Embodiments of the application provide methods and devices for analyzing surgeries. It may include recording images of a surgery with a camera, wherein the images may include a visual element chosen from a surgeon's hands during the surgery, a patient's surgery area, equipment used in a surgery, instruments used in a surgery and the like; saving images of a surgery; displaying a timestamp in images; chapterizing the images into different chapters; leveraging additional radiologic imaging clinical data; maximizing treatment cost/benefit; and perhaps even analyzing recorded images of a surgery. Embodiments may use artificial intelligence, computer learning, and machine learning.

A surgical system includes a detector, comprising an array of pixels configured to detect light reflected by a surgical instrument and generate a first signal comprising a first dataset representative of a visible image of the surgical instrument. The surgical system also includes a processor configured to receive the first signal, generate a modified image of the surgical instrument that includes a control panel. The control panel includes one or more control elements representative of one or more operating parameters of the surgical instrument. The processor is further configured to receive an input to the control panel from a user, the input being effective to change one of the operating parameters. The processor is also configured to generate a command signal based on the input to change the one of the operating parameters.

Embodiments of the present invention provide a method for identifying a body position of a detection object in medical imaging, a medical imaging system, and a computer-readable storage medium. The method comprises: receiving an image group by a trained deep learning network, the image group comprising a plurality of pre-scan images in a plurality of directions obtained by pre-scanning a detection object; and outputting body position information of the detection object by the deep learning network.

A solution is proposed for assisting a medical procedure. A corresponding method comprises acquiring a luminescence image (205F), based on a luminescence light, and an auxiliary image (205R), based on an auxiliary light different from this luminescence light, of a field of view (103); the field of view (103) contains a region of interest comprising a target body of the medical procedure (containing a luminescence substance) and one or more foreign objects. An auxiliary informative region (210Ri) representative of the region of interest without the foreign objects is identified in the auxiliary image (205R) according to its content, and a luminescence informative region (210Fi) is identified in the luminescence image (205F) according to the auxiliary informative region (210Ri). The luminescence image (205F) is processed limited to the luminescence informative region (210Fi) for facilitating an identification of a representation of the target body therein. A computer program and a corresponding computer program product for implementing the method are also proposed. Moreover, a computing device for performing the method and an imaging system comprising it are proposed. A medical procedure based on the same solution is further proposed.

A number of improvements are provided relating to computer aided surgery utilizing an on tool tracking system. The various improvements relate generally to both the methods used during computer aided surgery and the devices used during such procedures. Other improvements relate to the structure of the tools used during a procedure and how the tools can be controlled using the OTT device. Still other improvements relate to methods of providing feedback during a procedure to improve either the efficiency or quality, or both, for a procedure including the rate of and type of data processed depending upon a CAS mode.

Information about a drug-containing vessel is determined by capturing image data of the curved surface of a cylindrical portion of a drug-containing vessel. The image data is unfurled from around the curved surface, binarised, and a template matching algorithm employed to determine that the label information comprises candidate information about the vessel and/or the drug.

Systems and methods are provided for processing X-ray images, wherein the methods are implemented as a software program product executable on a processing unit of the systems. Generally, an X-ray image is received by the system, the X-ray image being a projection image of a first object and a second object. The first and second objects are classified and a respective 3D model of the objects is received. At the first object, a geometrical aspect like an axis or a line is determined, and at the second object, another geometrical aspect like a point is determined. Finally, a spatial relation between the first object and the second object is determined based on a 3D model of the first object, a 3D model of the second object, and the information that the point of the second object is located on the geometrical aspect of the first object.

Methods for guiding a surgical procedure include accessing information relating to a surgical procedure, accessing at least one image of a surgical site captured by an endoscope during the surgical procedure, identifying a tool captured in the at least one image by a machine learning system, determining whether the tool should be changed based on comparing the information relating to the surgical procedure and the tool identified by the machine learning system, and providing an indication when the determining indicates that the tool should be changed.

A system may comprise a processor and a memory having computer readable instructions stored thereon. The computer readable instructions, when executed by the processor, may cause the system to record shape data from a shape sensor for an instrument during an image capture period and generate a sensor point cloud from the recorded shape data. The computer readable instructions, when executed by the processor, may also cause the system to receive image data from an imaging system during the image capture period, generate an image point cloud for the instrument from the image data, and register the sensor point cloud to the image point cloud.

It is judged that a relevant tool is lost at a second time point after the elapse of a certain amount of time or longer after a first time point when it is judged that the tool is not recognized at all in images; and regarding the tool which is judged to have been lost, trace data corresponding to heads-up time, which is immediately before the first time point to immediately after the first time point, is read with reference to a corresponding identifier and a movement locus of the tool based on the trace data is displayed on a screen.