The present disclosure is directed to methods and apparatus for monitoring bone characteristics for various conditions such as osteoporosis and/or periodontitis. In various embodiments, a dental hygiene appliance (100) may include: a handle (102) adapted to be held by a user; a tool (106) secured to the handle to perform a dental hygiene-related task; emitter(s) (114A, 116, 360A) mounted on the dental hygiene appliance to emit wave(s) (252, 362) towards a mandible (250, 350) of the user; sensor(s) (114B, 116, 360B) mounted on the dental hygiene appliance to detect wave(s) (254, 364) propagating away from the mandible, wherein the detected wave(s) originate from or are caused by the emitted wave(s); and a controller (108) communicatively coupled with the emitter(s) and the sensor(s). The controller may: receive, from the sensor(s), signal(s) indicative of the detected waves; and determine, based on the signal(s), bone characteristic(s) of the user.

A dental implant for supporting periodontal tissue and the supporting bone is provided. The dental implant includes an implant member with inner canal for insertion into a periodontal bone socket, and an anchoring assembly. The anchoring assembly includes a first fastening element and radially equidistant cylindrical members. The first fastening element engages the implant member within the hollow axial cavity. The root section includes through-holes for radially and forcibly sliding the cylindrical members through them. When the first fastening element apically advances within the hollow axial cavity, the cylindrical members generate an anchoring force to anchor the dental implant.

A system and method for classifying a structure or material in an image of a subject. The system comprises: a segmenter configured to segment an image into one or more segmentations that correspond to respective structures or materials in the image, and to generate from the segmentations one or more segmentation maps of the image (each of the segmentation maps representing the image) including categorizations of pixels or voxels of the segmentation maps assigned from one or more respective predefined sets of categories; a classifier that implements a classification machine learning model configured to generate, based on the segmentations maps, one or more classifications and to assign to the classifications respective scores indicative of a likelihood that the structure or material, or the subject, falls into the respective classifications; and an output for outputting a result indicative of the classifications and scores.

Parametric model based computer implemented methods for determining the stiffness of a bone, systems for estimating the stiffness of a bone in vivo, and methods for determining the stiffness of a bone. The computer implemented methods include determining a complex compliance frequency response function Y(f) and an associated complex stiffness frequency response function H(f) and fitting a parametric mathematical model to Y(f) and to H(f). The systems include a device for measuring the stiffness of the bone in vivo and a data analyzer to determine a complex compliance frequency response function Y(f) and an associated complex stiffness frequency response function H(f). The methods for determining the stiffness include fitting a parametric model to stiffness of the skin-bone complex as a function of frequency H(f) and the compliance of the skin-bone complex as a function of frequency Y(f).

A method of estimating bone mineral density according to at least one embodiment of the present disclosure includes receiving one or more images of an anatomical element; generating, based on the one or more images of the anatomical element, a three-dimensional mask for the anatomical element; generating, based on the three-dimensional mask for the anatomical element, a transformed three-dimensional mask for at least a portion of the anatomical element; filtering the one or more images of the anatomical element with the transformed three-dimensional mask for the at least a portion of the anatomical element; and determining, based on the filtering, a bone mineral density for the at least a portion of the anatomical element.

A console for medical diagnosis includes a chair, a computer for displaying and communicating test results, various testing areas for performing multiple diagnostic tests, various testing devices including at least an EEG testing device, an ECG testing device, a BMD testing device, an ultrasonography testing device, and an EMG testing device, and openings for kidney probes and an echocardiogram probe. The testing areas include a first area for performing diagnostic tests on the head, a second area for performing diagnostic tests on sensory, a third area for performing diagnostic tests on the chest region, a fourth area for performing diagnostic tests on the pelvic and chest regions, a fifth area for performing diagnostic tests on blood, tissue, and bodily fluids, a sixth area for performing electromyographical tests, a seventh area for performing bone-related diagnostic tests, and an eighth area for performing diagnostic tests related to physical parameters and vitals.

Provided is an apparatus for measuring the thickness, roughness, and morphology index of the mandibular cortical bone using a dental panorama image to assist in the diagnosis of osteoporosis, wherein the thickness, roughness, and morphological index of the cortical bone is measured more accurately and the diagnosis of osteoporosis can be supported more accurately. An osteoporosis diagnostic support apparatus, wherein the apparatus has a contour extraction unit adapted to extract a mandibular contour from an image of a mandibular cortical bone photographed by a photographic apparatus adapted to photograph the mandibular cortical bone and surroundings thereof, a line segment extraction unit adapted to extract line segments from the image of the mandibular cortical bone photographed by the photographic apparatus; and a cortical bone thickness calculation unit adapted to calculate a thickness of the cortical bone based on the extracted mandibular contour and line segments.

A bone density measuring apparatus and a bone density imaging method capable of improving the accuracy of a bone density analysis are provided. In a state in which no subject is present, a detector detects X-rays emitted from an X-ray tube under a high tube voltage X-ray condition/a low tube voltage X-ray condition and a first gain correction map/a second gain correction map is generated (S1, S2). A detector detects the X-rays emitted from an X-ray tube and transmitted through a subject under a high tube voltage X-ray condition/a low tube voltage X-ray condition, and a high voltage image/a low voltage image captured by the detector is generated (S3). By performing a gain correction of the high voltage image using the first gain correction map, performing a gain correction of the low voltage image using the second gain correction map (S4), and performing a subtraction of the high voltage image after the gain correction and the low voltage image after the gain correction (S5), the accuracy of the bone density analysis can be improved.

A bone density estimating method, comprising: acquiring, by an MR scanning device, a magnetic resonance, MR, sequence of a body portion, wherein the MR sequence comprises quantitative information of the body portion; generating, by a processing circuit, an MR image of the body portion based on the MR sequence, wherein each voxel of the MR image represents a volume of the body portion; identifying, by the processing circuit, a part of the MR image representing a bone portion of the body portion; for a voxel of the identified part of the MR image, estimating a bone density of a volume of the bone portion represented by the voxel, based on a quantitative value of the voxel. The quantitative information of the body portion comprises a proton density.

Various systems and methods are provided for surgical and interventional planning, support, post-operative follow-up, and functional recovery tracking. In general, a patient can be tracked throughout medical treatment including through initial onset of symptoms, diagnosis, non-surgical treatment, surgical treatment, and recovery from the surgical treatment. In one embodiment, a patient and one or more medical professionals involved with treating the patient can electronically access a comprehensive treatment planning, support, and review system. The system can provide recommendations regarding diagnosis, non-surgical treatment, surgical treatment, and recovery from the surgical treatment based on data gathered from the patient and the medical professional(s). The system can manage the tracking of multiple patients, thereby allowing for data comparison between similar aspects of medical treatments and for learning over time through continual data gathering, analysis, and assimilation to decision-making algorithms.