Surgical Hammer Impact Monitoring via Neural Network Audio Analysis
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Solution Overview
Problem
Current methods for monitoring the force of surgical hammer blows during prosthesis implantation in bones lack continuous and precise feedback, leading to potential fractures and damage due to excessive force, as they primarily rely on acoustic measurements that are difficult to normalize and do not assess individual steps of the implantation process effectively.
Innovation Solution
The method employs a trained artificial neural network that integrates audio measurement data from surgical hammer impacts with clinical parameters like bone density, weight, height, and Canal Flare Index to provide continuous monitoring and feedback on the impact quality, ensuring secure anchoring of prostheses without excessive force, using a device with a microphone for audio data recording and processing units for real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If acoustic measurements are used to monitor surgical hammer blows, then monitoring capability is provided, but measurement precision deteriorates due to difficulty in standardizing patient-specific acoustic emissions
Solution Approach 1:
The patent transforms acoustic emission monitoring into impact force monitoring by changing the measurement parameter from acoustic properties (frequency, amplitude, duration) to mechanical properties (force magnitude, force rate of change). This is achieved through machine learning models that correlate acoustic signals with impact forces, enabling precise quantification of hammer blow forces while accounting for patient-specific anatomical variations through normalization techniques.
Solution Approach 2:
The patent replaces the direct mechanical measurement approach (which would require contact sensors on the bone) with an acoustic field-based indirect measurement system. Microphones capture acoustic emissions during hammer blows, and machine learning algorithms process these signals to infer impact forces, thereby avoiding the complexity of direct mechanical sensing while maintaining measurement capability.
2Reliability
If acoustic evaluation is performed on the entire implantation process, then overall monitoring is achieved, but manufacturing precision deteriorates because individual implantation steps cannot be evaluated separately
Solution Approach 1:
The patent divides the continuous implantation process into discrete segments or individual steps (e.g., initial impacts, intermediate impacts, final impacts). The machine learning model processes acoustic emissions in real-time to identify and evaluate each impact event separately, assigning quality ratings to individual steps while maintaining context of the overall implantation process. This enables both comprehensive monitoring and step-specific precision evaluation.
Solution Approach 2:
The patent implements real-time feedback mechanisms where the machine learning model continuously analyzes acoustic emissions and provides immediate evaluation of each hammer blow. This feedback loop allows surgeons to adjust their technique during the procedure based on real-time quality assessments of individual impacts, ensuring precise control over each step while maintaining awareness of the overall implantation progress.
3Device complexity
If traditional acoustic monitoring methods are used, then device complexity is minimized, but measurement precision deteriorates due to inability to account for patient-specific factors like bone density and body characteristics
Solution Approach 1:
The patent enables the monitoring system to automatically adapt to each patient's specific characteristics without requiring manual input or calibration. The machine learning model is trained on diverse datasets including bone density, body mass index, and anatomical variations, allowing it to self-adjust and normalize acoustic emissions for each patient's unique physiology. This maintains system simplicity while achieving patient-specific precision through automated data-driven adaptation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables continuous and precise monitoring of surgical hammer impacts, providing real-time feedback to surgeons to ensure proper force application, reducing the risk of fractures and damage, and improving the reliability of the implantation process through intelligent and targeted visual and acoustic feedback.
Implementation Method 1
recording the sound emissions in an operating room as audio measurement data during an implantation
Data Source
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AI summary
The invention relates to a method for hammer impact monitoring for monitoring the implantation of prostheses in bones using a surgical hammer, comprising at least the following steps: a) recording the sound emissions in an operating room as audio measurement data D1 during an implantation and transferring the audio measurement data D1) to a trained artificial neural network (ANN); b) preprocessing and segmenting the audio measurement data (D1) from step a) by the trained artificial neural network (ANN), such that the audio measurement data generated by hammer blows of a surgical hammer on a prosthesis are extracted and preprocessed audio measurement data (D1*) are generated.c) Determination of the classification data (E) at least from the preprocessed audio measurement data (D1*) from step b) using the trained Artificial Neural Network (ANN), which includes at least one classification value for at least one blow of an operating hammer on a prosthesis, so that an evaluation of the blow quality can be made from these classification data (E), wherein the preprocessed audio measurement data (D1*) are used over the entire course of an operation. d) Output of the classification data (E) from step c) for monitoring of prosthesis implantations.