Native extension and flexion joint gaps are primarily measured intraoperatively using devices such as navigation systems or tensioners, but there are advantages to being able to pre-operatively plan to such gaps. This study aims to validate the ability of a novel distracted joint gap radiology protocol to measure pre-operative extension andflexion joint gaps. A retrospective study comprised of 42 knees was performed. Patient imaging was obtained and used to perform segmentation, landmarking and 3D-to-2D registration. The pre-operative medial and lateral joint gaps were determined in extension and flexion. Intraoperatively, a range of motion analysis was conducted using the Brainlab Knee 3 navigation system to measure the joint gaps in extension and flexion.In extension, both medial and lateral pre-operative radiological and intraoperative navigated gaps displayed moderate and statistically significant correlations (r=0.45; p=0.003 for medial and r=0.4; p=0.01 for lateral). In flexion, only the medial radiological and navigated joint gaps correlated (r=0.54, p<0.001), with a not statistically significant trend for the lateral flexion joint gaps.The moderate and statistically significant correlations between these joint gaps to those measured intraoperatively suggests they are reflective of on the table experience with patients. Although further work is required to understand if differences are attributable to variability in the radiological or intra-operative assessments, the pre- operative analysis technique described in this study provides the opportunity to develop a more holistic pre-operative surgical plan which considers the state of both hard and soft tissue within the joint.
{"title":"Comparison of a Novel Joint Distraction Radiology Protocol in Total Knee Arthroplasty Planning with Navigated Joint Gaps","authors":"David W. Liu, Ishaan Jagota, J. Twiggs, B. Miles","doi":"10.29007/j6kh","DOIUrl":"https://doi.org/10.29007/j6kh","url":null,"abstract":"Native extension and flexion joint gaps are primarily measured intraoperatively using devices such as navigation systems or tensioners, but there are advantages to being able to pre-operatively plan to such gaps. This study aims to validate the ability of a novel distracted joint gap radiology protocol to measure pre-operative extension andflexion joint gaps. A retrospective study comprised of 42 knees was performed. Patient imaging was obtained and used to perform segmentation, landmarking and 3D-to-2D registration. The pre-operative medial and lateral joint gaps were determined in extension and flexion. Intraoperatively, a range of motion analysis was conducted using the Brainlab Knee 3 navigation system to measure the joint gaps in extension and flexion.In extension, both medial and lateral pre-operative radiological and intraoperative navigated gaps displayed moderate and statistically significant correlations (r=0.45; p=0.003 for medial and r=0.4; p=0.01 for lateral). In flexion, only the medial radiological and navigated joint gaps correlated (r=0.54, p<0.001), with a not statistically significant trend for the lateral flexion joint gaps.The moderate and statistically significant correlations between these joint gaps to those measured intraoperatively suggests they are reflective of on the table experience with patients. Although further work is required to understand if differences are attributable to variability in the radiological or intra-operative assessments, the pre- operative analysis technique described in this study provides the opportunity to develop a more holistic pre-operative surgical plan which considers the state of both hard and soft tissue within the joint.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127593246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Angibaud, Wen Fan, Florian Kerveillant, P. Dubard, Marine Torrollion, M. Rueff, A. Sah, J. Huddleston
Total knee replacement (TKA) represents a well-established reconstructive procedure for end-stage knee joint disorders with the balancing of soft-tissue envelope throughout the full arc of motion as a newly emerging possibility. This cadaveric study evaluated the ability to achieve targeted mediolateral (ML) gap balance throughout the arc of motion using conventional mechanical instrumentation versus a computer-assisted orthopaedic surgery (CAOS) system featuring an intraarticular distractor while considering surgeon experience level. For the CAOS system, an intraarticular distractor applied a quasi- constant distraction force to the joint (instrumented) while the conventional system involved conventional spacers. Regardless of experience level, the instrumented TKAs were associated with a significantly lower ML gap differential than the conventional TKAs. In contrast, regardless of the type of instrumentation, there were no significant differences between the junior and senior surgeon mean gaps. Historically, soft tissue balancing during TKA has been reported as an art rather than a science. In this regard, the addition of dedicated technology to characterize the soft-tissue envelope during TKA has the potential to provide an augmented perspective to the surgeon and can be particularly beneficial for junior surgeons. The present study established that the usage of instrumented CAOS led to significantly lower ML gap differences than conventional instrumentation.
{"title":"Improved Mediolateral Gap Balance Achievement with Instrumented Navigated Total Knee Arthroplasty Compared to Conventional Instrumentation","authors":"L. Angibaud, Wen Fan, Florian Kerveillant, P. Dubard, Marine Torrollion, M. Rueff, A. Sah, J. Huddleston","doi":"10.29007/4lwm","DOIUrl":"https://doi.org/10.29007/4lwm","url":null,"abstract":"Total knee replacement (TKA) represents a well-established reconstructive procedure for end-stage knee joint disorders with the balancing of soft-tissue envelope throughout the full arc of motion as a newly emerging possibility. This cadaveric study evaluated the ability to achieve targeted mediolateral (ML) gap balance throughout the arc of motion using conventional mechanical instrumentation versus a computer-assisted orthopaedic surgery (CAOS) system featuring an intraarticular distractor while considering surgeon experience level. For the CAOS system, an intraarticular distractor applied a quasi- constant distraction force to the joint (instrumented) while the conventional system involved conventional spacers. Regardless of experience level, the instrumented TKAs were associated with a significantly lower ML gap differential than the conventional TKAs. In contrast, regardless of the type of instrumentation, there were no significant differences between the junior and senior surgeon mean gaps. Historically, soft tissue balancing during TKA has been reported as an art rather than a science. In this regard, the addition of dedicated technology to characterize the soft-tissue envelope during TKA has the potential to provide an augmented perspective to the surgeon and can be particularly beneficial for junior surgeons. The present study established that the usage of instrumented CAOS led to significantly lower ML gap differences than conventional instrumentation.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127685877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prashant U. Pandey, B. Hohlmann, Peter Brößner, I. Hacihaliloglu, Keiran Barr, T. Ungi, O. Zettinig, R. Prevost, G. Dardenne, Zian Fanti, W. Wein, E. Stindel, F. A. Cosío, P. Guy, G. Fichtinger, K. Radermacher, A. Hodgson
Ultrasound (US) bone segmentation is an important component of US-guided or- thopaedic procedures. While there are many published segmentation techniques, there is no direct way to compare their performance. We present a solution to this, by curating a multi-institutional set of US images and corresponding segmentations, and systematically evaluating six previously-published bone segmentation algorithms using consistent metric definitions. We find that learning-based segmentation methods outperform traditional al- gorithms that rely on hand-crafted image features, as measured by their Dice scores, RMS distance errors and segmentation success rates. However, there is no single best performing algorithm across the datasets, emphasizing the need for carefully evaluating techniques on large, heterogenous datasets. The datasets and evaluation framework described can be used to accelerate development of new segmentation algorithms.
{"title":"Standardized Evaluation of Current Ultrasound Bone Segmentation Algorithms on Multiple Datasets","authors":"Prashant U. Pandey, B. Hohlmann, Peter Brößner, I. Hacihaliloglu, Keiran Barr, T. Ungi, O. Zettinig, R. Prevost, G. Dardenne, Zian Fanti, W. Wein, E. Stindel, F. A. Cosío, P. Guy, G. Fichtinger, K. Radermacher, A. Hodgson","doi":"10.29007/q51n","DOIUrl":"https://doi.org/10.29007/q51n","url":null,"abstract":"Ultrasound (US) bone segmentation is an important component of US-guided or- thopaedic procedures. While there are many published segmentation techniques, there is no direct way to compare their performance. We present a solution to this, by curating a multi-institutional set of US images and corresponding segmentations, and systematically evaluating six previously-published bone segmentation algorithms using consistent metric definitions. We find that learning-based segmentation methods outperform traditional al- gorithms that rely on hand-crafted image features, as measured by their Dice scores, RMS distance errors and segmentation success rates. However, there is no single best performing algorithm across the datasets, emphasizing the need for carefully evaluating techniques on large, heterogenous datasets. The datasets and evaluation framework described can be used to accelerate development of new segmentation algorithms.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121636658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clément Daviller, S. Polakovic, A. Greene, F. Bertrand
Reference axis based on Friedman’s approach is widely recognized as an anatomic landmark from which to measure and compare implant parameters within preoperative planning software for total shoulder arthroplasty. Equinoxe Planning Application (ExactechInc.) offers 3D measurements techniques for glenoid version and inclination requiring meticulous placement of trigonum and glenoid center. We propose as automatic determination of this reference axis, based on deep learning that shown a median error of less than 1°.
{"title":"Automatic Friedman’s Axis placement via the use of deep learning algorithms","authors":"Clément Daviller, S. Polakovic, A. Greene, F. Bertrand","doi":"10.29007/r8cp","DOIUrl":"https://doi.org/10.29007/r8cp","url":null,"abstract":"Reference axis based on Friedman’s approach is widely recognized as an anatomic landmark from which to measure and compare implant parameters within preoperative planning software for total shoulder arthroplasty. Equinoxe Planning Application (ExactechInc.) offers 3D measurements techniques for glenoid version and inclination requiring meticulous placement of trigonum and glenoid center. We propose as automatic determination of this reference axis, based on deep learning that shown a median error of less than 1°.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122519638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ricardo Antunes, P. Jacob, A. Meyer, R. Marchand, M. Verstraete
Remote patient monitoring, using wearable devices and connected patient engagement platforms has the potential to improve timely clinical decisions. Data collected from multiple patients, including using the remote engagement platforms themselves, can be used to produce evidence-based reference to support clinical decisions. While some normative references for functional measure currently exist for total knee arthroplasty (TKA), these are still lacking for VAS pain scores. Therefore, VAS pain scores on a 10-point Likert scale were analyzed for 66 patients, each reporting at least five scores in the 180 days following surgery. These were used to produce a normative recovery model for total knee arthroplasty patients. A nonlinear mixed effects model was fitted, whereby the response variable is assumed to be distributed following a beta-binomial distribution. The population mean trend showed a with wide dispersion in the first few days following surgery, showing scores ranging throughout the 10-point scale. After the first week, the expected pain score steadily decreases, resulting in a score no higher than one in 50% of the population beyond 90 days after surgery. The fitted model allows referencing individual patient's pain scores at different stages of recovery, against the model’s predicted distribution. This approach can support early detection of patients that significantly deviate from the reference model and be a useful integration into clinical decision support software tools.
{"title":"Feeling Better After TKA: Reference chart for remotely collected pain scores","authors":"Ricardo Antunes, P. Jacob, A. Meyer, R. Marchand, M. Verstraete","doi":"10.29007/mlnb","DOIUrl":"https://doi.org/10.29007/mlnb","url":null,"abstract":"Remote patient monitoring, using wearable devices and connected patient engagement platforms has the potential to improve timely clinical decisions. Data collected from multiple patients, including using the remote engagement platforms themselves, can be used to produce evidence-based reference to support clinical decisions. While some normative references for functional measure currently exist for total knee arthroplasty (TKA), these are still lacking for VAS pain scores. Therefore, VAS pain scores on a 10-point Likert scale were analyzed for 66 patients, each reporting at least five scores in the 180 days following surgery. These were used to produce a normative recovery model for total knee arthroplasty patients. A nonlinear mixed effects model was fitted, whereby the response variable is assumed to be distributed following a beta-binomial distribution. The population mean trend showed a with wide dispersion in the first few days following surgery, showing scores ranging throughout the 10-point scale. After the first week, the expected pain score steadily decreases, resulting in a score no higher than one in 50% of the population beyond 90 days after surgery. The fitted model allows referencing individual patient's pain scores at different stages of recovery, against the model’s predicted distribution. This approach can support early detection of patients that significantly deviate from the reference model and be a useful integration into clinical decision support software tools.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126959934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jean-Rassaire Fouefack, G. Dardenne, Bhushan S Borotikar, Tinashe Ernest Mutsvangwa, V. Burdin
In clinical routine, the capture of three-dimensional (3D) bone geometry is crucial for surgical planning, implant placement and postoperative evaluation. Nevertheless, accurate 3D reconstruction of the knee joint for the estimation of patient-specific features remains a challenge, although it has been widely studied. In this context, statistical shape models (SSM) have been used to reconstruct a global shape from partial observations, based on their ability to capture the anatomical variation from different patients. However, these studies incorporate single object SSMs which limit their application for analyzing local bone morphology and thus they lack the capacity to analyze the human anatomy at the joint level. In this paper, we present a multi-object based framework for the 3D reconstruction of the knee joint using a dynamic multi-object Gaussian process model (DMO-GPM) and an adapted Markov Chain Monte Carlo (MCMC) based model fitting algorithm.The knees were reconstructed with an average mean square error of 1.81±0.37 mm and maximum error of 3.31 mm corresponding to the surface-to-surface distance between the predicted and original knees. The results show that the knee is accurately reconstructed, especially around the joint contact surfaces. This is crucial because most of the patient- specific features required for the implant design, use landmarks in this area. The results suggest that the approach is robust and accurate to design personalized knee implants.
{"title":"3D reconstruction of joints from partial data using multi-object-based model: Towards a patient-specific knee implant design","authors":"Jean-Rassaire Fouefack, G. Dardenne, Bhushan S Borotikar, Tinashe Ernest Mutsvangwa, V. Burdin","doi":"10.29007/dcj8","DOIUrl":"https://doi.org/10.29007/dcj8","url":null,"abstract":"In clinical routine, the capture of three-dimensional (3D) bone geometry is crucial for surgical planning, implant placement and postoperative evaluation. Nevertheless, accurate 3D reconstruction of the knee joint for the estimation of patient-specific features remains a challenge, although it has been widely studied. In this context, statistical shape models (SSM) have been used to reconstruct a global shape from partial observations, based on their ability to capture the anatomical variation from different patients. However, these studies incorporate single object SSMs which limit their application for analyzing local bone morphology and thus they lack the capacity to analyze the human anatomy at the joint level. In this paper, we present a multi-object based framework for the 3D reconstruction of the knee joint using a dynamic multi-object Gaussian process model (DMO-GPM) and an adapted Markov Chain Monte Carlo (MCMC) based model fitting algorithm.The knees were reconstructed with an average mean square error of 1.81±0.37 mm and maximum error of 3.31 mm corresponding to the surface-to-surface distance between the predicted and original knees. The results show that the knee is accurately reconstructed, especially around the joint contact surfaces. This is crucial because most of the patient- specific features required for the implant design, use landmarks in this area. The results suggest that the approach is robust and accurate to design personalized knee implants.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126688471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Hess, Philipp Gussarow, J. T. Rojas, Stefan Weber, Annabel Hayoz, M. Zumstein, Kate Gerber
Rotator cuff tears (RCT) are one of the most common sources of shoulder pain. Many factors can be considered to choose the right surgical treatment procedure. Of the most important factors are the tear retraction and tear width, assessed manually on preoperative MRI.A novel approach to automatically quantify a rotator cuff tear, based on the segmentation of the tear from MRI images, was developed and validated. For segmentation, a neural network was trained and methods for the automatic calculation of the tear width and retraction from the segmented tear volume were developed.The accuracy of the automatic segmentation and the automated tear analysis were evaluated relative to manual consensus segmentations by two clinical experts. Variance in the manual segmentations was assessed in an interrater variability study of two clinical experts.The accuracy of the tear retraction calculation based on the developed automatic tear segmentation was 5.3 mm ± 5.0 mm in comparison to the interrater variability of tear retraction calculation based on manual segmentations of 3.6 mm ± 2.9 mm.These results show that an automatic quantification of a rotator cuff tear is possible. The large interrater variability of manual segmentation-based measurements highlights the difficulty of the tear segmentations task in general.
{"title":"Fully Automatic Analysis of Posterosuperior Full-Thickness Rotator Cuff Tears from MRI","authors":"H. Hess, Philipp Gussarow, J. T. Rojas, Stefan Weber, Annabel Hayoz, M. Zumstein, Kate Gerber","doi":"10.29007/fnjd","DOIUrl":"https://doi.org/10.29007/fnjd","url":null,"abstract":"Rotator cuff tears (RCT) are one of the most common sources of shoulder pain. Many factors can be considered to choose the right surgical treatment procedure. Of the most important factors are the tear retraction and tear width, assessed manually on preoperative MRI.A novel approach to automatically quantify a rotator cuff tear, based on the segmentation of the tear from MRI images, was developed and validated. For segmentation, a neural network was trained and methods for the automatic calculation of the tear width and retraction from the segmented tear volume were developed.The accuracy of the automatic segmentation and the automated tear analysis were evaluated relative to manual consensus segmentations by two clinical experts. Variance in the manual segmentations was assessed in an interrater variability study of two clinical experts.The accuracy of the tear retraction calculation based on the developed automatic tear segmentation was 5.3 mm ± 5.0 mm in comparison to the interrater variability of tear retraction calculation based on manual segmentations of 3.6 mm ± 2.9 mm.These results show that an automatic quantification of a rotator cuff tear is possible. The large interrater variability of manual segmentation-based measurements highlights the difficulty of the tear segmentations task in general.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116038188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aouam Djamel, R. Querrec, Thierry Duval, N. Zenati, C. Hamitouche
Minimally invasive intervention requires accuracy and practice as it can be vital in complex and narrow places. In this paper we propose a solution based on augmented reality (AR) for the ablation of bone tumors. Our proposal deals with the preoperative and intraoperative phases of the procedure. The first part consists of the segmentation and 3D reconstruction of the structures of interest. The second part consists of the visualization in AR. This solution is intended to facilitate the tasks of surgeons and radiologists when planning RF needle insertion and trajectory in order to avoid excessive exposure to X-rays, which is a phase that requires more precision and knowledge of the morphology of the mass tumor. The second part offers AR assistance based on the planning of the preoperative phase. The solution we proposed is based on the use of HoloLens 2 headsets to provide better AR visualization and assistance.
{"title":"Mixed reality for minimally invasive Bone Tumor ablation","authors":"Aouam Djamel, R. Querrec, Thierry Duval, N. Zenati, C. Hamitouche","doi":"10.29007/jzrh","DOIUrl":"https://doi.org/10.29007/jzrh","url":null,"abstract":"Minimally invasive intervention requires accuracy and practice as it can be vital in complex and narrow places. In this paper we propose a solution based on augmented reality (AR) for the ablation of bone tumors. Our proposal deals with the preoperative and intraoperative phases of the procedure. The first part consists of the segmentation and 3D reconstruction of the structures of interest. The second part consists of the visualization in AR. This solution is intended to facilitate the tasks of surgeons and radiologists when planning RF needle insertion and trajectory in order to avoid excessive exposure to X-rays, which is a phase that requires more precision and knowledge of the morphology of the mass tumor. The second part offers AR assistance based on the planning of the preoperative phase. The solution we proposed is based on the use of HoloLens 2 headsets to provide better AR visualization and assistance.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122915112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Souipas, Stephen Laws, F. Rodriguez y Baena, B. Davies
This paper describes Signature Robot, a cooperative haptic robot for knee surgery. Designed to address the lessons learned from the pioneering Acrobot Company ltd, this novel platform allows low and even impedance motion across 3 degrees of freedom, whilst the implementation of active constraints ensures patient safety throughout surgery. The robot was demonstrated to have an average positional accuracy of 0.82mm.
{"title":"Towards Miniaturised Collaborative Haptic Robots For Computer Aided Knee Surgery: Signature Robot","authors":"S. Souipas, Stephen Laws, F. Rodriguez y Baena, B. Davies","doi":"10.29007/h469","DOIUrl":"https://doi.org/10.29007/h469","url":null,"abstract":"This paper describes Signature Robot, a cooperative haptic robot for knee surgery. Designed to address the lessons learned from the pioneering Acrobot Company ltd, this novel platform allows low and even impedance motion across 3 degrees of freedom, whilst the implementation of active constraints ensures patient safety throughout surgery. The robot was demonstrated to have an average positional accuracy of 0.82mm.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129164914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Di Paolo, S. Fratini, A. Meena, S. Bignozzi, G. M. Marcheggiani Muccioli, S. Zaffagnini
The purpose of the present study was to associate the intraoperative kinematics acquired with a computer navigation system with long-term clinical outcomes and survivorship in patients undergoing TKA to investigate the role of constraint in patients’ satisfaction.A surgical navigation system was used to verify bone resections, gaps, and implant positioning during TKA. Kinematic examination, i.e. varus-valgus at full-extended knee (VV0), varus-valgus at 30° of flexion (VV30), anterior/posterior displacement at 90° of flexion (AP90), passive range of motion (ROM) were performed. Long-term clinical assessment interviews were performed. The Knee Injury and Osteoarthritis Outcome Score (KOOS) was used to investigate patients’ clinical and functional status.Out of 165 patients, 120 met the inclusion criteria. The average follow-up time was 7.7±2.8 years. 7 patients had undergone revision surgery and were considered as a surgical failure with an overall survival rate of 94.2%, while the survival rate at 6, 8, 10 years was 98.8%, 97.4%, 93.6%, respectively. Clinical failure (KOOS score <70) was detected in 11 (9.2%), 10 (8.3%), 21 (17.5%), 39 (32.5%), 113 (94.2%) patients for the Symptoms, Pain, ADL, QoL, and Sport sub-scores, respectively. A statistically significant difference was found in KOOS-QoL between patients with and without clinical failure for the VV0 test (ES=0.58, p=0.022), with lower laxity for patients with score<70.Over-constraint kinematics during TKA surgery leads to worse clinical outcomes at long-term follow-up. Surgeons should be aware of the intraoperative ligament balancing and avoid over-constraint, especially in PS TKA designs.
{"title":"Over Constraint Varus Valgus Laxity Leads to Worse Clinical Outcomes at Long Term Follow Up in Total Knee Arthroplasty: Intraoperative Assessment through Surgical Navigation System","authors":"S. Di Paolo, S. Fratini, A. Meena, S. Bignozzi, G. M. Marcheggiani Muccioli, S. Zaffagnini","doi":"10.29007/tz3f","DOIUrl":"https://doi.org/10.29007/tz3f","url":null,"abstract":"The purpose of the present study was to associate the intraoperative kinematics acquired with a computer navigation system with long-term clinical outcomes and survivorship in patients undergoing TKA to investigate the role of constraint in patients’ satisfaction.A surgical navigation system was used to verify bone resections, gaps, and implant positioning during TKA. Kinematic examination, i.e. varus-valgus at full-extended knee (VV0), varus-valgus at 30° of flexion (VV30), anterior/posterior displacement at 90° of flexion (AP90), passive range of motion (ROM) were performed. Long-term clinical assessment interviews were performed. The Knee Injury and Osteoarthritis Outcome Score (KOOS) was used to investigate patients’ clinical and functional status.Out of 165 patients, 120 met the inclusion criteria. The average follow-up time was 7.7±2.8 years. 7 patients had undergone revision surgery and were considered as a surgical failure with an overall survival rate of 94.2%, while the survival rate at 6, 8, 10 years was 98.8%, 97.4%, 93.6%, respectively. Clinical failure (KOOS score <70) was detected in 11 (9.2%), 10 (8.3%), 21 (17.5%), 39 (32.5%), 113 (94.2%) patients for the Symptoms, Pain, ADL, QoL, and Sport sub-scores, respectively. A statistically significant difference was found in KOOS-QoL between patients with and without clinical failure for the VV0 test (ES=0.58, p=0.022), with lower laxity for patients with score<70.Over-constraint kinematics during TKA surgery leads to worse clinical outcomes at long-term follow-up. Surgeons should be aware of the intraoperative ligament balancing and avoid over-constraint, especially in PS TKA designs.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127172337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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