Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements
Authors: Al-Nasser, S., Noroozi, S., Harvey, A., Aslani, N. and Haratian, R.
Journal: Sensors
Volume: 24
Issue: 2
ISSN: 1424-8220
DOI: 10.3390/s24020585
Abstract:Using tibial sensors in total knee replacements (TKRs) can enhance patient outcomes and reduce early revision surgeries, benefitting hospitals, the National Health Services (NHS), stakeholders, biomedical companies, surgeons, and patients. Having a sensor that is accurate, precise (over the whole surface), and includes a wide range of loads is important to the success of joint force tracking. This research aims to investigate the accuracy of a novel intraoperative load sensor for use in TKRs. This research used a self-developed load sensor and artificial intelligence (AI). The sensor is compatible with Zimmer’s Persona Knee System and adaptable to other knee systems. Accuracy and precision were assessed, comparing medial/lateral compartments inside/outside the sensing area and below/within the training load range. Five points were tested on both sides (medial and lateral), inside and outside of the sensing region, and with a range of loads. The average accuracy of the sensor was 83.41% and 84.63% for the load and location predictions, respectively. The highest accuracy, 99.20%, was recorded from inside the sensing area within the training load values, suggesting that expanding the training load range could enhance overall accuracy. The main outcomes were that (1) the load and location predictions were similar in accuracy and precision (p > 0.05) in both compartments, (2) the accuracy and precision of both predictions inside versus outside of the triangular sensing area were comparable (p > 0.05), and (3) there was a significant difference in the accuracy of load and location predictions (p < 0.05) when the load applied was below the training loading range. The intraoperative load sensor demonstrated good accuracy and precision over the whole surface and over a wide range of load values. Minor improvements to the software could greatly improve the results of the sensor. Having a reliable and robust sensor could greatly improve advancements in all joint surgeries.
https://eprints.bournemouth.ac.uk/39460/
Source: Scopus
Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements.
Authors: Al-Nasser, S., Noroozi, S., Harvey, A., Aslani, N. and Haratian, R.
Journal: Sensors (Basel)
Volume: 24
Issue: 2
eISSN: 1424-8220
DOI: 10.3390/s24020585
Abstract:Using tibial sensors in total knee replacements (TKRs) can enhance patient outcomes and reduce early revision surgeries, benefitting hospitals, the National Health Services (NHS), stakeholders, biomedical companies, surgeons, and patients. Having a sensor that is accurate, precise (over the whole surface), and includes a wide range of loads is important to the success of joint force tracking. This research aims to investigate the accuracy of a novel intraoperative load sensor for use in TKRs. This research used a self-developed load sensor and artificial intelligence (AI). The sensor is compatible with Zimmer's Persona Knee System and adaptable to other knee systems. Accuracy and precision were assessed, comparing medial/lateral compartments inside/outside the sensing area and below/within the training load range. Five points were tested on both sides (medial and lateral), inside and outside of the sensing region, and with a range of loads. The average accuracy of the sensor was 83.41% and 84.63% for the load and location predictions, respectively. The highest accuracy, 99.20%, was recorded from inside the sensing area within the training load values, suggesting that expanding the training load range could enhance overall accuracy. The main outcomes were that (1) the load and location predictions were similar in accuracy and precision (p > 0.05) in both compartments, (2) the accuracy and precision of both predictions inside versus outside of the triangular sensing area were comparable (p > 0.05), and (3) there was a significant difference in the accuracy of load and location predictions (p < 0.05) when the load applied was below the training loading range. The intraoperative load sensor demonstrated good accuracy and precision over the whole surface and over a wide range of load values. Minor improvements to the software could greatly improve the results of the sensor. Having a reliable and robust sensor could greatly improve advancements in all joint surgeries.
https://eprints.bournemouth.ac.uk/39460/
Source: PubMed
Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements
Authors: Al-Nasser, S., Noroozi, S., Harvey, A., Aslani, N. and Haratian, R.
Journal: SENSORS
Volume: 24
Issue: 2
eISSN: 1424-8220
DOI: 10.3390/s24020585
https://eprints.bournemouth.ac.uk/39460/
Source: Web of Science (Lite)
Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements.
Authors: Al-Nasser, S., Noroozi, S., Harvey, A., Aslani, N. and Haratian, R.
Journal: Sensors (Basel, Switzerland)
Volume: 24
Issue: 2
Pages: 585
eISSN: 1424-8220
ISSN: 1424-8220
DOI: 10.3390/s24020585
Abstract:Using tibial sensors in total knee replacements (TKRs) can enhance patient outcomes and reduce early revision surgeries, benefitting hospitals, the National Health Services (NHS), stakeholders, biomedical companies, surgeons, and patients. Having a sensor that is accurate, precise (over the whole surface), and includes a wide range of loads is important to the success of joint force tracking. This research aims to investigate the accuracy of a novel intraoperative load sensor for use in TKRs. This research used a self-developed load sensor and artificial intelligence (AI). The sensor is compatible with Zimmer's Persona Knee System and adaptable to other knee systems. Accuracy and precision were assessed, comparing medial/lateral compartments inside/outside the sensing area and below/within the training load range. Five points were tested on both sides (medial and lateral), inside and outside of the sensing region, and with a range of loads. The average accuracy of the sensor was 83.41% and 84.63% for the load and location predictions, respectively. The highest accuracy, 99.20%, was recorded from inside the sensing area within the training load values, suggesting that expanding the training load range could enhance overall accuracy. The main outcomes were that (1) the load and location predictions were similar in accuracy and precision (p > 0.05) in both compartments, (2) the accuracy and precision of both predictions inside versus outside of the triangular sensing area were comparable (p > 0.05), and (3) there was a significant difference in the accuracy of load and location predictions (p < 0.05) when the load applied was below the training loading range. The intraoperative load sensor demonstrated good accuracy and precision over the whole surface and over a wide range of load values. Minor improvements to the software could greatly improve the results of the sensor. Having a reliable and robust sensor could greatly improve advancements in all joint surgeries.
https://eprints.bournemouth.ac.uk/39460/
Source: Europe PubMed Central
Exploring the Performance of an Artificial Intelligence-Based Load Sensor for Total Knee Replacements.
Authors: Al-Nasser, S., Noroozi, S., Harvey, A., Aslani, N. and Haratian, R.
Journal: Sensors
Volume: 24
Issue: 2
ISSN: 1424-8220
Abstract:Using tibial sensors in total knee replacements (TKRs) can enhance patient outcomes and reduce early revision surgeries, benefitting hospitals, the National Health Services (NHS), stakeholders, biomedical companies, surgeons, and patients. Having a sensor that is accurate, precise (over the whole surface), and includes a wide range of loads is important to the success of joint force tracking. This research aims to investigate the accuracy of a novel intraoperative load sensor for use in TKRs. This research used a self-developed load sensor and artificial intelligence (AI). The sensor is compatible with Zimmer's Persona Knee System and adaptable to other knee systems. Accuracy and precision were assessed, comparing medial/lateral compartments inside/outside the sensing area and below/within the training load range. Five points were tested on both sides (medial and lateral), inside and outside of the sensing region, and with a range of loads. The average accuracy of the sensor was 83.41% and 84.63% for the load and location predictions, respectively. The highest accuracy, 99.20%, was recorded from inside the sensing area within the training load values, suggesting that expanding the training load range could enhance overall accuracy. The main outcomes were that (1) the load and location predictions were similar in accuracy and precision (p > 0.05) in both compartments, (2) the accuracy and precision of both predictions inside versus outside of the triangular sensing area were comparable (p > 0.05), and (3) there was a significant difference in the accuracy of load and location predictions (p < 0.05) when the load applied was below the training loading range. The intraoperative load sensor demonstrated good accuracy and precision over the whole surface and over a wide range of load values. Minor improvements to the software could greatly improve the results of the sensor. Having a reliable and robust sensor could greatly improve advancements in all joint surgeries.
https://eprints.bournemouth.ac.uk/39460/
Source: BURO EPrints