Invented by Samuel Cho, Cho Samuel Dr

The market for architecture, system, and method for developing and robotically performing a medical procedure activity is rapidly growing and evolving. With advancements in technology and robotics, the healthcare industry is witnessing a significant shift towards automation and precision in medical procedures. This article explores the current state of the market, its potential growth, and the benefits it brings to patients and healthcare professionals. The architecture, system, and method for developing and robotically performing a medical procedure activity refer to the integration of robotics and artificial intelligence (AI) into surgical procedures. This technology allows for more precise and controlled movements, reducing the risk of human error and improving patient outcomes. It involves the use of robotic systems, specialized instruments, and advanced imaging techniques to perform complex medical procedures with enhanced precision. One of the key drivers of the market for this technology is the increasing demand for minimally invasive surgeries. Robotic systems enable surgeons to perform procedures through small incisions, resulting in reduced pain, faster recovery times, and minimal scarring for patients. This has led to a growing preference for robotic-assisted surgeries in various medical specialties, including urology, gynecology, and general surgery. Furthermore, the market is also driven by the need for improved surgical outcomes and patient safety. Robotic systems provide surgeons with a greater range of motion and dexterity, allowing them to perform intricate procedures with enhanced precision. The integration of AI algorithms and machine learning capabilities further enhances the system’s ability to adapt to individual patient anatomy and optimize surgical techniques. The market for architecture, system, and method for developing and robotically performing a medical procedure activity is witnessing significant growth due to the numerous benefits it offers. Firstly, it enhances surgical precision, reducing the risk of complications and improving patient outcomes. The robotic systems provide surgeons with a 3D visualization of the surgical site, allowing for better accuracy and control during the procedure. Secondly, this technology enables surgeons to perform complex procedures with greater ease and efficiency. The robotic systems can be programmed to perform repetitive tasks, freeing up the surgeon’s time to focus on critical decision-making and ensuring optimal patient care. This not only improves surgical efficiency but also reduces the overall procedure time, leading to cost savings for healthcare providers. Moreover, the market for this technology is also driven by the increasing demand for remote surgery and telemedicine. With the advancements in connectivity and communication technologies, surgeons can now perform procedures remotely, providing access to specialized care in remote areas or during emergencies. This has the potential to revolutionize healthcare delivery, particularly in underserved regions. However, despite the numerous benefits and potential growth opportunities, there are challenges that need to be addressed in the market for architecture, system, and method for developing and robotically performing a medical procedure activity. One of the major challenges is the high cost associated with acquiring and maintaining robotic systems. The initial investment and ongoing maintenance expenses can be significant, limiting the adoption of this technology in some healthcare settings. Additionally, there is a need for standardized training programs and guidelines for surgeons to ensure safe and effective use of robotic systems. As this technology becomes more prevalent, it is crucial to establish comprehensive training programs that equip surgeons with the necessary skills to operate the robotic systems and interpret the data provided by the AI algorithms. In conclusion, the market for architecture, system, and method for developing and robotically performing a medical procedure activity is experiencing rapid growth and holds immense potential for the future of healthcare. The integration of robotics and AI into surgical procedures offers numerous benefits, including enhanced precision, improved patient outcomes, and increased surgical efficiency. However, challenges such as high costs and the need for standardized training programs need to be addressed to ensure widespread adoption and maximize the benefits of this technology.

The Cho Samuel Dr invention works as follows

Embodiments for architecture, systems and methods of developing a learning/evolving robot to perform one or several activities of a surgical procedure, where the medical procedures may include diagnosing and treating a patient?s medical conditions, robotically diagnosing and treating a patient?s medical conditions, and performing one or multiple medical procedure activities without User interaction.

Background for Architecture, System, and Method for Developing and Robotically Performing a Medical Procedure Activity

It may be desirable to create a system that can learn and evolve to perform robotically one or several activities of a procedure. The medical procedure could include diagnosing or treating a patient medical condition, and robotically diagnosing or treating a patient medical condition and performing one or multiple medical procedure activities on the basis of the diagnosis. The present invention relates to architectures, systems and methods.

The present invention provides an architectural (10?FIG. 1) for developing a base logic/model/procedure (L/M/P) and training/improving neural network systems to enable robot(s) to perform one or more activities of a medical procedure according to various embodiments. The embodiments of the invention can be used to train architecture 10 continuously to diagnose medical conditions and treat medical conditions. Architecture 10 can be used to perform one or several activities of a procedure that is employed by medical professionals for diagnosis or treatment. In one embodiment, architecture 10 can divide a medical process into a number of predefined steps or activities that are performed by robot systems A-N60A-60C using feedback or input received from sensor systems 20A-20C and controlled by neural network systems 50A-50C in order to diagnose medical conditions or to treat medical conditions.

A base logic/model(s)/procedure (L/M/P) may be developed for the step or activities based on available sensor data. One or more robots can be trained using machine learning to perform the steps or activities based upon the L/M/P developed. Robots can then be used to perform the step or activity based on L/M/P developed and live sensor data. Machine learning can be enhanced or evolved by adding additional sensor data, and user input/guidance.

In one embodiment, a professional medical 70B can be instructed to perform different activities of a procedure on a patient, 70A, while sensor systems 20A-20C are recording various data regarding the patient, 70A, medical instruments, implants and other medical implements used by a professional medical 70B in order to perform a procedure. Sensor systems 20A-20C may store position, generated and received data in training databases 30A-30C. Based on the sensor data, system experts/users, and medical professionals 70B inputs a base logic/model(s)/procedure (L/M/P) may be developed for the activities of a medical procedure.

The training systems A – N 40A-40C can use the retrieval of training data 30A-30C as well as live sensor system generated, received and position data. Medical professionals 70B may also input data to utilize machine learning to control one or more robot systems 60A-60C. Sensor systems A -N 20A-20C will then be used to perform a medical procedure using sensor system A -N generated, received and position data. The sensor system A -N 20A-20C can be part of the robotic system A -N 60A-60C, and controlled by a machine-learning system (neural system A -N 50A-50C is an embodiment). This includes its position in relation to a patient as well as signals it generates.

In an embodiment, a system of neural networks A-N 50A-50C can also be a part of a robot system A-60A-C. In one embodiment, neural network systems AN 50A-50C can be machine learning systems or artificial intelligence systems or any other logic-based systems, networks or architecture.

FIG. 1 is a diagram of architecture 10 for developing a learning/evolving system and robotically/autonomously performing a medical procedure activity according to various embodiments. As shown in FIG. As shown in FIG. 1, architecture 10 can include a number of sensor systems (A-N) 20A-20C; a number of training databases (30A-30C); a group of training systems (A-N) 40A-40C; a group of neural network systems (A-N) 50A-50C; and a variety of robotic systems 60A-60C. The architecture 10 can be directed at a patient 70A, and controlled/developed/modulated by one or several system experts and medical professionals. A sensor system 20A-20C in an embodiment may be passive or active. In an active system, sensor systems A-N-20A-20C can generate signals 22 configured to activate or highlight one or more physical characteristics of a 70A patient, 70A’s environment, medical instruments deployed to evaluate 70A patient, or medical constructs employed within 70A patient. Signal(s), 24 may be received by an active sensor system A -N 20A-20C. These signal(s), 24 may be generated as a response to signal(s), 22 or independently. The active sensor system A-N 20A-20C to be deployed/employed/positioned in architecture 10 may vary as a function of the medical procedure activity to be conducted by architecture 10 and may include electro-magnetic sensor systems, electrical stimulation systems, chemically based sensors, and optical sensor systems.

In a passive system, the sensor system A-N-20A-20C can receive signals 24 generated by other stimuli, such as electro-magnetic or optical stimuli, chemical stimuli, temperature or other 70A patient measurable stimuli. A passive sensor system A-N 20A-20C to be deployed/employed/positioned in architecture 10 may also vary as a function of the medical procedure activity to be conducted by architecture 10 and may include electro-magnetic sensor systems, electrical systems, chemically based sensors, and optical sensor systems.

During training and non-training activities, “sensor system signals A-N 20A-20C (generated, received/measured and position relative to the patient) 22, 24, may be stored in a training database 30A-30C. In one embodiment, architecture 10 can store sensor system signals A-N 20A-20C (generated, measured, and positional data) 22, 24 during non-training and training medical procedures. The generated, measured, and positioning data may then be used by the training systems 40A-40C in order to update and form neural network systems 50A-50C using developed L/M/P. The data 80B in the training databases, and feedback or reviews 42 from medical professionals 70B may be used by one or more of the training systems A-N40A-40C to generate training signals. These training signals are then used by neural network system A-N50A-50C to create or update neural networks or networks using developed L/M/P. The data 80B can be used to create the initial L/M/P of a specific activity in a medical procedure.

The training system data may be sensor data 80A previously recorded for an activity of a procedure. The neural network systems can use the formed developed L/M/P and live sensor system A-N-20A-20C data 80D to control the operation of one or more robotic systems A -N 60A-60C and sensor systems A -N20A-20C. The neural network system A-50A-C can use the developed L/M/P as well as live sensor data 80D from the sensor system A-20A-20C to control one or more robot systems 60A-60C, and sensor systems 20A-20C.

As indicated, one or multiple sensor systems A – N 20A – C may be part a robot system A -N 60A – 60C or a system of neural networks A -N 50A – 50C. Sensor system A-N-20A-C can also be a separate system. In either configuration, the neural network system (A-N 50A-50C) can control the signals generated by sensor systems A-N20A-C for active sensors and their position(s) in relation to a patient while performing an activity. In a similar way, one or several training systems AN 20AC can be part of either a robot system AN 60A-60C (or a neural system AN 50A-50C). A training system AN 40AC can also be a separate system. A training system A -N 40A -C can also communicate with a neuronal network system A -N 50A-50C over a wireless or wired network. One or more training database 30A-C can also be a part of the training system A-N40A-40C. A training database 30A-30C can also be a separate system that communicates with a sensor system A-20A-C or a training system 40A-40C over a wireless or wired network. The wired or wireless networks may be local or Internet-based and use cellular, WiFi and satellite communication systems.

FIG. The diagram 2A shows a first sensor network and architecture 90A in accordance with various embodiments. As shown in FIG. As shown in FIG. In this embodiment, the neural network system 50A-C can be trained to respond to specific sensor data (generated and received) based upon one or more developed L/M/P. The outputs 52A to N of the neural network system 50A-C may be used separately to control a robot system 60A-C. In a second embodiment, the neural networks A-N 50A-50C can be coupled with another neural network O 50O. This is shown in FIG. 2B. The neural network architecture (90B) may allow neural network systems 50A to N to process sensor data and the neural networks 50A to O to process outputs 52A to 52N. The neural network O 50O can then control one or several robotic systems A -N 60A – C and sensor systems A -N 20A – 20C using neural processing of the combined neural processed data. The neural network O 50O can make decisions using a combination different sensor data from various sensor systems A -N 20A-20C, and based upon one or more developed L/M/P. This makes the neural network O 50O more similar to a medical professional 70B who may take into account many different sensor types as well as their sensory inputs in formulating an action.

In another embodiment, the neural network architecture 90C of FIG. 2C can use a single neural system P 50P to receive and process sensor data 80D coming from multiple sensor systems A-N. The single neural system P 50P, like the neural system O 50O can make decisions using a combination different sensor data from various sensor systems A-N. This makes the single network system P. 50P more similar to a medical professional. 70B who may take into account many different types of sensor data in addition to sensory inputs to formulate an action or a decision. In one embodiment, any of the neural structures 90A-C can employ millions of nodes in different configurations. This includes a feed-forward network such as that shown in FIG. Each column of nodes 2A-D and 3A feeds into the right column. The input vectors I and O can have many entries, and each node could include a weighted matrices that are applied to the vector upstream. This weight matrix may be developed by training databases 30A-30C or training systems 40A-40C.

Different sets of neural network 90A-90D can be formed/trained and updated (evolved) to perform a specific activity in a medical procedure. On the basis of sensor data, 80A, one or more L/M/P can be developed to perform a specific activity in a medical procedure. Different sets of neural network 90A-90D can be formed/trained and updated (evolved) for a specific activity of a procedure based on developed L/M/P.

FIG. 3A is a flow diagram illustrating several methods 100A for developing one or more base logic/model/procedure (L/M/P) and training/improving neural network systems 50A-50C to enable robot(s) 60A-60C to perform activities of a medical procedure based on a developed L/M/P and sensor systems A-N 20A-20C according to various embodiments. Architecture 10 can be used to train neural network 50A-N, develop/evolve L/M/P, or operate robots 60A-N, or sensor systems A-20A-20C, based on one developed L/M/P, sensor data (generated and received) 80A, or sensor data 80A, for one sensor system 20A-20C, or sensor data 80A, which may be stored by one training database 30A-30C.

As shown in FIG. 3A and discussed above architecture 10 may be employed to develop one or more logic/models/procedures (L/M/P) for a new activity of a medical procedure or continue to update/evolve one or more logic/models/procedures (L/M/P) of a previously analyzed activity of a medical procedure. Architecture 10 can also be used to train neural network systems (or automated systems) to perform a new medical procedure activity or to continue updating or improving neural network system 50A-50C for an analyzed medical procedure activity.

As shown in FIG. A training system 40A-40C or expert 70B can determine in 3A whether a selected medical procedure for review by architecture 10 was previously reviewed/analyzed (activity 102A). If the medical procedure was previously reviewed/analyzed, then new data can be collected to improve one or more L/M/P systems and machine learning systems that have been developed (e.g. neural networks 50A-C). If the medical procedure has been reviewed/analyzed previously, new data may be collected for one of the known activities (activities 128A-134A) to improve evolvement of L/M/P and related machine learning systems (neural networks 50A-C in an embodiment).

A medical professional or another user 70B might be able indicate one or more activities which underlie a particular medical procedure. Depending on the procedure, there may be different activities that are defined by medical groups or boards. (such as the American Board of Orthopaedic Surgery ABOS?) A medical professional 70B who is certified in the medical procedure will be expected to perform all activities as defined by medical groups or boards. In one embodiment, a certified medical professional 70B can also define a new procedure and the activities that support it. A medical procedure to perform spinal fusion between adjacent vertebrae, for example, may include activities defined by the ABOS. (Activity 104A). The medical procedure can be further divided based on different L/M/P which may be created/developed for each activity.

A simplified procedure can include multiple activities, including: placing a screw in superior vertebra’s left pedicle using sensor systems A-N-20A-C, placing a screw in superior vertebra’s right pedicle using sensor systems A-N-20A-C, placing a screw in inferior vertebra’s right pedicle using sensor systems A-N-20A-C, placing a screw in inferior vertebra’s left pedicle using sensor system A-N-20A-C, and loosely coupling an a screw between superior pedicle a screw (a-

It should be noted that architecture 10 is not required or requested to perform all activities in a medical procedure. Some activities can be performed by medical professionals 70B. Architecture 10 can be used to develop a L/M/P, train a neural network system 50A-50C using robotic systems 60A-60C, and use sensor systems A-N 20A-20C to insert pedicle screw in the left and right pedicles for vertebrae that are to be coupled. A medical professional can place rods, compress vertebrae or decompress them and lock the rods onto the screws. The activities can also include multiple steps. “Once developed and trained, the architecture 10 can be used to place pedicle screws into vertebrae.

A medical professional or another user can start an activity (activity 106)A, and one or several sensor systems 20A-20C are employed/positioned in order to collect and generate sensor data (actively) while the activity (activity 108)A is being performed. The Architecture 10 can sample sensor data 80A (generated, received and position) of one or more sensors systems 20A-20C to ensure that sufficient data is collected during an activity 108A. Sensor data can include, for example, the position of a radiographic device, its generated signals and its radiographic image, such as the images 220A and 220B in FIGS. The 4A and the 4B are generated from data received. FIG. FIG. 4A shows an axial view or cross-section of a vertebral column from a computed Tomography scan 230A, which was created by a sensor system 40A that generated a signal and had a position in relation to the patient. FIG. FIG. 4B shows a sagittal view or side view, of several vertebrae in the spine from a computed-tomography scan (230A) created by a sensor system 40A that generates a second signal. The position of this sensor system relative to the patient is also shown.

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