Neural network adapted to wound cell analysis in surgical patients

Abstract

Assessment of the real state of wound healing of closed surgical wounds is uncertain both clinically and from conventional laboratory tests. Therefore, a novel approach based on early analysis of exactly timed wound cells, computerized further with an artificial neural network, was developed. At the end of routine surgery performed on 481 children under 18 years of age, a specific wound drain Cellstick™ was inserted subcutaneously between the wound edges to harvest wound cells. The Cellsticks™ were removed from 1 to 50 hours, mainly at hour 3 or 24 postsurgery. Immediately, the cellular contents were washed out using a pump constructed for the purpose. After cytocentrifugation, the cells were stained and counted differentially. Based on their relative proportions at selected time intervals, an artificial self-organizing neural map was developed. This was further transformed to a unidirectional linear graph where each node represents one set of relative cell quantities. As early as 3 hours, but more precisely 24 hours after surgery, the location of the nodes on this graph showed individually the patients' initial speed of wound inflammatory cell response. Similarly, timed Cellstick™ specimens from new surgical patients could be analyzed, computerized, and compared with these node values to assess their initial speed in wound inflammatory cell response. Location of the node on the graph does not express the time lapse after surgery but the speed of wound inflammatory cell response in relation to that of other patients.