System and method for performing joint replacement surgery using artificial neural network
OwnerJared Weirinventor
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Abstract
This disclosure relates to a system and method for performing joint replacement surgery, and in particular relates to an artificial neural network that uses machine learning to improve patient outcomes. In an example method, the method includes the step of predicting a patient score indicative of a success of a surgical procedure as perceived by a patient using a neural network. Based on the predicted patient score, a surgeon may update a treatment plan in a way that improves the patient's outcome. These and other benefits will be appreciated from the following description.
Claims24
A method, comprising:
- predicting a patient score indicative of a success of a surgical procedure as perceived by a patient using a neural network, wherein the step of predicting the patient score is based on a predicted fatigue of a surgeon, and wherein the predicted fatigue of the surgeon is based on each of a time of day, a schedule of the surgeon, a workload of the surgeon, and a handedness of the surgeon;
- using the neural network, updating the predicted patient score during the surgical procedure based on the predicted fatigue of the surgeon;
- using the neural network, accounting for the predicted fatigue of the surgeon during the surgical procedure when evaluating gap measurements between bones, wherein the gap measurements are obtained when the surgeon applies a physical load to distract a joint;
- using the neural network, recommending an update to a surgical plan during the surgical procedure when the neural network determines, based on the predicted fatigue of the surgeon, that the recommended update to the surgical plan will improve the predicted patient score;
- and
- presenting, to the surgeon during the surgical procedure, an expected increase to the predicted patient score associated with the recommended update to the surgical plan.
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A robot-assisted surgery system, comprising:
- a neural network configured to predict a patient score indicative of a success of a surgical procedure as perceived by a patient, wherein the neural network is configured to predict the patient score based on a predicted fatigue of a surgeon, and wherein the predicted fatigue of the surgeon is based on each of a time of day, a schedule of the surgeon, a workload of the surgeon, and a handedness of the surgeon, wherein the neural network is configured to update the predicted patient score during the surgical procedure based on the predicted fatigue of the surgeon, wherein the neural network is configured to account for the predicted fatigue of the surgeon during the surgical procedure when evaluating gap measurements between bones, wherein the gap measurements are obtained when the surgeon applies a physical load to distract a joint, wherein the neural network is configured to recommend an update to a surgical plan during the surgical procedure when the neural network determines, based on the predicted fatigue of the surgeon, that the recommended update to the surgical plan will improve the predicted patient score;
- and
- at least one display, wherein the neural network is configured to cause the at least one display to display, to the surgeon during the surgical procedure, an expected increase to the predicted patient score associated with the recommended update to the surgical plan.
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Dependencies are inferred from the claim text.