Method for examining bone in vivo, comprises obtaining a laser beam; modulating the laser beam to insert therein photoacoustic frequencies including optical frequencies and acoustic frequencies, the acoustic frequencies being able to give rise to acoustic waves; directing the modulated beam at a bone to cause acoustic waves resulting from the beam to travel through the bone; analyzing received signals from the bone including signals resulting from the acoustic waves, to determine a mineral density and a bone quality for said bone, and thus obtain in-vivo data that can be of assistance to a doctor when diagnosing osteoporosis.

The present invention relates to a method for improving the diagnostic accuracy of an artificial intelligence (AI) to diagnose osteoarthritis (OA). The method involves all or some steps of: generating a plurality of feature values from at least one input skeletal image, generating a quantitative Kellgren-Lawrence (KL) grade based on the plurality of feature values, and generating an explanation plot showing the contributions of each feature. The present invention also relates to a method of constructing a non-transitory computer-readable medium to perform the above tasks.

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.

Provided are bone quality and/or strength measuring devices. In some embodiments, the devices include a body and a detector disposed therein designed to interact with a bone, determine the quality and/or strength thereof, and communicate an evaluation thereof to a user. The presently disclosed devices can also include a torque and/or force sensor that facilitates placement of the device and/or that is in communication with the detector to cause the detector to evaluate the bone; an output that feedback to a user based on the quality and/or strength of the bone determined by the detector, and/or a piston that interacts with the detector to cause the detector to interact with and thereby evaluate the bone. Also provided are methods for determining if a region of a bone is appropriate for placement of an orthopedic hardware piece and methods for reducing risk of a complication of spinal surgery associated with failure of an orthopedic hardware piece.

According to some embodiments, systems and methods for reshaping bone are provided. The systems may include an implant body, an actuator coupled to the implant body, a sensor configured to detect a parameter indicative of a biological condition, a transceiver, and a controller. The transceiver may be configured to transmit data associated with the parameter to an external remote control and receive instructions from the external remote control. Finally, the controller is configured to move the actuator in response to the instructions from the external remote control, wherein the actuator adjusts the implant body. The methods may include measuring a parameter indicative of a biological condition; transmitting data associated with the parameter from the implantable device to an external remote control; transmitting instructions from the external remote control to the implantable device; and actuating the bone growth device in response to the instructions from the external remote control.

Techniques are described herein for automated bone mineral density and fracture risk assessment from digital X-rays. A method includes: obtaining image data including information relating to cortical bone tissue of at least a part of a first bone; preprocessing the image data, by isolating a region of interest in the image data, to generate preprocessed image data; applying the preprocessed image data as inputs across a trained machine learning model to generate output indicative of bone mineral density of the first bone; determining a T-score value based on the bone mineral density of the first bone; and providing, on a user interface, an output based on the T-score value.

A method and system are provided to intraoperatively adjust the dimensions of a pre-operatively planned implant cavity to improve implant fit in a bone. The method includes obtaining a preoperative image data set of the bone. A surgical plan is generated using the image data set and/or a three-dimensional (3-D) bone model of the patient's bone generated from the image data set. Intraoperatively, the patient's bone is exposed and registered to the surgical plan and a computer assisted surgical system. The computer assisted surgical system having a cutting tip and a force sensor for sensing actual forces exerted on the cutting tip as an initial cut is created on the bone at a first bone region. Based on the difference between the actual cutting force and the expected cutting force in the plan, the dimensions of the cavity are adjusted accordingly.

A system and method for classifying a structure or material in an image of a subject. The system comprises: a segmenter configured to form one or more segmentations of a structure or material in an image and generate from the segmentations one or more segmentation maps of 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.

An ultrasound device captures skeleton density information. This ultrasound device includes at least two transmitters 100, 102 having a distance of L1 between them for transmitting ultrasound signals into the skeleton, at least two receivers 104 having a distance of L2 between them for receiving ultrasound signals from the skeleton, as the calibrated known distance L is at least one of distances L1 and L2. The ultrasound device also includes a processing unit 101 for calculating a ultrasound velocity in the skeleton for forming skeleton density information on the basis of the ultrasound signals received by the receivers 104, 106 by dividing known distance by an average of a travel time difference for a ultrasound signal between the receivers 104, 106 receiving the ultrasound signal from the first transmitter 100 and the second transmitter 102.

The present invention relates to a device and a method for measuring osseous quality.