System and method for adverse event detection or severity estimation from surgical data
AssigneeSurgical Safety Technologies Inc.
InventorsHaiqi WEI, Teodor Pantchev GRANTCHAROV, Babak TAATI, Yichen ZHANG, Frank RUDZICZ, Kevin Lee YANG
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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.
Claims20
A system for automatically generating data structures adapted for storing classifications relating to an adverse event based on audio or video data, the classifications based at least on a plurality of classification tasks, the system comprising:
- a processor, operating in conjunction with computer memory, the processor configured to:
- receive a set of audio or video data;
- extract, using a feature extractor neural network, a vector of latent features from the set of audio or video data;
- provide, to each of a plurality of time-based classifiers, the vector of latent features from the convolutional network, each time-based classifier corresponding to a classification task of the plurality of classification tasks;
- train the feature extractor neural network on a training data set using a sigmoid binary cross-entropy loss;
- and
- train each time-based classifier of the plurality of time-based classifiers separately on each classification task of the plurality of classification tasks with a loss function that includes at least the binary cross-entropy loss.
A method for automatically generating data structures adapted for storing classifications relating to an adverse event based on audio or video data, the classifications based at least on a plurality of classification tasks, the method comprising:
- receiving a set of audio or video data;
- extracting, using a feature extractor neural network, a vector of latent features from the set of audio or video data;
- providing, to each of a plurality of time-based classifiers, the vector of latent features from the convolutional network, each time-based classifier corresponding to a classification task of the plurality of classification tasks;
- training the feature extractor neural network on a training data set using a sigmoid binary cross-entropy loss;
- and
- training each time-based classifier of the plurality of time-based classifiers separately on each classification task of the plurality of classification tasks with a loss function that includes at least the binary cross-entropy loss.
A non-transitory computer readable medium storing machine interpretable instructions, the machine interpretable instructions, which when executed by a processor, cause the processor to perform a method for automatically generating data structures adapted for storing classifications relating to an adverse event based on audio or video data, the classifications based at least on a plurality of classification tasks, the method comprising:
- receiving a set of audio or video data;
- extracting, using a feature extractor neural network, a vector of latent features from the set of audio or video data;
- providing, to each of a plurality of time-based classifiers, the vector of latent features from the convolutional network, each time-based classifier corresponding to a classification task of the plurality of classification tasks;
- training the feature extractor neural network on a training data set using a sigmoid binary cross-entropy loss;
- and
- training each time-based classifier of the plurality of time-based classifiers separately on each classification task of the plurality of classification tasks with a loss function that includes at least the binary cross-entropy loss.
Dependencies are inferred from the claim text.