Conventionally, systems and methods have been provided for manual annotation of anatomical landmarks in digital radiography (DR) images. Embodiments of the present disclosure provides system and method for anatomical landmark detection and identification from DR images containing severe skeletal deformations. More specifically, motion artefacts and exposure are filtered from an input DR image to obtain a pre-processed DR image and probable/candidate anatomical landmarks comprised therein are identified. These probable candidate anatomical landmarks are assigned a score. A subset of the candidate anatomical landmarks (CALs) is selected as accurate anatomical landmarks based on comparison of the score with a pre-defined threshold performed by a trained classifier. Position of remaining CALs may be fine-tuned for classification thereof as accurate anatomical landmarks or missing anatomical landmarks. The CALs may be further fed to the system for checking misalignment of any of the CALs and correcting the misaligned CALs.

A method for providing a foraminal stenosis ratio using 3-dimensional CT includes (a) transmitting a spine image of a patient to an information extracting unit by an image capturing unit in response to an input signal transmitted from an input unit; (b) extracting spine boundary information and neural foramen area information based on a pixel value of the spine image by the information extracting unit; (c) storing the spine boundary information and the neural foramen area information by an information storing unit; (d) calculating the foraminal stenosis ratio by using the spine image, the spine boundary information, and the neural foramen area information by an information calculating unit; and (e) outputting maximum neural foramen area information of the neural foramen area information, a neural foramen angle for the maximum neural foramen area information, and the foraminal stenosis ratio by an output unit.

Systems, devices, and methods for identifying a region of interest are provided. A plurality of skeletal landmarks may be identified from an image received from an imaging device. A pose of a patient may be determined based on the plurality of skeletal landmarks. A region of interest may be identified on the patient based on the determined pose. Instructions may be automatically provided to the controller to adjust a pose of a surgical instrument relative to the region of interest. The plurality of skeletal landmarks may be tracked for movement. The region of interest may be updated when movement of the plurality of skeletal landmarks is detected.

A system for automated comprehensive assessment of clinical lumbar MRIs includes a MRI standardization component that reads MRI data from raw lumbar MRI files, uses an artificial intelligence (AI) model to convert the raw MRI data into a standardized format. A core assessment component automatically generates MRI assessment results, including multi-tissue anatomical annotation, multi-pathology detection and multi-pathology progression prediction based on the structured MRI data package. The core assessment component contains a semantic segmentation module that utilizes a deep learning artificial intelligence (AI) model to generate an MRI assessment results that contains multi-tissue anatomical annotation, a pathology detection module to generate multi-pathology detection, and a pathology progression prediction module to generate multi-pathology progression prediction. A model optimization component archives clinical MRI data and MRI assessment results based on comments provided by a specialist, and periodically optimizes the AI deep learning model of the core assessment component.

A method of extracting and displaying postural measurements from patient data includes retrieving, by a processor of a computing device, the patient data from memory. The patient data includes a geometric mesh representation of a patient, including a plurality of data points corresponding to spatial coordinates of a plurality of vertices in three dimensions. The method also includes determining, by the processor, a reference geometry along the geometric mesh representation in a fixed position with respect to the spatial coordinates; determining, by the processor, a landmark corresponding to one of skeletal or soft tissue anatomy for the patient; and determining, by the processor, a postural deviation of a body portion of the patient by comparing the reference geometry and the landmark. The method further includes displaying, by a display of the computing device, a graphical user interface indicating a characteristic related to the postural deviation.

A system and method for determining patient-specific anatomical parameters to improve surgical outcomes. Some embodiments include processes for predicting the parameters of occluded anatomy. Some embodiment includes processes for more accurately identifying a center point of a ball and socket joint, such as a center point or center of rotation of a femoral head. Some embodiments include processes for identify a patient-specific spinal curvature, including more precisely determining patient specific spinal inflection points. The various steps can be performed automatically through trained computing devices and graphically presented to a surgeon for review and any necessary modifications.

A method for obtaining a spatial pattern of an anatomical structure of a subject includes comprising the steps of a) acquiring, from at least one digital image capturing device, at least one uncalibrated image of a calibration reference applied on a surface configured to receive the subject, the calibration reference having at least one known dimension and defining at least one known direction; b) defining an absolute calibrated reference system of three coordinates based on the calibration reference depicted in the at least one uncalibrated image; and c) acquiring, from the at least one digital image capturing device, at least a first and at least a second calibrated image of a plurality of markers applied on a corresponding plurality of body landmarks of the anatomical structure of the subject at respective contact points with the body landmarks, the plurality of markers being arranged within the absolute calibrated reference system,

Systems and methods are provided to plan a spinal correction surgery. The method includes measuring parameters of a spine in a two-dimensional (2D) spinal image including a thoracic Cobb angle and a thoracic kyphosis (TK) and transforming the 2D image to a three-dimensional (3D), spinal image representation. The transforming includes performing segmentation of spine elements in the 2D image, and applying a formula based on the thoracic Cobb angle and the TK to the spine elements. The method includes identifying a TK goal having a post-operative TK value to selected spine elements, transforming a gap of the spine elements representative of a difference between the pre-operative TK in 3D spinal image representation and the TK goal to create a 3D post-operative spinal image representation, and determining a first rod design based on the 3D post-operative spinal image representation to achieve the post-operative TK value in the spine elements.

A computer system receives readings from sensors embedded in a spinal implant implanted in a patient during surgery. The sensor readings are indicative of a load applied by a spine of the patient on the spinal implant. The load causes physical discomfort to the patient. A feature vector is extracted from the implant sensor readings using a machine learning module. The feature vector is indicative of the physical discomfort caused by the load. Electrical signals are generated using the machine learning module based on the feature vector. The machine learning module is trained based on patient data sets to generate the electrical signals to balance the load, such that the physical discomfort is reduced. The electrical signals are transmitted to one or more actuators embedded in the spinal implant to cause the one or more actuators to configure the spinal implant, such that the load is balanced.

In certain embodiments, the systems, apparatus, and methods disclosed herein relate to robotic surgical systems with built-in navigation capability for patient position tracking and surgical instrument guidance during a surgical procedure, without the need for a separate navigation system. Robotic based navigation of surgical instruments during surgical procedures allows for easy registration and operative volume identification and tracking. The systems, apparatus, and methods herein allow re-registration, model updates, and operative volumes to be performed intra-operatively with minimal disruption to the surgical workflow. In certain embodiments, navigational assistance can be provided to a surgeon by displaying a surgical instrument's position relative to a patient's anatomy. Additionally, by revising pre-operatively defined data such as operative volumes, patient-robot orientation relationships, and anatomical models of the patient, a higher degree of precision and lower risk of complications and serious medical error can be achieved.