Orthodontic Image Alignment Using AI Facial Landmark Detection
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Solution Overview
Problem
Existing orthodontic diagnosis methods rely on manual marking of anatomical reference points by orthodontists, leading to variability and reduced accuracy, time consumption, and difficulty in standardizing the face shape determination for orthodontic treatment.
Innovation Solution
A computing device and method using artificial neural network-based machine learning to automatically detect and align medical and facial images by matching feature points, including anatomical reference points, through superimposition and alignment techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual marking of reference points by orthodontists is used, then flexibility in academic features can be applied, but accuracy and standardization deteriorate due to arbitrary marking and skill variation
Solution Approach 1:
The system enables self-service by allowing the computer to automatically detect and mark reference points on facial images using machine learning algorithms, eliminating the need for manual marking by orthodontists while maintaining consistency and accuracy across different cases
Solution Approach 2:
The manual mechanical process of orthodontists marking reference points is replaced with an automated computer vision system using artificial neural networks and machine learning algorithms that automatically detect and identify anatomical landmarks on facial images
2Adaptability or versatility
If manual marking of reference points is performed, then orthodontists can apply academic features, but time consumption increases due to one-by-one marking
Solution Approach 1:
The system performs automatic self-service by detecting multiple reference points simultaneously through machine learning algorithms, processing the entire facial image to identify all anatomical landmarks in one operation rather than marking them individually
Solution Approach 2:
The time-consuming manual marking process is replaced with automated computer-based image processing using convolutional neural networks that can analyze and detect reference points across the entire facial image instantaneously
3Measurement precision
If automated feature point detection using machine learning is implemented, then accuracy and speed are improved, but complexity of the system increases
Solution Approach 1:
The system introduces an intermediary machine learning model trained on labeled facial images that acts as a mediator between the input image and the detected reference points, enabling accurate detection without requiring complex manual processing algorithms
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on large datasets of labeled facial images before deployment, so that when the system is used for actual orthodontic diagnosis, the reference points can be detected accurately without requiring complex real-time computations
Data Source
AI summary
Provided is a computing device for supporting medical image analysis. The computing device may include: at least one or more memories for storing a program composed of one or more modules for performing medical image analysis; a communication unit for acquiring a medical image and a facial image for a patient's head; and at least one processor for detecting a plurality of feature points from the facial image through a feature point detection module performing artificial neural network-based machine learning to thus superimpose the medical image and the facial image on top of each other with respect to a plurality of first feature points displayed on the medical image and a plurality of second feature points as at least some of the feature points detected from the facial image, wherein the first feature points are anatomical reference points indicating relative positions of at least one of facial skeleton, teeth, and facial contour.