ISSLS Prize in Bioengineering Science 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.

Study design Investigation of the automation of radiological features from magnetic resonance images (MRIs) of the lumbar spine. Objective To automate the process of grading lumbar intervertebral discs and vertebral bodies from MRIs. Summary of background data MR imaging is the most common imaging technique used in investigating low back pain (LBP). Various features of degradation, based on MRIs, are commonly recorded and graded, e.g., Modic change and Pfirrmann grading of intervertebral discs. Consistent scoring and grading is important for developing robust clinical systems and research. Automation facilitates this consistency and reduces the time of radiological analysis considerably and hence the expense. Methods 12,018 intervertebral discs, from 2009 patients, were graded by a radiologist and were then used to train: (1) a system to detect and label vertebrae and discs in a given scan, and (2) a convolutional neural network (CNN) model that predicts several radiological gradings. The performance of the model, in terms of class average accuracy, was compared with the intra-observer class average accuracy of the radiologist. Results The detection system achieved 95.6% accuracy in terms of disc detection and labeling. The model is able to produce predictions of multiple pathological gradings that consistently matched those of the radiologist. The model identifies ‘Evidence Hotspots’ that are the voxels that most contribute to the degradation scores. Conclusions Automation of radiological grading is now on par with human performance. The system can be beneficial in aiding clinical diagnoses in terms of objectivity of gradings and the speed of analysis. It can also draw the attention of a radiologist to regions of degradation. This objectivity and speed is an important stepping stone in the investigation of the relationship between MRIs and clinical diagnoses of back pain in large cohorts

Monitoring of metabolite gradients in tissue-engineered constructs

At present, the assessment of developing tissue-engineered constructs is almost always carried out destructively using biochemical or histological methods to determine cell number, viability and tissue growth throughout the construct. Since many of these experiments are long, taking weeks or even months to complete, simple and readily applicable non-destructive methods of monitoring changes in cell metabolism, viability and tissue deposition within the construct would be invaluable; such methods could point out adverse responses during the early stages of culture. Here, we describe the use of microdialysis for detecting local changes in cellular metabolism within a tissue-engineered construct. Three-dimensional constructs consisting of bovine articular chondrocytes entrapped in an alginate gel were cultured in a bioreactor for two weeks. Glucose and lactate were monitored by microdialysis, as the major nutrient and metabolite, respectively. Concentration gradients within the construct were evident, with the highest lactate concentrations in the construct centre. The local lactate concentration was a measure of cellular metabolic activity, decreasing as cellular activity fell and increasing as cellular activity was stimulated. Nutrient starvation and cell death in the construct centre could be readily detected in constructs deliberately cultured under adverse conditions. The results show that probe measurements can give an early warning of inappropriate local metabolic changes. Such information during the growth of tissue-engineered constructs would allow either corrective action or else an early end to an unsuccessful test