Semi-autonomous medical systems and methods
View Patent ↗This document describes medical systems that use artificial intelligence to facilitate autonomous or semi-autonomous medical procedures. For example, this document describes heart/lung machine systems that are used in conjunction with artificial intelligence systems to facilitate autonomous or semi-autonomous open-heart surgery operations.
1. A system for performing an open-heart surgical procedure on a patient, the system comprising:
a heart/lung machine;
one or more monitoring devices configured to monitor parameters indicative of conditions of the patient during the procedure;
a database storing:
first medical data describing medical information about the patient;
second medical data that summarizes health information of a general population of other patients; and
third medical data defining target ranges for operational parameters of the heart/lung machine and the one or more monitoring devices during the procedure; and
a computer system configured to receive in real-time during the procedure:
operational data from the heart/lung machine;
the parameters from the one or more monitoring devices;
the first medical data;
the second medical data; and
the third medical data,
the computer system further configured to, during the procedure, iteratively:
analyze: (i) the operational data from the heart/lung machine, (ii) the parameters from the one or more monitoring devices, (iii) the first medical data, and (iv) the second medical data; and
determine predictions, based on a comparison of the analysis of (i)-(iv) to the third medical data, that the operational data from the heart/lung machine or the parameters from the one or more monitoring devices are trending out of the target ranges,
wherein the computer system is further configured to generate, based on the predictions, a trained model for the procedure, wherein generating the trained model for the procedure comprises iteratively training a model for the procedure by correlating each of the predictions to (i) the operational data from the heart/lung machine, (ii) the parameters from the one or more monitoring devices, (iii) the first medical data, and (iv) the second medical data across one or more model layers using one or more machine learning algorithms, and wherein the computer system is further configured to determine the predictions that the operational data from the heart/lung machine or the parameters from the one or more monitoring devices are trending out of the target ranges based on applying the trained model for the procedure.
2. The system of claim 1 , wherein the trained model is stored in the database.
3. The system of claim 1 , wherein the computer system is further configured to generate, based on the predictions, recommended adjustments to be made in real-time during the procedure to at least one of the heart/lung machine or the one or more monitoring devices.
4. The system of claim 3 , wherein the computer system is further configured to, in real-time during the procedure:
select one or more of the recommended adjustments based at least in part on analyzing (i) the operational data from the heart/lung machine, (ii) the parameters from the one or more monitoring devices, (iii) the first medical data, and (iv) the second medical data; and
autonomously implement the selected one or more recommended adjustments.
5. The system of claim 1 , wherein the computer system is further configured to receive operational data from a plurality of heart/lung machines.
6. The system of claim 1 , wherein the one or more monitoring devices include at least one of a camera or a sensor array.
7. The system of claim 1 , wherein the one or more monitoring devices include a urine collection bag monitor.
8. The system of claim 1 , wherein the first medical data includes current conditions of the patient and historic conditions of the patient.
9. The system of claim 1 , wherein the second medical data includes historical health information of patients who have undergone the procedure.
10. A computer-implemented method for use while performing an open-heart surgical procedure on a patient, the method comprising:
receiving, by a computer system and in real-time during the procedure: (i) operational data from a heart/lung machine, (ii) parameters indicative of conditions of the patient during the procedure from one or more monitoring devices, (iii) first medical data describing medical information about the patient, (iv) second medical data that summarizes health information of a general population of other patients, and (v) third medical data defining target ranges for operational parameters of the heart/lung machine and the one or more monitoring devices during the procedure;
iteratively analyzing, by the computer system and during the procedure, (i)-(iv);
determining predictions, based on a comparison of the analysis of (i)-(iv) to the third medical data, that the operational data from the heart/lung machine or the parameters from the one or more monitoring devices are trending out of the target ranges; and
generating, based on the predictions, a trained model for the procedure, wherein generating the trained model for the procedure comprises iteratively training a model for the procedure by correlating each of the predictions to (i) the operational data from the heart/lung machine, (ii) the parameters from the one or more monitoring devices, (iii) the first medical data, and (iv) the second medical data across one or more model layers using one or more machine learning algorithms,
wherein the predictions are further determined based on applying the trained model for the procedure.
11. The method of claim 10 , further comprising generating, based on the predictions, recommended adjustments to be made in real-time during the procedure to at least one of the heart/lung machine or the one or more monitoring devices.
12. The method of claim 11 , further comprising selecting, by the computer system and during the procedure, one or more of the recommended adjustments based at least in part on analyzing (i) the operational data from the heart/lung machine, (ii) the parameters from the one or more monitoring devices, (iii) the first medical data, and (iv) the second medical data.
13. The method of claim 12 , further comprising autonomously implementing, by the computer system and during the procedure, the selected one or more recommended adjustments.