A surgical instrument detection system is provided that can determine the kinds of surgical instruments without special processing, such as application of an optically readable symbol, to the surgical instruments. A surgical instrument detection system 100 includes: an image input section 31 to input an image taken by a camera 1; an object extraction section 32 to clip an object image of a small steel article from the input image; a determination section 33 to input the object image to a learned classification model 331 and determine a kind of the small steel article based on features included in the object image; and an output image generation section 34 to generate an image representing the result of determination by the determination section and output such image to a monitor 2.

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.

An image processing device performs projection conversion that makes an image captured of an object to be recognized closer to a normal image captured from front of the object to be recognized based on a correlation between: a pre-specified plurality of feature ranges dispersed within a range of the object to be recognized; and a plurality of feature ranges designated based on the dispersion in the image.

Aspects of the present disclosure related to a computer system and method for analyzing whether a package has been opened. The system includes a memory and one or more processors communicatively coupled to the memory and can be configured to receive an image corresponding to a wrapped package. The one or more processors can also be configured to generate a set of feature values based on a visual representation of the wrapped package in the image. The one or more processors can also be configured to apply the set of feature values to at least one model that is trained based at least in part on a set of images that include the wrapped package having the status, to determine the status of the wrapped package.

Systems and methods for analyzing complexity of surgical footage are disclosed. A system may include at least one processor configured to analyze frames of the surgical footage to identify an anatomical structure in a first set of frames. The processor may access first historical data based on an analysis of first frame data captured from a first group of surgical procedures and analyze the first set of frames to determine a first complexity level. The processor may analyze the surgical footage to identify in a second set of frames a medical tool, an anatomical structure, and an interaction between the medical tool and the anatomical structure. The processor may access second historical data based on an analysis of a second frame data captured from a second group of surgical procedures and analyze the second set of frames to determine a second complexity level associated with the second set of frames.

A medical information processing device according to an embodiment includes processing circuitry configured to acquire a plurality of X-ray images including a device inserted into a body of a subject, suppress movement of a characteristic portion characterized in a shape that is positioned distant from a distal end of the device among the X-ray images, and output the X-ray images in which movement of the characteristic portion is suppressed.

Disclosed herein are methods for identifying polyps or lesions in a colon. In some variations, computer-implemented methods for polyp detection may be used in conjunction with an endoscope system to analyze the images captured by the endoscopic system, identify any polyps and/or lesions in a visual scene captured by the endoscopic system, and provide an indication to the practitioner that a polyp and/or lesion has been detected.

Systems and methods for predicting post-discharge risk are disclosed. A system may include at least one processor configured to access frames of video captured during a specific surgical procedure on a patient and access stored historical data identifying intraoperative events and associated outcomes. The processor may analyze the accessed frames and, based on information obtained from the historical data, identify in the accessed frames at least one specific intraoperative event. The processor may further determine, based on information obtained from the historical data and the identified at least one intraoperative event, a predicted outcome associated with the specific surgical procedure, and output the predicted outcome in a manner associating the predicted outcome with the patient.

Apparatus and methods are described including inserting a tool into a blood vessel, and, while the tool is within the blood vessel, acquiring an extraluminal image of the blood vessel. In the extraluminal image of the blood vessel, a location of a portion of the tool with respect to the blood vessel is detected automatically. In response to detecting the location of the portion of the tool, a target portion of the blood vessel that is in a vicinity of the portion of the tool is designated automatically. Using the extraluminal image, quantitative vessel analysis is performed on the target portion of the blood vessel. Other embodiments are also described.

Systems and methods for planning the position of surgical hardware to be robotically implanted in a subject. The system extracts information about the planned position of hardware from an operative plan based on preoperative images, and converts this information into mathematical vectors. Intraoperatively, at least one three-dimensional scan of the operative site is obtained. The intraoperative images are processed by image analysis, to which are applied artificial intelligence algorithms for feature identification. The vectors derived from the preoperative plan are superimposed on identified anatomical features from the processed intraoperative images. The surgical plan can then be updated intraoperatively, taking into account any shift in position of the anatomical features between the preoperative images and the intraoperative images, prior to robotic insertion of the hardware.