Fully Automated Analysis of the Anatomic and Mechanical Axes From Pediatric Standing Lower Limb Radiographs Using Convolutional Neural Networks.

BACKGROUND: Lower limb alignment is the quantification of a set of parameters that are commonly measured radiographically to test for and track a wide range of skeletal pathologies. Determining limb alignment is a commonly performed yet laborious task in the pediatric orthopaedic setting and is therefore an interesting goal for automation. METHODS: We employ a machine learning approach using convolutional neural networks (CNNs) to segment pediatric weight-bearing lower limb radiographs. The results are then used with custom Matlab code to extract anatomic landmarks and to determine lower limb alignment parameters. RESULTS: Measurements obtained from the automated workflow proposed here were compared with manual measurements performed by orthopaedic surgery fellows. Mechanical axis deviation was determined within a mean of 2.02 mm. Lateral distal femoral angle and medial proximal tibial angle were determined with a mean deviation of 1.73 and 2.90 degrees, respectively. The calculation speed for the full set of mechanical and anatomic axis parameters was found to be ~2 seconds per radiograph. CONCLUSIONS: The CNN-based approach proposed in this work was shown to produce results comparable to orthopaedic surgery fellows at fast calculation speed. Although further work is needed to validate these results against radiographs and measurements from other centers, we see this as a promising start and a functional path that can be employed in further research. CLINICAL RELEVANCE: CNNs are a promising approach to automating commonly performed, repetitive tasks, especially those pertaining to image processing. The time savings are particularly important in clinical research applications where large sets of radiographs are routinely available and require analysis. With further development of these algorithms, we anticipate significantly improved agreement with expert-measured results and the calculation speed.

The effect of type-2 diabetes conditions on neutrophil rolling adhesion.

OBJECTIVE: Type 2 diabetes mellitus (T2D) is the result of a dysregulation of insulin production and signalling, leading to an increase in both glucose concentration and pro-inflammatory cytokines such as interleukin (IL)-6 and tumour necrosis factor (TNF)-α. Previous work showed that T2D patients exhibited immune dysfunction associated with increased adhesion molecule expression on endothelial cell surfaces, accompanied by decreased neutrophil rolling velocity on the endothelial cell surface. Changes in cell rolling adhesion have direct vascular and immune complications such as atherosclerosis and reduced healing time in T2D patients. While previous studies focused primarily on how endothelial cells affect neutrophil rolling under T2D conditions, little is known about changes to neutrophils that affect their rolling. In this study, we aim to show how the rolling behaviour of neutrophils is affected by T2D conditions on a controlled substrate. RESULTS: We found that neutrophils cultured in T2D-serum mimicking media increased cell rolling velocity compared to neutrophils under normal conditions. Specifically, glucose alone is responsible for higher rolling velocity. While cytokines further increase the rolling velocity, they also reduce the cell size. Both glucose and cytokines likely reduce the function of P-selectin Glycoprotein Ligand-1 (PSGL-1) on neutrophils.