Publication: Application of a Multi-Layer Perceptron in Preoperative Screening for Orthognathic Surgery
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Issued Date
2023
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application/pdf
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Scopus
Bibliographic Citation
Healthcare Informatics Research. Vol 29, No.1 (2023), p.16-22
Suggested Citation
Chaiprasittikul N., Thanathornwong B., Pornprasertsuk-Damrongsri S., Raocharernporn S., Maponthong S., Manopatanakul S. Application of a Multi-Layer Perceptron in Preoperative Screening for Orthognathic Surgery. Healthcare Informatics Research. Vol 29, No.1 (2023), p.16-22. doi:10.4258/hir.2023.29.1.16 Retrieved from: https://hdl.handle.net/20.500.14740/10736
Other Contributor(s)
Abstract
Objectives: Orthognathic surgery is used to treat moderate to severe occlusal discrepancies. Examinations and measurements for preoperative screening are essential procedures. A careful analysis is needed to decide whether cases require orthognathic surgery. This study developed screening software using a multi-layer perceptron to determine whether orthognathic surgery is required. Methods: In total, 538 digital lateral cephalometric radiographs were retrospectively collected from a hospital data system. The input data consisted of seven cephalometric variables. All cephalograms were analyzed by the Detectron2 detection and segmentation algorithms. A keypoint region-based convolutional neural network (R-CNN) was used for object detection, and an artificial neural network (ANN) was used for classification. This novel neural network decision support system was created and validated using Keras software. The output data are shown as a number from 0 to 1, with cases requiring orthognathic surgery being indicated by a number approaching 1. Results: The screening software demonstrated a diagnostic agreement of 96.3% with specialists regarding the requirement for orthognathic surgery. A confusion matrix showed that only 2 out of 54 cases were misdiagnosed (accuracy = 0.963, sensitivity = 1, precision = 0.93, F-value = 0.963, area under the curve = 0.96). Conclusions: Orthognathic surgery screening with a keypoint R-CNN for object detection and an ANN for classification showed 96.3% diagnostic agreement in this study. © 2023 The Korean Society of Medical Informatics.