Surgical Safety Technologies Inc.

Sst Canada Inc.

Teodor Pantchev Grantcharov

Kevin Lee Yang

Frank Rudzicz

Babak Taati

Tomas Jaan Saun

Amar S Chaudhry

Yichen Zhang

Haiqi Wei

Juliana De La Vega Fernandez

Peter Dupont Grantcharov

Shuja Khalid

Mitchell Geoffrey Goldenberg

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.

System and method for biometric data capture for 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.

Operating room black-box device, system, method and computer readable medium for event and error prediction

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 adverse event detection or severity estimation from surgical data

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.

System and method for surgical performance tracking and measurement

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.

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Stouffville, Canada

Teodor Pantchev Grantcharov