A neural network approach to the analysis and classification of human craniofacial growth.
Planning of treatment in the field of orthodontics and maxillo-facial surgery is largely dependent on the individual growth of a patient. In the present work, the growth of 43 orthodontically untreated children was analysed by means of lateral cephalograms taken at the ages of 7 and 15. For the description of craniofacial skeletal changes, the concept of tensor analysis and related methods have been applied. Thus the geometric and analytical shortcomings of conventional cephalometric methods have been avoided. Through the use of an artificial neural network, namely self-organizing neural maps, the resultant growth data were classified and the relationships of the various growth patterns were monitored. As a result of self-organization, the 43 children were topologically ordered on the emerging map according to their craniofacial size and shape changes during growth. As a new patient can be allocated on the map, this type of network provides a frame of reference for classifying and analysing previously unknown cases with respect to their growth pattern. If landmarks are used for the determination of growth, the morphometric methods applied as well as the subsequent visualization of the growth data by means of neural networks can be employed for the analysis and classification of growth-related skeletal changes in general.