Disclosed are systems and methods for rapid generation of simulations of a patient's spinal morphology that enable pre-operative viewing of a patient's condition and to assist surgeons in determining the best corrective procedure and with any of the selection, augmentation or manufacture of spinal devices based on the patient specific simulated condition. The simulation is generated by morphing a generic spine model with a three-dimensional curve representation of the patient's particular spinal morphology derived from existing images of the patient's condition.

A diagnosis support system includes a calculator configured to calculate position information indicating a positional relationship between a biological sensor and a predetermined region of a measurement target; and an extractor configured to extract, from biological information already diagnosed, biological information that is associated with position information, which is similar to the position information calculated by the calculator.

A method, applied to an apparatus for mammographic multi-view mass identification, includes receiving a main image, a first auxiliary image, and a second auxiliary image. The main image and the first auxiliary image are images of a breast of a person, and the second auxiliary image is an image of another breast of the person. The method further includes detecting the nipple location based on the main image and the first auxiliary image; generating a first probability map of the main image based on the main image, the first auxiliary image, and the nipple location; generating a second probability map of the main image based on the main image, the second auxiliary image, and the nipple location; and generating and outputting a fused probability map based on the first probability map and the second probability map.

A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

Disclosed is a computer-implemented method of overlaying a representation of a medical instrument over a two-dimensional medical image. It finds at least one feature point along a detection line which is defined relative to the medical instrument in the medical image, calculates a geometrical quantity based on this feature point and adds the geometrical quantity to the two-dimensional medical image.

Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

A system includes a processor circuit configured for communication with an external imaging device. The processor circuit receives, from the external imaging device, an image comprising a blood vessel within a patient. The processor circuit determines, using the image, a first location of the blood vessel with a restriction in blood flow caused by compression of the blood vessel by an anatomical structure within the patient and different than the blood vessel. The processor circuit generates a first graphical representation associated with the restriction. The processor circuit outputs, to a display in communication with the processor circuit, a screen display. The screen display includes the image and the first graphical representation at the first location of the blood vessel in the image.

A patient-specific assessment of fracture probability for the femur proximal end is provided. 3D locations of the femur head center, a point on the femoral shaft center, and the femoral intercondylar notch are determined from a clinical image. A frontal plane, a perpendicular thereunto and a bone shaft axis are determined from the 3D locations. An FEA coordinate system is defined from the frontal plane, the perpendicular and the axis. Two FEA analyses are performed, one for neck fracture and one for pertrochanteric fracture, with the same displacement constraints and the same load magnitude but different load angles. The femur proximal end is divided into four anatomically-based regions. For each region and each load, maximum tensile and compressive principal strains are determined and, based on the body weight and the principal strains, a likelihood of fracture is obtained. The minimum of these 8 likelihoods gives the probability of fracture.

A method for identifying a misidentified study can utilize a set of photographs captured at substantially the same time as a corresponding set of medical images. The method can include determining similarities between the photographs through machine learning models and determining that a misidentified study exists when the similarity between the photographs fails to satisfy a threshold similarity.

A method comprises: applying a first trained function to first input data to generate first output data, the first output data including first key elements; receiving second input data, the second input data being an X-ray image of an examination region acquired using a first collimation region; applying a second trained function to the second input data to generate second output data, the second output data including second key elements; receiving third input data in response to an incomplete set of second key elements, the third input data including the second key elements and an X-ray image of the examination region acquired using the first collimation region; applying a third trained function to the third input data to generate third output data, the third output data including an estimated third key element to complete the set of second key elements; and providing a complete set of second key elements.