix (ECM) and to defining the cellular microenvironment. Glycation of collagen fibrils is known to impact on cell adhesion and migration in the context of cancer and in model studies, glycation of collagen molecules has been shown to affect the binding of other ECM components to collagen. Here we use TEM to show that ribose-5-phosphate (R5P) glycation of collagen fibrils – potentially important in the microenvironment of actively dividing cells, such as cancer cells – disrupts the longitudinal ordering of the molecules in collagen fibrils and, using KFM and FLiM, that R5P-glycated collagen fibrils have a more negative surface charge than unglycated fibrils. Altered molecular arrangement can be expected to impact on the accessibility of cell adhesion sites and altered fibril surface charge on the integrity of the extracellular matrix structure surrounding glycated collagen fibrils. Both effects are highly relevant for cell adhesion and migration within the tumour microenvironment