Technologies for monitoring and predicting impaction state of an orthopaedic surgical implement during an orthopaedic surgical procedure
Technologies for monitoring impaction and predicting impaction state during an orthopaedic surgical procedure include one or more impaction sensors that generate sensor data. The surgical procedure includes impaction of an orthopaedic implement such as a surgical instrument or a prosthetic component. An impaction analyzer generates an impaction state prediction with a machine learning model based on the sensor data. The impaction state prediction may include an unseated state, a seated state, and a fracture state. An impaction state user interface outputs the impaction state prediction. A model trainer may train the machine learning model with labeled sensor data.
1. A system for training an impaction analyzer for an orthopaedic surgical procedure, the system comprising:
an impaction sensor to generate sensor data indicative of impaction state of an orthopaedic implement relative to a bone or bone analog;
an impaction data collector to collect the sensor data from the impaction sensor; and
a model trainer to (i) label the sensor data with an impaction state label to generate labeled sensor data, wherein the impaction state label comprises an unseated state, a seated state, or a fracture state, and (ii) train a machine learning model to predict impaction state for input sensor data based on the labeled sensor data.
2. The system of claim 1 , wherein to collect the sensor data from the impaction sensor comprises to collect vibration data from a vibration sensor coupled to a surgical instrument.
3. The system of claim 1 , wherein to collect the sensor data from the impaction sensor comprises to collect motion data from an inertial measurement unit coupled to a surgical instrument.
4. The system of claim 1 , wherein to collect the sensor data from the impaction sensor comprises to collect audio data from an external microphone.
5. The system of claim 1 , wherein to collect the sensor data from the impaction sensor comprises to collect sensor data from a sensor coupled to a surgical instrument, wherein the sensor comprises a force sensing resistor, a load cell, or a displacement sensor.
6. The system of claim 1 , wherein to label the sensor data with the impaction state label to generate the labeled sensor data comprises to receive the impaction state label from a user interface.
7. The system of claim 1 , wherein to train the machine learning model based on the labeled sensor data comprises to:
pre-process the labeled sensor data to generate processed sensor data; and
train the machine learning model based on the processed sensor data.
8. The system of claim 7 , wherein to pre-process the labeled sensor data comprises to:
transform the labeled sensor data to a frequency domain to generate frequency domain sensor data; and
reduce dimensionality of the frequency domain sensor data to generate the processed sensor data.
9. The system of claim 8 , wherein to reduce the dimensionality of the frequency domain sensor data comprises to perform principal component analysis of the frequency domain sensor data.
10. The system of claim 1 , wherein to train the machine learning model to predict impaction state for input sensor data based on the labeled sensor data comprises to:
train a recurrent neural network with the labeled sensor data to identify anomalies in the labeled sensor data; and
train a classifier with the anomalies in the labeled sensor data to predict the impaction state.
11. The system of claim 10 , wherein the recurrent neural network comprises a long short-term memory network.
12. The system of claim 11 , wherein the classifier comprises a random forest predictive model.
13. The system of claim 1 , wherein the orthopaedic implement comprises a femoral broach.
14. The system of claim 1 , wherein the orthopaedic implement comprises a prosthetic component.