Dataset Information

Imageless robotic total knee arthroplasty determines similar coronal plane alignment of the knee (CPAK) parameters to long leg radiographs.

ABSTRACT:

Background

The coronal plane alignment of the knee (CPAK) classification was first developed using long leg radiographs (LLR) and has since been reported using image-based and imageless robotic total knee arthroplasty (TKA) systems. However, the correspondence between imageless robotics and LLR-derived CPAK parameters has yet to be investigated. This study therefore examined the differences in CPAK parameters determined with LLR and imageless robotic navigation using either generic or optimized cartilage wear assumptions.

Methods

Medial proximal tibial angle (MPTA) and lateral distal femoral angle (LDFA) were determined from the intraoperative registration data of 61 imageless robotic TKAs using either a generic 2 mm literature-based wear assumption (Nav_lit) or an optimized wear assumption (Nav_opt) found using an error minimization algorithm. MPTA and LDFA were also measured from preoperative LLR by two observers and intraclass correlation coefficients (ICCs) were calculated. MPTA, LDFA, joint line obliquity (JLO), and arithmetic hip-knee-ankle angle (aHKA) were compared between the robotic and the average LLR measurements over the two observers.

Results

ICCs between observers for LLR were over 0.95 for MPTA, LDFA, JLO, and aHKA, indicating excellent agreement. Mean CPAK differences were not significant between LLR and Nav_lit (all differences within 0.6°, P > 0.1) or Nav_opt (all within 0.1°, P > 0.83). Mean absolute errors (MAE) between LLR and Nav_lit were: LDFA = 1.4°, MPTA = 2.0°, JLO = 2.1°, and aHKA = 2.7°. Compared to LLR, the generic wear classified 88% and the optimized wear classified 94% of knees within one CPAK group. Bland-Altman comparisons reported good agreement for LLR vs. Nav_lit and Nav_opt, with > 95% and > 91.8% of measurements within the limits of agreement across all CPAK parameters, respectively.

Conclusions

Imageless robotic navigation data can be used to calculate CPAK parameters for arthritic knees undergoing TKA with good agreement to LLR. Generic wear assumptions determined MPTA and LDFA with MAE within 2° and optimizing wear assumptions showed negligible improvement.

SUBMITTER: Edelstein AI

PROVIDER: S-EPMC10909262 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Imageless robotic total knee arthroplasty determines similar coronal plane alignment of the knee (CPAK) parameters to long leg radiographs.

Edelstein Adam I AI Orsi Alexander D AD Plaskos Christopher C Coffey Simon S Suleiman Linda I LI

Arthroplasty (London, England) 20240303 1

<h4>Background</h4>The coronal plane alignment of the knee (CPAK) classification was first developed using long leg radiographs (LLR) and has since been reported using image-based and imageless robotic total knee arthroplasty (TKA) systems. However, the correspondence between imageless robotics and LLR-derived CPAK parameters has yet to be investigated. This study therefore examined the differences in CPAK parameters determined with LLR and imageless robotic navigation using either generic or op ...[more]

PMID: 38431650

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Project description:PurposeTo describe a strategy for coronal alignment using a computed tomography (CT) based custom total knee arthroplasty (TKA) system, and to evaluate the agreement between the planned and postoperative Hip-Knee-Ankle (HKA) angle, Femoral Mechanical Angle (FMA) and Tibial Mechanical Angle (TMA).MethodsFrom a consecutive series of 918 primary TKAs, 266 (29%) knees received CT-based posterior-stabilized cemented custom TKA. In addition to a preoperative CT-scan, pre- and post-operative radiographs of weight-bearing long leg, anterior-posterior and lateral views of the knee were obtained, on which the FMA, TMA and HKA angles were measured. CT-based three-dimensional (3D) models enabled to correct for cases with bony wear by referring to the non-worn areas and to estimate the native pre-arthritic angles. The alignment technique aimed to preserve or restore constitutional alignment (CA) within predetermined limits, by defining a 'target zone' based on three criteria: 1) a ± 3° (range 87°-93°) primary tolerance for the femoral and tibial resections; 2) a ± 2° secondary tolerance for component obliquity, extending the bounds for FMA and TMA (range 85°-95°); 3) a planned HKA angle range of 175°-183°. Agreement between preoperative, planned and postoperative measurements of FMA, TMA and HKA angle were calculated using intra-class correlation coefficients (ICC).ResultsPreoperative radiograph and CT-scan measurements revealed that, respectively, 73 (28%) and 103 (40%) knees were in the 'target zone', whereas postoperative radiographs revealed that 217 (84%) TKAs were in the 'target zone'. Deviation from the planned angles were - 0.5° ± 1.8° for FMA, - 0.5° ± 1.8° for TMA, and - 1.1° ± 2.1° for HKA angle. Finally, the agreement between the planned and achieved targets, indicated by ICC, were good for FMA (0.701), fair for TMA (0.462) and fair for HKA angle (0.472).ConclusionUsing this strategy for coronal alignment, 84% of custom TKAs were within the 'target zone' for FMA, TMA and HKA angles. These findings support the concepts of emerging personalized medicine technologies, and emphasise the importance of accurate strategies for preoperative planning, which are key to achieving satisfactory 'personalised alignment' that can further be improved by customisation of implant components.Level of evidenceIV.

Project description:Background and objectives: The introduction of novel techniques in total knee arthroplasty (TKA) aiming to enhance outcomes and satisfaction of the procedure is constantly ongoing. In order to evidence a priority of one, we have conducted a randomized controlled trial with the aim of comparing patient-reported functional outcomes, radiographic outcomes and intraoperative measures between imageless (NAVIO and CORI), robotic-assisted (ra)- TKA (ra-TKA) and manual TKA (mTKA) for primary knee osteoarthritis (KOA). Materials and Methods: A total of 215 patients with the diagnosis of KOA of the knee were randomly assigned to one of the three groups: NAVIO (76 patients) or CORI (71 patients) robotic-assisted TKA, or manual technique (68 patients) TKA. The primary outcome (Knee Injury and Osteoarthritis Outcome Study [KOOS]), Visual Analogue Scale (VAS), Range of motion (ROM), femoral component rotational alignment and the secondary outcomes (surgery time, blood loss, complications, and revision at 12 months after surgery) were compared between three groups. KOOS and VAS were collected at particular follow up visits from each patient individually and ROM in flexion and extension was assessed during the physical examination. Femoral component rotational alignment was measured on the CT scan performed postoperatively utilizing the Berger's method. Statistical significance was set at p < 0.05. Results: Both the ra-TKA groups and mTKA group displayed significant improvements in the majority of the functional outcome scores at 12 months. Despite having more prominent surgery time (NAVIO: mean +44.5 min in comparison to mTKA and CORI: mean +38.5 min in comparison to mTKA), both NAVIO and CORI tend to achieve highly accurate femoral component rotational alignment with mean radiographic scores in NAVIO vs. CORI vs. mTKA of 1.48° vs. 1.33° vs. 3.15° and lower blood loss (NAVIO: 1.74; CORI: 1.51; mTKA: 2.32. Furthermore, the investigation revealed the significant difference in femoral component rotational alignment between mTKA-NAVIO and mTKA-CORI and significantly different KOOS scores in NAVIO vs. CORI vs. mTKA of 87.05 vs. 85.59 vs. 81.76. Furthermore, the KOOS analysis showed between group significant statistical differences, but did not reach minimal clinically significant difference. There were no differences in postoperative ROM and VAS. There were no differences in complications between groups. Conclusions: To achieve a successful TKA, the precise tool and individualised objective is of great importance. The results suggest satisfactory results after both ra-TKA methods and mTKA. Ra-TKA and mTKA stand for a safe and reliable treatment method for OA. Patients reported excellent alleviation in functional outcomes and the radiological results revealed that the better precision does not necessarily lead to a better outcome. Therefore, ra-TKA does not imply strong enough advantages in comparison to the manual method, especially in terms of cost-efficiency and surgical time.

Project description:BackgroundAchieving balanced gaps is a key surgical goal in total knee arthroplasty, yet most methods rely on subjective surgeon feel and experience to assess and achieve knee balance intraoperatively. Our objective was to evaluate the ability to quantitatively plan and achieve a balanced knee throughout the range of motion using robotic-assisted instrumentation in a tibia-first, gap-balancing technique.MethodsA robotic-assisted, gap-balancing technique was used in 121 consecutive knees. After resection of the proximal tibia, a computer-controlled tensioning device was inserted into the knee joint and the pre-femoral-resection knee gaps were acquired dynamically throughout flexion under controlled load. Predicted gap profiles were used to plan the femoral implant by adjusting the implant alignment and position within certain boundaries to achieve a balanced knee throughout the range of flexion. Femoral cuts were then made according to this plan using a miniature robotic-assisted cutting guide. The tensioning device used to measure the pre-femoral-resection gaps was then reinserted into the joint to quantify the final gap balance under known tension. The final gap profiles were then compared with the predictive gap plans.ResultsThe overall root mean square error between the predicted and achieved gaps was 1.3 mm and 1.5 mm for the medial and lateral sides, respectively. Use of robotic assistance resulted in over 90% of knees having mediolateral balance within 2 mm across the flexion range. Gaps at 0° flexion were 2 mm smaller than the gaps at 90°. This difference decreased to less than 1 mm when comparing the tibiofemoral gaps at 10°, 45°, and 90°.ConclusionsImageless, robotic-assisted total knee arthroplasty accurately predicts postoperative gaps before femoral resections. This allows surgeons to virtually plan femoral implant alignment and optimize gap balance throughout the range of motion. The accurate prediction of gaps throughout the arc of motion combined with precise, robotically assisted femoral resection produces accurate postoperative ligament balance consistently.

Project description:PurposeTo develop and validate a deep learning-based method for automatic quantitative analysis of lower-extremity alignment.Materials and methodsIn this retrospective study, bilateral long-leg radiographs (LLRs) from 255 patients that were obtained between January and September of 2018 were included. For training data (n = 109), a U-Net convolutional neural network was trained to segment the femur and tibia versus manual segmentation. For validation data (n = 40), model parameters were optimized. Following identification of anatomic landmarks, anatomic and mechanical axes were identified and used to quantify alignment through the hip-knee-ankle angle (HKAA) and femoral anatomic-mechanical angle (AMA). For testing data (n = 106), algorithm-based angle measurements were compared with reference measurements by two radiologists. Angles and time for 30 random radiographs were compared by using repeated-measures analysis of variance and one-way analysis of variance, whereas correlations were quantified by using Pearson r and intraclass correlation coefficients.ResultsBilateral LLRs of 255 patients (mean age, 26 years ± 23 [standard deviation]; range, 0-88 years; 157 male patients) were included. Mean Sørensen-Dice coefficients for segmentation were 0.97 ± 0.09 for the femur and 0.96 ± 0.11 for the tibia. Mean HKAAs and AMAs as measured by the readers and the algorithm ranged from 0.05° to 0.11° (P = .5) and from 4.82° to 5.43° (P < .001). Interreader correlation coefficients ranged from 0.918 to 0.995 (r range, P < .001), and agreement was almost perfect (intraclass correlation coefficient range, 0.87-0.99). Automatic analysis was faster than the two radiologists' manual measurements (3 vs 36 vs 35 seconds, P < .001).ConclusionFully automated analysis of LLRs yielded accurate results across a wide range of clinical and pathologic indications and is fast enough to enhance and accelerate clinical workflows.Supplemental material is available for this article.© RSNA, 2020See also commentary by Andreisek in this issue.

Dataset Information

Imageless robotic total knee arthroplasty determines similar coronal plane alignment of the knee (CPAK) parameters to long leg radiographs.

Background

Methods

Results

Conclusions

Publications

Imageless robotic total knee arthroplasty determines similar coronal plane alignment of the knee (CPAK) parameters to long leg radiographs.

Similar Datasets

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