Artificial neural networks can be effectively used to model changes of intracranial pressure (ICP) during spinal surgery using different noninvasive ICP surrogate estimators.

BACKGROUND: Artificial intelligence (AI) techniques play a major role in anesthesiology, even though their importance is often overlooked. In the extant literature, AI approaches, such as artificial neural networks (ANNs), have been underutilized, being used mainly to model patient's consciousness state, to predict the precise number of anesthetic gases, the level of analgesia, or the need of anesthesiological blocks, among others. In the field of neurosurgery, ANNs have been effectively applied to the diagnosis and prognosis of cerebral tumors, seizures, low back pain, and also to the monitoring of intracranial pressure (ICP). METHODS: A multilayer perceptron (MLP), which is a feedforward ANN, with hyperbolic tangent as activation function in the input/hidden layers, softmax as activation function in the output layer, and cross-entropy as error function, was used to model the impact of prone versus supine position and the use of positive end expiratory pressure (PEEP) on ICP in a sample of 30 patients undergoing spinal surgery. Different noninvasive surrogate estimations of ICP have been used and compared: namely, mean optic nerve sheath diameter (ONSD), noninvasive estimated cerebral perfusion pressure (NCPP), Pulsatility Index (PI), ICP derived from PI (ICP-PI), and flow velocity diastolic formula (FVDICP). RESULTS: ONSD proved to be a more robust surrogate estimation of ICP, with a predictive power of 75%, whilst the power of NCPP, ICP-PI, PI, and FVDICP were 60.5%, 54.8%, 53.1%, and 47.7%, respectively. CONCLUSIONS: Our MLP analysis confirmed our findings previously obtained with regression, correlation, multivariate receiving operator curve (multi-ROC) analyses. ANNs can be successfully used to predict the effects of prone versus supine position and PEEP on ICP in patients undergoing spinal surgery using different noninvasive surrogate estimators of ICP.

What do people search online concerning the "elusive" fibromyalgia? Insights from a qualitative and quantitative analysis of Google Trends.

Fibromyalgia is a chronic disease, characterized by pain, fatigue, and poor sleep quality. Patients and mainly those with chronic diseases tend to search for health-related material online. Google Trends (GT), an online tracking system of Internet hit-search volumes that recently merged with its sister project Google Insights for Search (Google Inc.), was used to explore Internet activity related to fibromyalgia. Digital interest in fibromyalgia and related topics searched worldwide has been reported in the last 13 years. A slight decline in this interest has been observed through the years, remaining stable in the last 5 years. Fibromyalgia web behavior exhibited a regular, cyclic pattern, even though no seasonality could be detected. Similar findings have been reported among rheumatoid arthritis and depression. However, differently from rheumatoid arthritis and depression, the focus of the fibromyalgia-related queries was more concentrated on drug side effects and the "elusive" nature of fibromyalgia: is it a real or imaginary condition? Does it really exist or is it all in your head? A tremendous amount of information on fibromyalgia and related topics exist online. Still many queries have been raised and repeated constantly by fibromyalgia patients in the last 13 years. Therefore, physicians should be aware of the common concerns of people or patients regarding fibromyalgia in order to give a proper answers and education.