A neural network approach to the biopsy diagnosis of early acute renal transplant rejection

Abstract

A neural network can provide a considerable improvement in the diagnosis of early acute allograft rejection, though further development work will be needed before this becomes a routine diagnostic tool. The selection of cases used to train the network is crucial to the quality of its performance. There is scope to improve the system further by incorporating clinical information. Other related areas where this approach is likely to be of value are discussed.