Cephalometric Landmark Detection: Artificial Intelligence vs Human Examination
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Date
2021
Authors
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Journal ISSN
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Publisher
University of the Western Cape
Abstract
Background: Cephalometric landmark detection is important for accurate diagnosis and
treatment planning. The most common cause of random errors, in both computer-aided
cephalometry and manual cephalometric analysis, is inconsistency in landmark detection.
These methods are time-consuming. As a result, attempts have been made to automate
cephalometric analysis, to improve the accuracy and precision of landmark detection whilst
also minimizing errors caused by clinician subjectivity.
Aim: This mini-thesis aimed to determine the precision of two cephalometric landmark
identification methods, namely an artificial intelligence programme (BoneFinder®) and a
computer-assisted examination software (Dolphin ImagingTM).
Methods: This was a retrospective quantitative cross-sectional analytical study. The dataset
comprised of 409 cephalograms obtained from a South African population. 19 landmarks were
selected and detected using a computer-assisted approach and an automatic approach. The x,y
coordinates for each landmark per system was recorded and the Euclidean distance was
calculated. Precision was determined by calculating 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 intra-reliability tests yielded acceptable results. There were variations
present in several landmarks between Dolphin and BoneFinder; however, they were
statistically insignificant. The computer-aided approach was very sensitive to several variables.
Attempts were made to draw valid comparisons and conclusions.
Description
Magister Scientiae Dentium - MSc(Dent)
Keywords
Accuracy, Artificial intelligence, Automated identification, Cephalometric analysis, Cephalometry