This optical sensor system of high light sensitivity provides adequate prerequisites for tissue spectrometry in anesthesia and intensive care medicine. It can be applied for measurements of absolute hemoglobin concentration and oxygenation. In hemoglobin free perfused organs during transient hemoglobin free perfusion, monitoring of light scattering and oxygenation measurements of myoglobin and cytochromes are possible. Due to data base of spectra of organs high precision of optical parameters (microsecond(s) and (mu) a) can be attained. Calibration of light intensities of irradiated and backscattered light are performed by the computer system.
In pharmacology many optical sensors are applied for investigations in vitro and in reduced systems. Due to a lack of sensors for optical imaging of functional structures in capillaries as well as in subcellular spaces the drug-tissue interaction in organs could not be monitored systematically. However, recent developments opened the door of this microcosm of life in its smallest entities. This will enable a better understanding of the questions of area and quality of drug action in tissue.
Oxyscan proved to be a very reliable optical sensor system for application in physiology and pathophysiology. Furthermore, it can be applied during liver transplantation in order to diminish or prevent reperfusion injury. Oxyscan could be used for monitoring of subcellular structures with great success.
Oxygradient II is an optical spectrometric sensor system for use in the microvolume of tissues (1-2 mm3). The microlightguides used for monitoring have catchment volumes of sizes that enable measurements of critical values at the venous end of capillaries. The Oxygradient II is able to be used for monitoring of all usual optical parameters in most human organs. A non-invasive monitoring during thoracic or abdominal surgery is possible with less-invasive micro-lightguides of diameters of 600 micrometers in combination with thin endoscopes.
The paper presents a new way to study the results obtained by back-scattering of light in tissue through artificial intelligence. The artificial neural networks' (ANN) ability to extract significant information from an initial set of data allows both an interpolation, in the a priori defined points, and an extrapolation outside of the range bordered by the extreme points from the initial training set. The data obtained from EMPHO Spectrophotometer were used for neural networks learning. Specific aspects related to the training procedure and parameter fitting are presented. The evaluation of the computing effort shows some way for future optimizations.
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