Artificial intelligence-based maxillary sinus floor sclerotin classification method and system
A technology of artificial intelligence and classification method, applied in the fields of medical data mining, 2D image generation, image data processing, etc., can solve problems such as lack of resources for dental implant doctors, achieve strong scalability, accurate classification models, and reduce data missing. Effect
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Embodiment 1
[0037] Such as figure 1 , a method for classifying maxillary sinus floor bone based on artificial intelligence, comprising the following steps:
[0038] S1. Collect the patient's maxillary sinus floor bone information and clinical existing bone classification information, and perform noise reduction, normalization, and statistical processing on the patient's maxillary sinus floor bone information;
[0039] S2. Perform feature extraction on the processed patient's maxillary sinus floor bone information;
[0040] S3. Train the machine learning model on the sample data, and classify the sample data to obtain the maxillary sinus floor bone classification model;
[0041] Wherein, the sample data training method specifically includes the following sub-steps:
[0042] S301. Vectorizing the data key features of cone-beam CT computed tomography information and clinical existing bone classification information, and transforming them into feature points in a high-dimensional space;
...
Embodiment 2
[0052] On the basis of embodiment 1, this embodiment proposes a kind of maxillary sinus floor bone classification system based on artificial intelligence, comprising:
[0053] A data acquisition unit, the data acquisition unit includes an image acquisition module for reading information from the cone-beam CT computed tomography scan of the patient's maxillary sinus floor bone and a classification category data acquisition module for collecting clinical existing bone classification information ;
[0054] A data labeling unit, the data labeling unit includes a manual labeling module for the doctor to manually label the bone tissue of the target implant area and a label storage module for storing the labeling data manually marked by the doctor;
[0055] Further, the data training unit includes a data transport module for extracting bone characteristics of the patient's maxillary sinus floor and a mapping operation module for establishing a mapping between the labeled data and the...
Embodiment 3
[0062] On the basis of Embodiments 1 and 2, this embodiment proposes a method for classifying the bone of the maxillary sinus floor based on artificial intelligence and the principle flow of the system, as follows:
[0063] First, collect maxillary sinus floor bone data; second, use cone-beam CT computed tomography feature extraction based on the quality-controlled maxillary sinus floor bone data; third, combine the existing types of maxillary sinus floor bone , train the machine learning model on the sample data, and obtain the maxillary sinus floor bone classification model.
[0064] Preferably, the homogeneous type shows no significant difference in grayscale between the cortical bone of the maxillary sinus floor and the cancellous bone in the center of the alveolar process in the CBCT, and there is no significant difference in the gray level between the cortical bone of the alveolar crest and the cancellous bone in the central alveolar process.
[0065] Preferably, in the ...
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