Robotic surgical system with artificial intelligence
A surgical robot is coupled to the surgeon console. The surgical robot performs a robotic surgical procedure. The surgical robot includes one or more robotic surgical arms. A control system is coupled to the one or more robotic surgical arms. An artificial intelligence (“AI”) system includes a plurality of machine learning algorithms. The robotic surgical arms are at least partially controlled by the AI system and the control device to process intraoperative data including images captured by cameras and sensor inputs. The machine learning algorithms analyze the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases. The one or more machine algorithms enable at least partial identification of anatomical structures. In response to detection of the anatomical structures the AI system at least partially adjusts movement of the robotic surgical arms to avoid critical anatomical structures while performing the robotic surgery procedure to ensure precise targeting at the surgical site while minimizing damage to surrounding tissue at a surgical site. The AI system provides a surgeon with improved dexterity when the surgeon uses the robotic surgical arms at the surgical site, the improved dexterity resulting from at least partially analyzing the intraoperative data in real time by the one or more machine learning algorithms, enabling precise and adaptive manipulation of the robotic surgical arms at the surgical site.
1 . A robotic surgical system, comprising:
a surgeon console including a display and a planning module configured to allow a surgeon to create a surgical plan for a robotic surgical procedure;
a patient console operatively coupled to the surgeon console and including one or more robotic surgical arms and a plurality of robotic surgical instruments carried by the one or more robotic surgical arms;
a robotic surgical control system operatively coupled to the surgeon console and the patient console, the robotic surgical control system including a surgical computing device, the surgical computing device comprising at least one computer processor, a memory storing surgical instructions, and a non-transitory computer-readable storage medium storing computer-executable instructions including the surgical instructions, the surgical computing device being configured to access an image recognition database and a procedure database;
a sensor array operatively coupled to at least one of the patient console and the robotic surgical control system and configured to acquire intraoperative data comprising imaging data from a surgical site of a patient; and
an artificial intelligence (AI) system including an AI architecture implemented by the at least one computer processor and executing a plurality of machine learning algorithms, the AI system having access to the non-transitory computer-readable storage medium, being operatively coupled to the sensor array, the robotic surgical control system, and the patient console, and being configured to: receive the intraoperative data from the sensor array; clean imaging data included in the intraoperative data and transform the imaging data into a format suitable for analysis; and analyze the intraoperative data, including the imaging data, using the plurality of machine learning algorithms and at least one of the image recognition database and the procedure database to identify anatomical structures of the patient at the surgical site and generate control signals, the anatomical structures including one or more critical anatomical structures selected from the one or more of: subcutaneous tissue, adipose tissue, fascia, muscle, tendons, ligaments, bones, joints, cartilage, hollow organs, solid organs, vascular structures, peripheral nerves, spinal cord, nerve roots, autonomic nerves, peritoneum, pleura, pericardium, and tumors, in response to the control signals, the robotic surgical control system causes the one or more robotic surgical arms to move during the robotic surgical procedure so as to avoid at least one of the critical anatomical structures while performing the surgical plan; and
wherein execution of the computer-executable instructions further causes the robotic surgical system to perform one or more of: monitoring the robotic surgical procedure; executing a planned surgical step of the robotic surgical procedure using the plurality of machine learning algorithms; and determining that the robotic surgical procedure has been completed.
2 . The system of claim 1 , wherein the AI system is configured to detect a deviation between the surgical plan and the intraoperative data and, in response to detecting the deviation, generates a recommendation to modify the surgical plan and presents the recommendation on the display of the surgeon console.
3 . The system of claim 1 , wherein identification of the patient anatomical structures intraoperatively is facilitated by patient anatomical landmarks.
4 . The system of claim 1 , wherein once the patient anatomical structures are identified, the AI system calculates movements of the one or more robotic surgical arms and the plurality of surgical instruments.
5 . The system of claim 1 , wherein the AI system calculates movements of the one or more robotic surgical arms and the plurality of robotic surgical instruments and adjusts one or more parameters, including force and angle to ensure effective targeting.
6 . The system of claim 1 , wherein the robotic surgical system continuously monitors the interaction between the one or more robotic surgical arms and the plurality of robotic surgical instruments and the surgical site.
7 . The system of claim 1 , wherein the system uses predictive modeling and historical data for the one or more robotic arms and the plurality of robotic surgical instruments to update movement predictions.
8 . The system of claim 1 , wherein patterns learned from prior surgical procedures are used during the robotic surgical procedure.
9 . The system of claim 1 , wherein the plurality of machine learning algorithms assigns confidence scores to each planned movement based on the analysis of intraoperative data.
10 . The system of claim 1 , wherein the plurality of machine learning algorithms assigns confidence scores to each planned movement based on the analysis of intraoperative data and use the confidence scores to guide the surgeon to a selected movement path of the one or more robotic surgical arms and the plurality of robotic surgical instruments.
11 . The system of claim 1 , wherein the improved dexterity of one or more robotic surgical arms and the plurality of robotic surgical instruments also includes the ability to make one or more of: ultra-fine movements, and effective dissections, at least in part by utilizing feedback.
12 . The system of claim 1 , wherein during tumor resection the robotic surgical system can detect and adjust for differences in tissue texture and adjusts at least one movement parameter of the one or more robotic surgical arms in response to the detected differences.
13 . The system of claim 1 , wherein at least a portion of the intraoperative data includes noisy data, errors, outliers, and inconsistencies.
14 . The system of claim 13 , wherein the system provides functionality for identifying, cleaning, and transforming the noisy data for use in the plurality of machine learning algorithms.