Magnetic resonance imaging features of sinonasal disorders in horses.

Diseases of paranasal sinuses and nasal passages in horses can be a diagnostic challenge because of the complex anatomy of the head and limitations of many diagnostic modalities. Our hypothesis was that magnetic resonance (MR) imaging would provide excellent anatomical detail and soft tissue resolution, and would be accurate in the diagnosis of diseases of the paranasal sinuses and nasal passages in horses. Fourteen horses were imaged. Inclusion criteria were lesions located to the sinuses or nasal passages that underwent MR imaging and subsequent surgical intervention and/or histopathologic examination. A low field, 0.3 tesla open magnet was used. Sequences in the standard protocol were fast spin echo T2 sagittal and transverse, spin echo T1 transverse, short-tau inversion recovery (STIR) dorsal, gradient echo 3D T1 MPR dorsal (plain and contrast enhanced), spin echo T1 fatsat (contrast enhanced). Mean scan time to complete the examination was 53 min (range 39-99 min). Lesions identified were primary or secondary sinusitis (six horses), paranasal sinus cyst (four horses), progressive ethmoid hematoma (two horses), and neoplasia (two horses). The most useful sequences were fast spin echo T2 transverse and sagittal, STIR dorsal and FE3D MPR (survey and contrast enhanced). Fluid accumulation, mucosal thickening, presence of encapsulated contents, bone deformation, and thickening were common findings observed in MR imaging. In selected horses, magnetic resonance imaging is a useful tool in diagnosing lesions of the paranasal sinuses and nasal passages.

Prognostic and diagnostic value of EEG signal coupling measures in coma.

OBJECTIVE Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. METHODS In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. RESULTS Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). CONCLUSIONS EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. SIGNIFICANCE Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.