Method for deriving cephalometric parameters for orthodontic diagnosis based on machine learning from three-dimensional (3D) cbct image taken in natural head position
AssigneeAinsight Inc.
InventorJang Hoon Ahn
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Abstract
The present invention relates to a method for deriving cephalometric parameters for orthodontic diagnosis based on machine learning. More specifically, the present invention relates to a method for deriving cephalometric parameters for orthodontic diagnosis based on machine learning, wherein a machine-learning algorithm is applied after acquiring a 3D cone-beam computer tomography (CBCT) image of a subject data taken in a natural head position, and a plurality of cephalometric landmarks can be precisely and rapidly digitized on the 3D CBCT image in order to derive 13 diagnosis parameters for precise orthodontic diagnosis.
Claims20
A method of deriving cephalometric parameters for orthodontic diagnosis based on machine learning, the method comprising:
- obtaining 3D cone beam computed tomography (CBCT) images for orthodontic diagnosis including a CBCT image in a sagittal plane, a CBCT image in a coronal plane, a dental panoramic image, and an incisor image in a cross-sectional view, respectively, for a patient from 3D CBCT image data captured of the patient's head at a natural head position;
- detecting, based on a machine learning algorithm, a plurality of cephalometric landmarks on the 3D CBCT images to derive 13 parameters for orthodontic diagnosis;
- and
- deriving 13 parameters corresponding to distances or angles between the detected plurality of cephalometric landmarks,
- wherein at least one of the 13 parameters is derived using one of a nasion true vertical plane (NTVP) and a true horizontal plane (THP) among the plurality of cephalometric landmarks,
- wherein the NTVP is a vertical plane passing through a nasion, the THP is a horizontal plane passing through the nasion, and the nasion is a most concave point between a frontal bone and a nasal bone.
A non-transitory computer readable recording medium storing thereon a computer program, which when executed by a computer, performs a method of deriving cephalometric parameters for orthodontic diagnosis based on machine learning, the method comprising:
- obtaining 3D cone beam computed tomography (CBCT) images for orthodontic diagnosis including a CBCT image in a sagittal plane, a CBCT image in a coronal plane, a dental panoramic image, and an incisor image in a cross-sectional view, respectively, for a patient from 3D CBCT image data captured of the patient's head at a natural head position;
- detecting a plurality of cephalometric landmarks on the 3D CBCT images based on a machine learning algorithm;
- and
- deriving 13 parameters corresponding to distances or angles between the detected plurality of cephalometric landmarks,
- wherein at least one of the 13 parameters is derived using one of a nasion true vertical plane (NTVP) and a true horizontal plane (THP) among the plurality of cephalometric landmarks,
- wherein the NTVP is a vertical plane passing through a nasion, the THP is a horizontal plane passing through the nasion, and the nasion is a most concave point between a frontal bone and a nasal bone.
Dependencies are inferred from the claim text.