Summary
Objectives:
To evaluate the potential for machine learning techniques to identify objective criteria
for classifying vertical facial deformity.
Methods:
19 parameters were determined from 131 lateral skull radiographs. Classifications
were induced from raw data with simple visualisation, C5.0 and Kohonen feature maps;
and using a Point Distribution Model (PDM) of shape templates comprising points taken
from digitised radiographs.
Results:
The induced decision trees enable a direct comparison of clinicians’ idiosyncrasies
in classification. Unsupervised algorithms induce models that are potentially more
objective, but their blackbox nature makes them unsuitable for clinical application.
The PDM methodology gives dramatic visualisations of two modes separating horizontal
and vertical facial growth. Kohonen feature maps favour one clinician and PDM the
other. Clinical response suggests that while Clinician 1 places greater weight on
5 of 6 parameters, Clinician 2 relies on more parameters that capture facial shape.
Conclusions:
While machine learning and statistical analyses classify subjects for vertical facial
height, they have limited application in their present form. The supervised learning
algorithm C5.0 is effective for generating rules for individual clinicians but its
inherent bias invalidates its use for objective classification of facial form for
research purposes. On the other hand, promising results from unsupervised strategies
(especially the PDM) suggest a potential use for objective classification and further
identification and analysis of ambiguous cases. At present, such methodologies may
be unsuitable for clinical application because of the invisibility of their underlying
processes. Further study is required with additional patient data and a wider group
of clinicians.
Keywords
Maxillofacial Abnormalities - Artificial Intelligence
