Surgical Safety Technologies Inc.

University of Toronto

University Health Network

Unity Health Toronto

Frank Rudzicz

Teodor Pantchev Grantcharov

Babak Taati

Kevin Lee Yang

Azadeh Yadollahi

Noah Crampton

Shuja Khalid

Serena Jeblee

Mitchell Geoffrey Goldenberg

Yichen Zhang

Haiqi Wei

Shumit Saha

Muhammad Mamdani

Faiza Khan Khattak

Eyal De Lara

Daniyal Liaqat

Mohamed Abdalla

Andrea Gershon

Robert Wu

Described herein are systems and methods of extracting information from a dialogue, the dialogue having transcription data associated therewith. In an embodiment, the method including: receiving the transcription data associated with the dialogue; classifying utterances in the transcription data using a trained classification machine learning model, the classification machine learning model trained using one or more corpora of historical data comprising previous dialogues labelled with utterance types; identifying entities in the transcription data; classifying attributes in the transcription data using a trained attribute machine learning model, the attribute machine learning model trained using one or more corpora of historical data comprising previous dialogues labelled with attributes; and outputting at least one of the utterances, the entities, and the attributes.

Systems and methods for extracting information from a dialogue

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

Described are various embodiments of an acoustic upper airway assessment system and method, and sleep apnea assessment system and method relying thereon. In one embodiment, a non-invasive method is described for assessing upper airway anatomy in a subject while breathing. This method comprises receiving as input at a hardware processor a digital signal representative of respiratory sounds generated by the subject while breathing; isolating digital respiratory sound segments from said digital signal; computationally extracting a designated spectral feature from each of said segments to characterize said respiratory sounds within each of said segments; automatically evaluating each said extracted spectral feature against a preset upper airway anatomy metric associated with said designated spectral feature to characterize a given upper airway anatomy measure in the subject while breathing; and outputting a sleep apnea severity indication based on said characterized upper airway anatomy measure.

Acoustic upper airway assessment system and method, and sleep apnea assessment system and method relying thereon

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Toronto, Canada

Frank Rudzicz