Systems and methods for calculating external bone loss for alignment of pre-diseased joints comprising: generating a three-dimensional (“3D”) computer model of an operative area from at least two two-dimensional (“2D”) radiographic images, wherein at least a first radiographic image is captured at a first position, and wherein at least a second radiographic image is captured at a second position, and wherein the first position is different than the second position; identifying an area of bone loss on the 3D computer model; and applying a surface adjustment algorithm to calculate an external missing bone surface fitting the area of bone loss.

An X-ray image processing method, including obtaining a first X-ray image of an object including a plurality of materials including a first material and a second material; obtaining a first partial image generated by imaging the first material and a second partial image generated by imaging the first material overlapping the second material from the first X-ray image; obtaining first information related to a stereoscopic structure of the first material, based on the first partial image included in the first X-ray image; and obtaining second information about the second material based on the first information and the second partial image.

A patient-specific registration jig for registering an orthopaedic surgical instrument with a bony anatomy of a patient includes a head and an adaptor coupled to the head. The head includes a patient-specific contact surface configured to contact a portion of the patient's bony anatomy such that the head can be coupled to the patient's bony anatomy in a unique position. The adaptor includes an elongated shank having a first end coupled to the head and a second end and an adaptor end attached to the second end of the elongated shank. The adaptor end is configured to be received by a clutch of the orthopaedic surgical instrument. A method for registering the orthopaedic surgical instrument using the patient-specific registration jig is also disclosed.

A method for pre-operative orthopedic planning includes obtaining only a high-resolution knee-joint scan of a patient, determining hip rotation center and ankle rotation center from anthropometric data based on personal data of the patient, and determining a mechanical axis of the knee joint based on the anthropometric data. The method also includes preparing at least a two-dimensional image model of the knee joint using the knee-joint scan and the determined mechanical axis, and preparing a pre-operative surgical plan based on the image of the knee joint.

A system and method for analyzing images to optimize orthopedic functionality at a site within a patient, including obtaining at least a first, reference image of the site, or a corresponding contralateral site, the first image including at least a first anatomical region or a corresponding anatomical region. At least a second, intra-operative results image of the site is obtained. At least one point is selected to serve as a reference for both images during analysis including at least one of scaling, calculations, and image comparisons.

Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.

A surgical system can be configured to obtain image data of a joint that comprises at least a portion of a humerus; segment the image data to determine a shape for a diaphysis of the humerus; based on the determined shape of the diaphysis, determine an estimated pre-morbid shape of the humerus; based on the estimated shape of the humerus, identify one or more bone fragments in the image data; and based on the identified bone fragments in the image data, generate an output.

Methods and apparatus for treating a patient. The method includes acquiring a plurality of radio frequency (RF) signals with an ultrasound transducer, each RF signal representing one or more return echoes from a scan line of a pulse-mode echo ultrasound scan. A position of the ultrasound transducer corresponding to each of the acquired RF signals is determined, and a plurality of contour lines generated from the plurality of RF signals. The method estimates a 3-D shape and position of an anatomical feature, such as a joint of patient based on the generated contour lines and corresponding ultrasound transducer positions. An apparatus, or computer includes a processor and a memory with instructions that, when executed by the processor, perform the aforementioned method.

Systems and methods of detecting a presence of opacification or pneumatization in skeletal structures of patients are disclosed. The systems and methods include receiving images, processing the images using a convolutional neural network, and generating, with the convolutional neural network, an opacification score for the image. Systems and methods include training the convolutional neural network to delineate skeletal structure pixels within a computed tomography scan image and to generate an intensity value for each skeletal structure pixel within a computed tomography scan image to determine an opacification score for the computed tomography scan image.

A system for tracking at least one object in computer-assisted surgery may include a processing unit and a non-transitory computer-readable memory communicatively coupled to the processing unit and comprising computer-readable program instructions executable by the processing unit for: obtaining orientation data from at least one inertial sensor unit on at least one object; concurrently obtaining position and orientation data for a robot arm relative to a frame of reference; registering the at least one object with the robot arm to determine a position of the at least one object in the frame of reference; and continuously tracking and outputting the position and orientation of the at least one object in the frame of reference, using the orientation data from the at least one inertial sensor unit on the at least one object and the position and orientation data for the robot arm.