Systems and methods for enhanced automated endoscopy procedure workflow

Abstract

Systems and methods are provided for delivering consistent high quality, cost efficient results in fixed or mobile endoscopy facilities, without requiring the continuous real-time involvement of a fellowship trained gastroenterologist, by integrating patient specific information into decision support systems and AI/machine learning systems employed during the planning and examination phases of the endoscopy procedure.

What is claimed is: 
     
         1 . A system for enhancing detection of tissue abnormalities during an endoscopic procedure, the system comprising:
 an endoscopy system that outputs a video stream;   a monitor; and   a processor implementing a patient electronic intake module and an artificial intelligence module,   wherein, responsive to data input by a patient, the patient electronic intake module transmits patient specific data to the artificial intelligence module; and   wherein the artificial intelligence module is configured to preferentially search for features in the video stream indicative of tissue abnormalities corresponding to the patient specific data, the artificial intelligence module generating an overlay for display on the monitor identifying features in the video stream indicative of a presence of tissue abnormalities.   
     
     
         2 . The system of  claim 1 , wherein the patient specific data comprises patient identification information. 
     
     
         3 . The system of  claim 1 , wherein the patient specific data comprises patient medical history information. 
     
     
         4 . The system of  claim 3 , further comprising a database, wherein the patient electronic intake module initiates retrieval of patient medical history information from the database. 
     
     
         5 . The system of  claim 1 , wherein the patient specific data comprises family medical history information for the patient. 
     
     
         6 . The system of  claim 5 , further comprising a database, wherein the patient electronic intake module initiates retrieval of patient family medical history information from the database. 
     
     
         7 . The system of  claim 1 , wherein the system further comprises a database and a storage module for recording to the database procedure information about the video stream and overlay generated during the endoscopic procedure. 
     
     
         8 . The system of  claim 7 , further comprising a report generation module, wherein the report generation module selects from the database a subset of the procedure information and formats the subset into a report. 
     
     
         9 . The system of  claim 1 , wherein the patient electronic intake module is configured to present a series of questions to the patient to determine compliance with bowel cleansing guidelines. 
     
     
         10 . The system of  claim 1 , wherein the patient electronic intake module is configured to present an informative video describing the endoscopic procedure and, responsive to patient inputs, present options for sedation or anesthesia. 
     
     
         11 . A method for enhancing detection of tissue abnormalities during an endoscopic procedure, the method implemented by a processor executing a patient electronic intake module and an artificial intelligence module for use with an endoscopy system that outputs a video stream and a monitor, the method comprising:
 by the patient electronic intake module, querying the patient to input data;   responsive to data input by a patient to the patient electronic intake module, transmitting patient specific data to the artificial intelligence module;   based on the patient specific data, configuring the artificial intelligence module to preferentially search for features in the video stream indicative of tissue abnormalities that correspond to the patient specific data; and   generating by the artificial intelligence module an overlay for display on the monitor identifying features in the video stream indicative of a presence of tissue abnormalities.   
     
     
         12 . The method of  claim 11 , wherein querying the patient to input data comprises querying the patient to input patient identification information. 
     
     
         13 . The method of  claim 11 , wherein querying the patient to input data comprises querying the patient to input patient medical history information. 
     
     
         14 . The method of  claim 11 , further comprising retrieving patient medical history information from a database responsive to the data input by the patient. 
     
     
         15 . The method of  claim 11 , wherein querying the patient to input data comprises querying the patient to input patient family medical history information. 
     
     
         16 . The method of  claim 11 , further comprising retrieving patient family medical history information from a database responsive to the data input by the patient. 
     
     
         17 . The method of  claim 11 , further comprising recording to a database procedure information about the video stream and overlay generated during the endoscopic procedure. 
     
     
         18 . The method of  claim 17 , further comprising selecting from the database a subset of the procedure information and formatting the subset into a report. 
     
     
         19 . The method of  claim 11 , further comprising, by the patient electronic intake module, presenting a series of questions to the patient to determine compliance with bowel cleansing guidelines. 
     
     
         20 . The method of  claim 11 , further comprising, by the patient electronic intake module, presenting an informative video describing the endoscopic procedure and, responsive to patient inputs, presenting options for sedation or anesthesia. 
     
     
         21 . The method of  claim 11 , wherein machine learning generated algorithms are trained using cross-validation.