System and methods for enhanced automated endoscopy procedure workflow
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.
1. A system for enhancing detection of tissue abnormalities during an endoscopic procedure, the system comprising:
an endoscopy system that outputs a video stream during the endoscopic procedure;
a monitor;
a database that contains patient specific medical history information comprising previously analyzed video data indicative of previously encountered tissue abnormalities; 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 retrieve the patient specific medical history information from the database based on the patient specific data and to adjust the artificial intelligence module preferentially to analyze the video stream in real-time during the endoscopic procedure to detect features indicative of the tissue abnormalities previously encountered within the patient specific medical history information that correspond to the patient specific data and the patient specific medical history information, the artificial intelligence module generating a composite display comprising the video stream overlaid with graphical information identifying the detected features in the video stream from the endoscopy system indicative of a presence of endoluminal 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 features in the video stream indicative of the presence of endoluminal tissue abnormalities are recorded in the patient specific medical history information stored in the database.
4. The system of claim 1 , wherein the patient specific data comprises family medical history information for the patient.
5. The system of claim 4 , wherein the patient electronic intake module initiates retrieval of patient family medical history information from the database.
6. The system of claim 1 , wherein the system further comprises a storage module for recording to the database procedure information about the video stream and overlay generated during the endoscopic procedure.
7. The system of claim 6 , 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.
8. 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.
9. 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.
10. 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 during the endoscopic procedure 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;
by the artificial intelligence module, retrieving patient specific medical history information comprising previously analyzed video data indicative of previously encountered tissue abnormalities based on the patient specific data from a database;
based on the patient specific data and patient specific medical history information, configuring the artificial intelligence module preferentially to analyze the video stream in real-time during the endoscopic procedure to detect features indicative of the tissue abnormalities previously encountered within the patient specific medical history information that correspond to the patient specific data and the patient specific medical history information; and
generating by the artificial intelligence module a composite display comprising the video stream overlaid with graphical information identifying the detected features in the video stream from the endoscopy system indicative of a presence of endoluminal tissue abnormalities.
11. The method of claim 10 , wherein querying the patient to input data comprises querying the patient to input patient identification information.
12. The method of claim 10 , wherein the features in the video stream indicative of presence of endoluminal tissue abnormalities are recorded in the patient medical history information stored in the database.
13. The method of claim 10 , wherein querying the patient to input data comprises querying the patient to input patient family medical history information.
14. The method of claim 10 , further comprising retrieving patient family medical history information from the database responsive to the data input by the patient.
15. The method of claim 10 , further comprising recording to the database procedure information about the video stream and overlay generated during the endoscopic procedure.
16. The method of claim 15 , further comprising selecting from the database a subset of the procedure information and formatting the subset into a report.
17. The method of claim 10 , further comprising, by the patient electronic intake module, presenting a series of questions to the patient to determine compliance with bowel cleansing guidelines.
18. The method of claim 10 , 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.
19. The method of claim 10 , wherein machine learning generated algorithms are trained using cross-validation.