Abstract:
OBJECTIVES : To compare the precision of two cephalometric landmark identification methods,
namely a computer-assisted
human examination software and an artificial intelligence
program, based on South African data.
METHODS : This retrospective quantitative cross-sectional
analytical study utilized a data set
consisting of 409 cephalograms obtained from a South African population. 19 landmarks were
identified in each of the 409 cephalograms by the primary researcher using the two programs
[(409 cephalograms x 19 landmarks) x 2 methods = 15,542 landmarks)]. Each landmark generated
two coordinate values (x, y), making a total of 31,084 landmarks. Euclidean distances
between corresponding pairs of observations was calculated. Precision was determined by
using the standard deviation and standard error of the mean.
RESULTS : The primary researcher acted as the gold-standard
and was calibrated prior to
data collection. The inter- and intrareliability tests yielded acceptable results. Variations
were present in several landmarks between the two approaches; however, they were statistically
insignificant. The computer-assisted
examination software was very sensitive to several
variables. Several incidental findings were also discovered. Attempts were made to draw valid
comparisons and conclusions.
CONCLUSIONS : There was no significant difference between the two programs regarding the
precision of landmark detection. The present study provides a basis to: (1) support the use
of automatic landmark detection to be within the range of computer-assisted
examination
software and (2) determine the learning data required to develop AI systems within an African
context.
