Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was proposed for training ν -support vector classification (ν-SVC), which can handle a quadratic formulation with a pair of equality constraints. In this paper, we first present a modified SVOR formulation based on a sum-of-margins strategy. The formulation has multiple constraints, and each constraint includes a mixture of an equality and an inequality. Then, we extend the accurate on-line ν-SVC algorithm to the modified formulation, and propose an effective incremental SVOR algorithm. The algorithm can handle a quadratic formulation with multiple constraints, where each constraint is constituted of an equality and an inequality. More importantly, it tackles the conflicts between the equality and inequality constraints. We also provide the finite convergence analysis for the algorithm. Numerical experiments on the several benchmark and real-world data sets show that the incremental algorithm can converge to the optimal solution in a finite number of steps, and is faster than the existing batch and incremental SVOR algorithms. Meanwhile, the modified formulation has better accuracy than the existing incremental SVOR algorithm, and is as accurate as the sum-of-margins based formulation of Shashua and Levin.

Although conventional intraarterial digital subtraction angiography remains the gold standard method for imaging the vertebral artery, noninvasive modalities such as ultrasound, multislice computed tomographic angiography and magnetic resonance angiography are constantly improving and are playing an increasingly important role in diagnosing vertebral artery pathology in clinical practice. This paper reviews the current state of vertebral artery imaging from an evidence-based perspective. Normal anatomy, normal variants and a number of pathological entities such as vertebral atherosclerosis, arterial dissection, arteriovenous fistula, subclavian steal syndrome and vertebrobasilar dolichoectasia are discussed.

IC GCA appears to be associated with neurologic complications and mortality. In some cases corticosteroid alone was not sufficient to prevent neurologic complications. The role of additional immunosuppressive agents needs further investigation.

Artificial intelligence (AI)-based models have become a growing area of interest in predictive medicine and have the potential to aid physician decision-making to improve patient outcomes. Imaging and radiomics play an increasingly important role in these models. This review summarizes recent developments in the field of radiomics for AI in head and neck cancer. Prediction models for oncologic outcomes, treatment toxicity, and pathological findings have all been created. Exploratory studies are promising; however, validation studies that demonstrate consistency, reproducibility, and prognostic impact remain uncommon. Prospective clinical trials with standardized procedures are required for clinical translation.