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: 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: 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: A system for detecting and responding to emergency events includes a plurality of local emergency detection and response units positioned in a local area. Each unit includes one or more local sensing agents and a local detection manager. Each local sensing agent is operable to detect emergency events by a change in a given emergency factor in the local area and to convey data representative of the change in the emergency factor to a detection manager which is operable to receive the data and to assign a value to the emergency factor according to the data. A central location controller unit and/or the local emergency detection and response unit are operable for classifying the assigned value of the emergency function to form an assigned value classification and for initiating the local emergency event response agent to implement a response protocol according to the assigned value classification.