Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.