Intellectual Property
Patents
Surgical Safety Technologies’ intellectual property exemplifies our commitment to innovation. The following list includes the patents applied to our solutions.
Operating room black-box device, system, method and computer readable medium for error and event prediction
A multi-channel recorder/encoder for collecting, integrating, synchronizing and recording medical or surgical data received as independent live or real-time data streams from a plurality of hardware units. The medical or surgical data relating to a live or real-time medical procedure. Example hardware units include a control interface, cameras, sensors, audio devices, and patient monitoring hardware. Further example systems may include a cloud based platform incorporating the encoder.
System and method for biometric data capture for event prediction
A computer implemented system for automatically recording and generating predictive outputs relating to a medical procedure is described. The system is augmented with biometric sensory data from a biometric sensor coupled to a body of a healthcare practitioner. The biometric sensory data is processed to obtain one or more time-synchronized data objects providing a proxy to an estimated stress level associated with the healthcare practitioner, and the one or more time-synchronized data objects are utilized to identify abnormality-related durations of time encapsulated in the form of time-based metadata tags. The time-based metadata tags are utilized to automatically modify characteristics of the recording or generating of predictive outputs to temporarily consume more computational processing resources during the abnormality-related durations of time.
Hierarchical cnn-transformer based machine learning
Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians. Further, text medical notes may contain information not immediately available in structured variables. Applicants propose a hierarchical CNN-Transformer model with an explicit attention mechanism as an interpretable, multi-task clinical language model.
System and method for adverse event detection or severity estimation from surgical data
Embodiments described herein may provide devices, systems, methods, and/or computer readable medium for adverse event detection and severity estimation in surgical videos. The system can train multiple models for adverse detection and severity estimation. The system can load selected models for real-time adverse event detection and severity estimation.
System and method for surgical performance tracking and measurement
Computer implemented methods and systems are provided for training a machine learning architecture for surgical performance tracking and measurement based on surgical procedure video data set. The methods and systems include, in a first aspect, a sequential relation architecture and a dimensionality reduction architecture. In a second aspect, the methods and systems include a surgical instrument instance segmentation architecture, a decomposition model, and a sequential relation architecture. The video data is processed on a frame level to generate compressed or reduced representations of the video data.
Body-mounted or object-mounted camera system
An object or body-mounted camera apparatus for recording surgery is provided that is adapted for tracking a relevant visual field of an on-going operation. To help maintain visibility and/or focus of the visual field, specific machine learning approaches are proposed in combination with control commands to shift a physical positioning or a perspective of the camera apparatus. Additional variations are directed to tracking obstructions based on the visual field of the camera, which can be utilized for determining a primary recording for use when there are multiple cameras being used in concert.
Operating room black-box device, system, method and computer readable medium
A multi-channel recorder/encoder for collecting, integrating, synchronizing and recording medical or surgical data received as independent live ware units. The medical or surgical data relating to a live or real-time medical procedure. Example hardware units include a control interface, cameras, sensors, audio devices, and patient monitoring hardware. Further example systems may include a cloud based platform incorporating the encoder.
Systems and methods for surgical video de-identification
An improved approach is described herein wherein an automated de-identification system is provided to process the raw captured data. The automated de-identification system utilizes specific machine learning data architectures and transforms the raw captured data into processed captured data by modifying, replacing, or obscuring various identifiable features. The processed captured data can include transformed video or audio data.
System and method for operating room human traffic monitoring
Systems and methods for traffic monitoring in an operating room are disclosed herein. Video data of an operating room is received, the video data captured by a camera having a field of view for viewing movement of a plurality of individuals in the operating room during a medical procedure. An event data model is stored, the model including data defining a plurality of possible events within the operating room is stored. The video data is processed to track movement of objects within the operating room, the objects including at least one body part, and the processing using at least one detector trained to detect a given type of the objects. A likely occurrence of one of the possible events is determined based on the tracked movement.
Systems and methods for configuring and operating de-identification systems
Systems, devices, and methods of configuring and operating a de-identification system are provided. At least one electronic data model is maintained, which includes a plurality of de-identification requirements for removing personal information from clinical data obtained in a clinical environment, the plurality of de-identification requirements including requirements applicable to at least one jurisdiction of a plurality of jurisdictions. An identifier identifying one of the jurisdictions is received. The data model is traversed to determine a subset of the de-identification requirements applicable to the identified jurisdiction. Data defining a user selection of operating parameters of the de-identification system are received. The user selection is analyzed for conformity with the subset of de-identification requirements. In response to determining the user selection conforms with the subset of de-identification requirements, a signal is generated indicating the conformity. A configuration data structure is generated including data reflective of the user selection of operating parameters.
