The present disclosure discloses a method for drawing a cranial image trajectory. The method comprises: providing, by a processor, at least one trajectory pattern template for parts of a standard cranial anatomy; acquiring, by a radiation image device, at least one cranial image of a patient, superimposing, by the processor, the trajectory pattern template of each part of the standard cranial anatomy onto the corresponding position of the patient's cranial image, and acquiring the trajectory pattern of the cranial image; and if a deviation is determined between the shape of the respective structure on the trajectory pattern of the patient's cranial image and that of the trajectory pattern template of each part of the standard cranial anatomy, adjusting the shape of the corresponding trajectory pattern template in the cranial anatomy so as to be the same as the respective structure in the cranial image by the processor.

A method and system for assessing disc derangement disorders (DDD) in patients comprises an image scanning module, a DDD screening system and a diagnosis and assessment module. The DDD screening system is in communication with the image scanning module via a network. The DDD screening system includes an application server residing on a computer having a processor installed with a disc derangement disorders (DDD) screening application and coupled with a memory unit integrated with a central database. The DDD screening application provides a set of statistical probability data of the at least one image utilizing a normalized measurement of at least one image to a diagnosis and assessment module. The diagnosis and assessment module generates a report that allows the DDD system to evaluate the presence or absence of DDD based on the statistical probabilities.

Dental images are processed according to a first machine learning model to determine teeth labels. The teeth labels and image are concatenated and processed using a second machine learning model to label anatomy including CEJ, JE, GM, and Bone. The anatomy labels, teeth labels, and image are concatenated and processed using a third machine learning model to obtain feature measurements, such as pocket depth and clinical attachment level. The feature measurements, anatomy labels, teeth labels, and image may be concatenated and input to a fourth machine learning model to obtain a diagnosis for a periodontal condition. Feature measurements and/or the diagnosis may be processed according to a diagnosis hierarchy to determine whether a treatment is appropriate. Machine learning models may further be used to reorient, decontaminate, and restore the image prior to processing. A machine learning model may be made resistant to deception by images including added adversarial noise.

Dental images are processed according to a first machine learning model to determine teeth labels. The teeth labels and image are concatenated and processed using a second machine learning model to label anatomy including CEJ, JE, GM, and Bone. The anatomy labels, teeth labels, and image are concatenated and processed using a third machine learning model to obtain feature measurements, such as pocket depth and clinical attachment level. The feature measurements, anatomy labels, teeth labels, and image may be concatenated and input to a fourth machine learning model to obtain a diagnosis for a periodontal condition. Feature measurements and/or the diagnosis may be processed according to a diagnosis hierarchy to determine whether a treatment is appropriate. Machine learning models may further be used to reorient, decontaminate, and restore the image prior to processing. Machine learning models may be embodied as CNN, GAN, and cyclic GAN.

A patient positioning apparatus for an X-ray dental imaging system includes a head rest and a chair separate from and spaced from the head rest. The chair has a seat portion and a back rest coupled to the seat portion. The chair further includes a locking system that locks a position of the back rest relative to the seat portion.

Provided is a dental image registration device comprising: an outermost boundary detection unit for detecting, from first teeth image data, a first outermost boundary region which is the outermost boundary region of dentition, and detecting, from second teeth image data, a second outermost boundary region which is the outermost boundary region of the dentition; and an image registration unit which registers the first and second teeth image data on the basis of a first inscribed circle inscribed within the first outermost boundary region and a second inscribed circle inscribed within the outermost boundary region, or registers the first and second teeth image data on the basis of a first central point of the first outermost boundary region and a second central point of the second outermost boundary region.

Disclosed herein is an apparatus and a method of making the apparatus. The method comprises obtaining a plurality of semiconductor single crystal chunks. Each of the plurality of semiconductor single crystal chunks may have a first surface and a second surface. The second surface may be opposite to the first surface. The method may further comprise bonding the plurality of semiconductor single crystal chunks by respective first surfaces to a first semiconductor wafer. The plurality of semiconductor single crystal chunks forming a radiation absorption layer. The method may further comprise forming a plurality of electrodes on respective second surfaces of each of the plurality of semiconductor single crystal chunks, depositing pillars on each of the plurality of semiconductor single crystal chunks and bonding the plurality of semiconductor single crystal chunks to a second semiconductor wafer by the pillars.

Three dimensional x-ray imaging systems are described in this application. In particular, this application describes a 3D dental intra-oral imaging (3DIO) system that collects a series of 2D image projections. The x-ray imaging system comprises a housing, an x-ray source attached to a articulating or motion gantry configured to move the source within the housing to multiple positions, an x-ray detector array located on an opposite side of an object to be imaged from the x-ray source, where the detector array is synchronized with the x-ray source to capture 2D images of the object when the x-ray source is located in multiple imaging positions, and a processor configured to accept the 2D images and reconstruct a 3D image. The multiple imaging positions can be located on a plane substantially parallel to the x-ray detector array. Other embodiments are described.

Methods and systems for determining an orthodontic treatment for a patient are provided. The method comprises: receiving image data associated with a patient's skull; conducting, based on the image data, a cephalometric analysis of the patient's skull; identifying, via the cephalometric analysis, a first pair of reference points; identifying, via the cephalometric analysis, a second pair of reference points; generating based on the first and second pairs of reference points a first reference line and the second reference line, respectively; determining, based on an intersection of the first reference line and the second reference line, a rotation center of a patient's mandible; and based on the rotations center, determining the orthodontic treatment for the patient.

In embodiments, a first 3D model of a patient's teeth based on first intraoral scan data taken at a first time is processed to recognize the teeth in the first 3D model. A second 3D model of the patient's teeth based on second intraoral scan data taken at a second time is processed to recognize the teeth in the second 3D model. The first 3D model is compared to the second 3D model and a plurality of AOIs in the second virtual 3D model representative of at least one of tooth wear, tooth breakage, tooth movement, gingival recession or gingival swelling are determined based on the comparison. The teeth recognized in the first 3D model are compared to the teeth recognized in the second 3D model, and one or more AOIs in the second virtual 3D model representative of tooth wear or tooth breakage are determined based on the comparison.