Abstract: Systems and methods are providing for detecting and monitoring thoracic fluid, air-trapping and ventilation assessment in real time, wherein data obtained from a non-invasive electrode patch is analyzed using analysis algorithms for an electrical equivalent model that have been personalized for a patient's physiologic characteristics, medical condition and/or historical medical information using machine learning trained on a dataset representative of a large and diverse patient population. The systems and methods provide a simple, real-time, highly sensitive and specific, non-invasive, bedside solution for fluid level assessment, checking for increased air trapping, and ventilation assessment. The described methods include a variety of use cases for the inventive system and methods.
Type:
Application
Filed:
November 21, 2025
Publication date:
March 26, 2026
Applicant:
Anusar Inc.
Inventors:
Sriram GANESAN, Goutam DUTTA, Rahul SHUKLA, Jagdish CHANDER SURI
Abstract: Systems and methods are providing for detecting and monitoring thoracic fluid, air-trapping and ventilation assessment in real time, wherein data obtained from a non-invasive electrode patch is analyzed using analysis algorithms for an electrical equivalent model that have been personalized for a patient's physiologic characteristics, medical condition and/or historical medical information using machine learning trained on a dataset representative of a large and diverse patient population. The systems and methods provide a simple, real-time, highly sensitive and specific, non-invasive, bedside solution for fluid level assessment, checking for increased air trapping, and ventilation assessment. The described methods include a variety of use cases for the inventive system and methods.
Type:
Grant
Filed:
March 24, 2025
Date of Patent:
December 30, 2025
Assignee:
Anusar Inc.
Inventors:
Sriram Ganesan, Goutam Dutta, Rahul Shukla, Jagdish Chander Suri
Abstract: Systems and methods are providing for detecting and monitoring thoracic fluid, air-trapping and ventilation assessment in real time, wherein data obtained from a non-invasive electrode patch is analyzed using analysis algorithms for an electrical equivalent model that have been personalized for a patient's physiologic characteristics, medical condition and/or historical medical information using machine learning trained on a dataset representative of a large and diverse patient population. The systems and methods provide a simple, real-time, highly sensitive and specific, non-invasive, bedside solution for fluid level assessment, checking for increased air trapping, and ventilation assessment. The described methods include a variety of use cases for the inventive system and methods.
Type:
Application
Filed:
March 24, 2025
Publication date:
October 2, 2025
Applicant:
Anusar Inc.
Inventors:
Sriram Ganesan, Goutam Dutta, Rahul Shukla, Jagdish Chander Suri