Healthc Inform Res.  2023 Jan;29(1):16-22. 10.4258/hir.2023.29.1.16.

Application of a Multi-Layer Perceptron in Preoperative Screening for Orthognathic Surgery

• Chaiprasittikul N
• Thanathornwong B
• Pornprasertsuk-Damrongsri S
• Raocharernporn S
• Maponthong S
• Manopatanakul S

• KMID: 2539394
• DOI: http://doi.org/10.4258/hir.2023.29.1.16

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.

Keyword

Orthognathic Surgery, Cephalometry, Neural Network Models, Classification, Artificial Intelligence

Figure

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  Figure 1 Flow chart of the entire process. R-CNN: region-based convolutional neural network.

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  Figure 2 Lateral cephalogram in which Detectron2 labeled 13 anatomical landmarks (1: U1 incisal tip, 2: U1 root tip, 3: ANS, 4: PNS, 5: L1 incisal tip, 6: L1 root tip, 7: A-point, 8: B-point, 9: nasion, 10: gonion, 11: menton, 12: mesiobuccal cusp of L4, and 13: mesiobuccal cusp of L6). The image showed the vertical and horizontal resolution of 96 dpi with a height and width of 1020 × 1024 pixels. See Table 1 for descriptions of anatomical landmarks.

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  Figure 3 Training loss of the object detection model, with the graph decreasing to the point of stability.

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  Figure 4 Image evaluated with the percentage of detected joints at a threshold of 0.05. Each circular zone includes the border of each landmark detection.

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  Figure 5 Diagram of the multi-layer perceptron used in this study.

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  Figure 6 (A) Training accuracy and validation accuracy of the neural network model, with the graph increasing to the point of stability. (B) Training loss and validation loss of the neural network model, with the graph decreasing to the point of stability.

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  Figure 7 The receiver operating characteristic curve swiftly changed from the origin to (0, 1), exhibiting a high true-positive rate and a low false-positive rate, with an area under the curve (AUC) of 0.96.

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  Figure 8 Confusion matrix showing only two misdiagnosed cases (accuracy = 0.963; sensitivity = 1; precision = 0.93; Fvalue = 0.963). Orthognathic stands for orthognathic surgery combined with orthodontic treatment, while non-orthognathic stands for non (orthognathic) surgery orthodontic treatment.

Reference

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