Digital templating in primary total hip and knee arthroplasty

Digital Templating of Hip Arthroplasty Using Microsoft PowerPoint: A Pilot Study with Technical Details

Templating is essential in hip arthroplasty preparation, facilitating implant size prediction and surgical rehearsal. It ensures the selection of suitable implants according to patient anatomy and disease, aiming to minimize post-operative complications. Various templating methods exist, including traditional acetate templating on both analog and digital images, alongside digital templating on digital images, which is categorized into 2D and 3D approaches. Despite the popularity of acetate templating on digital images, challenges such as the requirement for physical templates and result preservation persist. To address these limitations, digital templating with software like OrthoSize and Orthoview has been suggested, although not universally accessible. This technical note advocates for Microsoft PowerPoint as an effective alternative for 2D digital templating, highlighting its user-friendly features for image manipulation without needing specialized software. The described method involves scanning acetate templates, adjusting the images in PowerPoint 365 for size, position, and calibration on patient radiographs, and demonstrating reliability through preliminary assessments, with intraclass correlation coefficient (ICC) values indicating a high level of agreement for cup and stem size (ICC = 0.860, 0.841, respectively) but moderate for neck length (ICC = 0.592). We have introduced a method for performing 2D digital templating in the clinical field without the need for specialized software dedicated to digital templating. We believe this method significantly improves the accessibility to 2D digital templating, which was previously limited by the need for digital templating software. Additionally, it enables surgeons to easily establish arthroplasty plans and share them, overcoming the limitations of acetate templates.

THA-Net: A Deep Learning Solution for Next-Generation Templating and Patient-specific Surgical Execution

BACKGROUND

This study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either unconditionally (algorithm chooses implants) or conditionally (surgeon chooses implants).

METHODS

The THA-Net is a deep learning algorithm which receives an input preoperative radiograph and subsequently replaces the target hip joint with THA implants to generate a synthetic yet realistic postoperative radiograph. We trained THA-Net on 356,305 pairs of radiographs from 14,357 patients from a single institution's total joint registry and evaluated the validity (quality of surgical execution) and realism (ability to differentiate real and synthetic radiographs) of its outputs against both human-based and software-based criteria.

RESULTS

The surgical validity of synthetic postoperative radiographs was significantly higher than their real counterparts (mean difference: 0.8 to 1.1 points on 10-point Likert scale, P < .001), but they were not able to be differentiated in terms of realism in blinded expert review. Synthetic images showed excellent validity and realism when analyzed with already validated deep learning models.

CONCLUSION

We developed a THA next-generation templating tool that can generate synthetic radiographs graded higher on ultimate surgical execution than real radiographs from training data. Further refinement of this tool may potentiate patient-specific surgical planning and enable technologies such as robotics, navigation, and augmented reality (an online demo of THA-Net is available at: https://demo.osail.ai/tha_net).

The accuracy of digital templating in the preoperative planning of total knee arthroplasties: A systematic review and meta-analysis

AIMS

Accurately predicting the implant size in total knee arthroplasties could increase the efficiency of the operation, decrease the costs associated with the procedure and result in improved patient outcomes. To substantiate its continued use, digital templating must demonstrate itself to be an accurate tool in predicting component size in order for surgeons to confidently use it to optimize the procedure.

METHODS

A systematic literature review was performed and identified 16 studies within the Pubmed, Ebsco and Ovid-Embase databases, with 1189 TKR prostheses included for analysis. A quality of evidence assessment was performed on each study depending on the study design. A random effects meta-analysis model was used to pool overall implant accuracy and the reported inter-rater agreement when performing digital templating and displayed in a forest plot. Meta-regression was used analyze potential factors that may affect the accuracy of digital templating.

RESULTS

The pooled proportion of accurate templates with 0 margin of error was found to be 56% (52-61, 95CI), which increases to 96% (0.94-0.98, 95CI) when allowing for a 1 size margin of error. Subgroup analysis between femoral and tibial components concluded no statistically significant difference.

CONCLUSIONS

This study supports the continued use of digital templating for planning total knee arthroplasties and recommends further subgroup analysis of patient age, body mass index and sex against accuracy. This review was registered in the International Prospective Register of Systematic Reviews Database under ID: CRD420222367461. No funding was provided for the completion of this systematic review.

BACKGROUND

Templating in the preoperative planning of total knee arthroplasties is a vital step in ensuring maximum operative efficiency. A method that can accurately predict the required implant size within 1 size could improve theatre turnover, decrease costs and benefit patient outcomes. The current literature on the accuracy of digital templating in total knee arthroplasties lacks a systematic review calculating the overall accuracy of the process, this study aims to address this gap.

Analyzing the Accuracy of Digital Sizing on Long-Leg Alignment X-rays by Using a 1-Inch Ball Bearing: A Cheap and Effective Method

Background and objective Sizing on digital films is important for implants and planning deformity correction. CT is the most accurate digital measurement method. We use a 1-inch ball bearing (cost: $1) to size our long-leg standing films (LLSFs) when planning deformity correction. In this study, we aimed to assess the accuracy of digital measurements calibrated by this method. Methods We conducted An IRB-approved study involving 25 patients having both an LLSF with a 1-inch ball bearing taped to the inner mid-thigh and a CT scanogram. The longest distance in the axial cut of the bilateral ankle, knee, and femoral heads of the CT images were compared to the same anatomic locations on LLSFs calibrated with the ball bearing using the online digital planning software DetroitBonesetter (DBS) and measurements from our Picture Archiving Communication Software (PACS). Five observers performed each measurement. Results The average measurement differences between the gold standard CT scan and LLSFs calibrated with DBS were as follows: 0.110 ± 0.432 mm (femoral head); 2.173 ± 0.0619 mm (knee); and 3.671 ± 0.30 mm (ankle). In PACS, they were as follows: 5.470 ± 0.381 mm (femoral head); 6.248 ± 0.712 mm (knee); and 1.806 ± 0.548 mm (ankle). The intraclass correlation coefficient for 600 measurements by five observers was 0.972. Conclusions The $1 ball-bearing sizing on DBS using LLSFs provides accuracy to <1 mm for the femoral head, 2 mm at the knee, and 3.7 mm at the ankle. It was significantly better than the PACS system for both the femoral head and knee (<0.001), while PACS was better at the ankle (<0.001).

Predictability of implant sizes during cruciate-retaining total knee arthroplasty using an image-free hand-held robotic system

The use of appropriately sized implants is critical for achieving optimal gap balance following total knee arthroplasty (TKA). Inappropriately sized implants could result in several complications. Robot-assisted TKA (RA-TKA) using CT-based pre-operative planning predicts implant sizes with high accuracy. There is scant literature describing the accuracy of image-free RA-TKA in predicting implant sizes. The purpose of this study was to assess the accuracy of an image-free robotic system in predicting implant sizes during RA-TKA. Patients who underwent cruciate-retaining RA-TKA for primary osteoarthritis, using an image-free hand-held robotic system were studied. The predicted and implanted sizes of the femoral component, tibial component and polyethylene insert, for 165 patients, were recorded. Agreement between robot-predicted and implanted component sizes was assessed in percentages, while reliability was assessed using Cohen's weighted kappa coefficient. The accuracy of the robotic system was 63% (weighted-kappa = 0.623, P < 0.001), 94% (weighted-kappa = 0.911, P < 0.001) and 99.4% (weighted-kappa = 0.995, P < 0.001), in predicting exact, ± 1 and ± 2 sizes of the femoral component, respectively. For the tibial component, an accuracy of 15.8% (weighted-kappa = 0.207, P < 0.001), 55.8% (weighted-kappa = 0.378, P < 0.001) and 76.4% (weighted-kappa = 0.568, P < 0.001) was noted, for predicting exact, ± 1 and ± 2 sizes respectively. An accuracy of 88.5%, 98.2% and 100%, was noted for predicting exact, ± 1 and ± 2 sizes of the polyethylene insert respectively. Errors in predicting accurate implant sizes could be multi-factorial. Though the accuracy of image-free RA-TKA with respect to alignment and component positioning is established, the surgeon's expertise should be relied upon while deciding appropriate implant sizes.

Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review

PURPOSE

This systematic review aimed to investigate the reliability of AI predictive models of intraoperative implant sizing in total knee arthroplasty (TKA).

METHODS

Four databases were searched from inception till July 2023 for original studies that studied the reliability of AI prediction in TKA. The primary outcome was the accuracy ± 1 size. This review was conducted per PRISMA guidelines, and the risk of bias was assessed using the MINORS criteria.

RESULTS

A total of four observational studies comprised of at least 34,547 patients were included in this review. A mean MINORS score of 11 out of 16 was assigned to the review. All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size.

CONCLUSION

This study demonstrated the potential of AI as a valuable complement for planning TKA, exhibiting a satisfactory level of reliability in predicting TKA implant sizes. This predictive accuracy is comparable to that of the manual and digital templating techniques currently documented in the literature. However, future research is imperative to assess the impact of AI on patient care and cost-effectiveness.

LEVEL OF EVIDENCE III

PROSPERO registration number: CRD42023446868.

Robotic arm-assisted total hip arthroplasty for preoperative planning and intraoperative decision-making

AIMS

This article aimed to explore the efficacy of robotic arm-assisted total hip arthroplasty (THA) in improving preoperative planning and intraoperative decision-making.

METHODS

In this single-center, prospective, randomized clinical controlled trial, 60 patients were randomly divided into two groups: conventional THA (cTHA) and robotic arm-assisted THA (rTHA). The rTHA underwent procedures using a robot-assisted surgical system, which generated three-dimensional models to determine the most appropriate prosthesis size and position. The standard process of replacement was executed in cTHA planned preoperatively via X-ray by experienced surgeons. Differences between predicted and actual prosthetic size, prosthetic position, and leg length were evaluated.

RESULTS

Sixty patients were included in the study, but one patient was not allocated due to anemia. No significant preoperative baseline data difference was found between the two groups. The actual versus predicted implantation size of both groups revealed that 27/30 (90.0%) in the rTHA group and 25/29 (86.2%) in the cTHA group experienced complete coincidence. The coincidence rate for the femoral stem was higher in the rTHA group (83.3%) than that in the cTHA group (62.7%). Between the actual and predicted rTHA, the difference in anteversion/inclination degree (< 6°) was largely dispersed, while cTHA was more evenly distributed in degree (< 9°). The differences in leg length between the surgical side and contralateral side showed a significant deviation when comparing the two groups (P = 0.003), with 0.281 (- 4.17 to 3.32) mm in rTHA and 3.79 (1.45-6.42) mm in cTHA.

CONCLUSION

Robotic arm-assisted total hip arthroplasty can be valuable for preoperative planning and intraoperative decision-making.

A Video Teaching Tool Is Effective for Training Residents in Hip Arthroplasty Templating

Work hour restrictions imposed on orthopedic surgery residents since the early 2000s have reduced educational opportunities at the workplace and encouraged alternative strategies for teaching outside the clinical setting. Preoperative templating is essential for safe and effective total hip arthroplasty (THA) and is accurate in predicting final implants. We sought to determine the effectiveness of a video tool for teaching orthopedic residents basic THA templating skills. We developed a video-based teaching tool with instructions on proper THA templating techniques. Ten cases were selected for testing, after excluding patients with severe hip deformities and poor-quality radiographs and only retaining those with concordance between templating by the senior authors and implanted components. The study subjects included three postgraduate year 1 (PGY-1), three PGY-2, and three PGY-5 residents, and three adult reconstruction fellows (PGY-6). Templating skills were assessed before and after watching the instructional video. The evaluation included the size and positioning of femoral and acetabular components, as well as the restoration of leg length. Each templating session was repeated twice. Variance was measured to evaluate consistency in measurements. A linear mixed model and F-test were used for statistical analyses. The number of years in training significantly affected performance prior to exposure to the instructional video. Post-exposure, there was a significant improvement in the accuracy of sizing and positioning of acetabular and femoral components for PGY-1, PGY-2, and PGY-5 residents. The results achieved were comparable to PGY-6 examiners, who did not gain substantial performance benefits from the instructional video. Limb length restoration was less affected by experience or exposure to the video. Component positioning and sizing, as well as leg length discrepancy (LLD), showed a significant decrease in variance after the intervention in all study groups. Video learning is reliable in teaching invaluable skills to orthopedic surgery residents without encroaching on work hours. We conceived a concise video to train orthopedic residents to perform THA templating with proper technique and demonstrated its efficiency and reproducibility.

Research Advances in the Application of AI for Preoperative Measurements in Total Knee Arthroplasty

Total knee arthroplasty (TKA) is widely used in clinical practice as an effective treatment for end-stage knee joint lesions. It can effectively correct joint deformities, relieve painful symptoms, and improve joint function. The reconstruction of lower extremity joint lines and soft tissue balance are important factors related to the durability of the implant; therefore, it is especially important to measure the joint lines and associated angles before TKA. In this article, we review the technological progress in the preoperative measurement of TKA.

Imprecise prediction of implant sizes with preoperative 2D digital templating in total knee arthroplasty

PURPOSE

To analyze the match between preoperatively determined implant size (2D templating) and intraoperatively used implant size in total knee arthroplasty (TKA). Also examined were the factors that might influence templating accuracy (gender, surgeon experience, obesity, etc.).

MATERIALS AND METHODS

The study was retrospective and conducted in a specialized ENDOCERT arthroplasty center. Digital templating was done with the MediCAD software. If the planned and implanted TKA components (both femur and tibia) were the same size, the match was classified "exact." A deviation of ± one size (at the femur or tibia or both) was classified "accurate." A deviation of ± two or more sizes (at the femur or tibia or both) was classified "inaccurate." Obesity, gender, implant type and surgeon experience were investigated for potential influence on templating accuracy. Chi-square tests and Cohen's weighted kappa test were used for statistical analysis.

RESULTS

A total of 482 cases [33.6% male, 66.4% female, age 69 ± 11, body mass index (BMI) 30.3 ± 5.8] were included. When the femur and tibia were taken together, exact size match was observed in 34% (95% CI 29.9-38.3%) of cases, accurate size match in 57.5% (95% CI 53-61.8%) and inaccurate size match in 8.5% (95% CI 6.3-11.2%). Inaccurate size match prolonged operative time (p = 0.028). Regarding the factors potentially influencing templating accuracy, only gender had a significant influence, with templating being more accurate in men (p = 0.004). BMI had no influence on accuracy (p = 0.87). No effect on accuracy was observed for implant type and surgeon experience.

CONCLUSIONS

The accuracy of 2D size templating in TKA is low, even in a specialized ENDOCERT arthroplasty center. The study findings challenge the usefulness of preoperative 2D size templating and highlight the importance of more reliable templating methods.

LEVEL OF EVIDENCE

Level III (retrospective observational study).

Machine Learning for the Orthopaedic Surgeon: Uses and Limitations

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Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedic research.

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There are several domains in which machine learning has application to orthopaedics, including radiographic diagnosis, gait analysis, implant identification, and patient outcome prediction.

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Several limitations prevent the widespread use of machine learning in the daily clinical environment. However, future work can overcome these issues and enable machine learning tools to be a useful adjunct for orthopaedic surgeons in their clinical decision-making.

Demographic Data Reliably Predicts Total Hip Arthroplasty Component Size

BACKGROUND

Preoperative radiographic templating for total hip arthroplasty (THA) has been shown to be inaccurate, although essential for streamlining operating room efficiency. Although demographic data have shown to predict total knee arthroplasty component sizes, the unique contour and design among femoral stem implants have limited a similar application for hip arthroplasty. The purpose of this study was to determine whether demographic data may predict cementless THA size independent of the stem design.

METHODS

A consecutive series of 1,653 index cementless metaphyseal-fitting THAs were reviewed between 2007 and 2019. This included 12 unique femoral component designs, 6 acetabular component designs, 60 femur size-design combinations, and 23 acetabular size-design combinations. Implanted component sizes and patient demographic data were collected, including gender, height, weight, laterality, age, race, and ethnicity. Multivariate linear regressions were formulated to predict implanted femur and acetabular component sizes from the demographic data.

RESULTS

There was a significant linear correlation between gender, implant model, age, height, and weight for femur (R² = 0.778; P < .001) and acetabular (R² = 0.491; P < .001) sizes. Calculated femur and acetabular component sizes averaged within 0.97 and 0.95 sizes of those implants, respectively. Femur and acetabular sizes were predicted within 1 size 79.1% and 78.2% and within 2 sizes 94.3% and 94.6% of the time, respectively.

CONCLUSIONS

Multivariate regression models were created based on specific demographics data to predict femur and acetabular component sizes. The model allows for simplified preoperative planning and potential cost savings implementation. A free phone application named EasyTJA was constructed for ease of implementation.

Machine learning algorithms predict within one size of the final implant ultimately used in total knee arthroplasty with good-to-excellent accuracy

PURPOSE

To develop a novel machine learning algorithm capable of predicting TKA implant sizes using a large, multicenter database.

METHODS

A consecutive series of primary TKA patients from two independent large academic and three community medical centers between 2012 and 2020 was identified. The primary outcomes were final tibial and femoral implant sizes obtained from an automated inventory system. Five machine learning algorithms were trained using six routinely collected preoperative features (age, sex, height, weight, and body mass index). Algorithms were validated on an independent set of patients and evaluated through accuracy, mean absolute error (MAE), and root mean-squared error (RMSE).

RESULTS

A total of 11,777 patients were included. The support vector machine (SVM) algorithm had the best performance for femoral component size(MAE = 0.73, RMSE = 1.06) with accuracies of 42.2%, 88.3%, and 97.6% for predicting exact size, ± one size, and ± two sizes, respectively. The elastic-net penalized linear regression (ENPLR) algorithm had the best performance for tibial component size (MAE 0.70, RMSE = 1.03) with accuracies of 43.8%, 90.0%, and 97.7% for predicting exact size, ± one size, and ± two sizes, respectively.

CONCLUSION

Machine learning algorithms demonstrated good-to-excellent accuracy for predicting within one size of the final tibial and femoral components used for TKA. Patient height and sex were the most important factors for predicting femoral and tibial component size, respectively. External validation of these algorithms is imperative prior to use in clinical settings.

LEVEL OF EVIDENCE

Case-control, III.

Can Component Size in Total Knee Arthroplasty Be Predicted Preoperatively?-An Analysis of Patient Characteristics

INTRODUCTION

 Accurately predicting component sizing in total knee arthroplasty (TKA) can ensure appropriate implants are readily available, avoiding complications from malsizing while also reducing cost by improving workflow efficiency through a reduction in instrumentation. This study investigated the utility of demographic variables to reliably predict TKA component sizes.

METHODS AND MATERIALS

 A retrospective chart review of 337 patients undergoing primary TKA was performed. Patient characteristics (age, sex, race, height, weight) were recorded along with implant and shoe size. Correlation between shoe size and TKA component size was assessed using Pearson's correlation coefficient and linear regression analysis using three models: (A) standard demographic variables, (B) shoe size, and (C) combination of both models.

RESULTS

 Shoe size demonstrated the strongest correlation with femoral anteroposterior (FAP) (p < 0.001) followed by height (p < 0.001). Conversely, height exhibited the strongest correlation with tibial mediolateral (TML) (p < 0.001) followed by shoe size (p < 0.001). Model C was able to correctly predict both the femur and tibia within one and two sizes in 83.09 and 98.14% of cases, respectively. Individually, model C predicted the FAP within one and two sizes in 83.09 and 96.14% of cases, and the TML in 98.81 and 100% of cases, respectively.

CONCLUSION

 A patient's shoe size demonstrates a strong correlation to the TKA implant size, and when combined with standard demographic variables the predictive reliability is further increased. Here, we present a predictive model for implant sizing based solely on easily attainable demographic variables, that will be useful for preoperative planning to improve surgical efficiency.

LEVEL OF EVIDENCE

 II, Diagnostic.

Prediction of Knee Prosthesis Using Patient Gender and BMI With Non-marked X-Ray by Deep Learning

Background

Total knee arthroplasty (TKA) is effective for severe osteoarthritis and other related diseases. Accurate prosthesis prediction is a crucial factor for improving clinical outcomes and patient satisfaction after TKA. Current studies mainly focus on conventional manual template measurements, which are inconvenient and inefficient.

Methods

In this article, we utilize convolutional neural networks to analyze a multimodal patient data and design a system that helps doctors choose prostheses for TKA. To alleviate the problems of insufficient data and uneven distribution of labels, research on model structure, loss function and transfer learning is carried out. Algorithm optimization based on error correct output coding (ECOC) is implemented to further boost the performance.

Results

The experimental results show the ECOC-based model reaches prediction accuracies of 88.23% and 86.27% for femoral components and tibial components, respectively.

Conclusions

The results verify that the ECOC-based model for prosthesis prediction in TKA is feasible and outperforms existing methods, which is of great significance for templating.

Increased Accuracy in Templating for Total Knee Arthroplasty Using 3D Models Generated from Radiographs

Templating prior to total hip arthroplasty is a widely adopted practice that aims to improve operative efficiency and reduce clinical outliers. Predicting implant size before total knee arthroplasty (TKA), although less common, could increase operating room efficiency by reducing necessary equipment needed for the procedure. This study compared templating accuracy in TKA using two-dimensional (2D) digital radiographs to a novel imaging technology that generates a three-dimensional (3D) model from these 2D radiographs. Two hundred and two robotic-assisted primary TKA surgical cases using Persona Knee System (Zimmer Biomet, Warsaw, IN) were retrospectively analyzed. For all cases, 3D templating was completed preoperatively using a novel radiographic image acquisition protocol. Using the same radiographs, the knee was templated using a 2D digital templating program. All surgeons were blinded to the final implant sizes, and all templating was done independently. The accuracy of predictions within ± 1 from the final implant size was determined for the femoral and tibial components. The accuracy (within 1 size) of tibial size predictions was comparable between attending surgeons and residents (87 vs. 82%, p = 0.08), but attending surgeons more accurately predicted the femoral size (77 vs. 60%, p < 0.05). The 2D to 3D imaging technology more accurately predicted both tibial and femoral sizes compared with the attending surgeons (99.5 vs. 87%, p < 0.05; 84% vs. 77%, p < 0.05). However, the imaging technology, attending surgeons, and residents were all more likely to overestimate femur size (p < 0.05). Moreover, the 3D imaging technology predicted the exact tibial component size in 93.1% of cases, which was significantly greater compared with residents (40%, p < 0.01) and attending surgeons (53%, p < 0.01). The 2D to 3D imaging technology more accurately predicted tibial and femoral component sizes compared with 2D digital templating done by surgeons. All templating predictions were more accurate for the tibial implant size than for the femoral size. The increased accuracy of implant size predictions from this 3D templating technology has the potential to improve intraoperative efficiency and minimize costs and surgical time.

Validation and performance of a machine-learning derived prediction guide for total knee arthroplasty component sizing

INTRODUCTION

Anticipation of patient-specific component sizes prior to total knee arthroplasty (TKA) is essential to avoid excessive cost associated with additional surgical trays and morbidity associated with imperfect sizing. Current methods of size prediction, including templating, are inconsistent and time-consuming. Machine learning (ML) algorithms may allow for accurate TKA component size prediction with the ability to make predictions in real-time.

METHODS

Consecutive patients receiving primary TKA between 2012 and 2020 from two large tertiary academic and six community hospitals were identified. The primary outcomes were the final femoral and tibial component sizes extracted from automated inventory systems. Five ML algorithms were trained with routinely corrected demographic variables (age, height, weight, body mass index, and sex) using 80% of the study population and internally validated on an independent set of the remaining 20% of patients. Algorithm performance was evaluated through accuracy, mean absolute error (MAE), and root mean-squared error (RMSE).

RESULTS

A total of 17,283 patients that received one of 9 TKA implants from independent manufacturers were included. The SGB model accuracy for predicting ± 4-mm of the true femoral anteroposterior diameter was 83.6% and for ± 1 size of the true femoral component size was 95.0%. The SGB model accuracy for predicting ± 4-mm of the true tibial medial/lateral diameter was 83.0% and for ± 1 size of the true tibial component size was 97.8%. Patient sex was the most influential feature in terms of informing the SGB model predictions for both femoral and tibial component sizing. A TKA implant sizing application was subsequently created.

CONCLUSION

Novel machine learning algorithms demonstrated good to excellent performance for predicting TKA component size. Patient sex appears to contribute an important role in predicting TKA size. A web-based real-time prediction application was created capable of integrating patient specific data to predict TKA size, which will require external validation prior to clinical use.

Accuracy of one-dimensional templating on linear EOS radiography allows template-directed instrumentation in total knee arthroplasty

BACKGROUND

Templating for total knee arthroplasty (TKA) is routinely performed on two-dimensional standard X-ray images and allows template-directed instrumentation. To date, there is no report on one-dimensional (1D) anteroposterior (AP) templating not requiring specific templating software. We aim to describe a novel technique and explore its reliability, accuracy and potential cost-savings.

METHODS

We investigated a consecutive series of TKAs at one institution between January and July 2019. Patients with preoperative low-dose linear AP EOS radiography images were included. Implant component sizes were retrospectively templated on the AP view with the hospitals imaging viewing software by two observers who were blinded to the definitive implant size. Planning accuracy as well as inter- and intra-observer reliability was calculated. Cost-savings were estimated based on the reduction of trays indicated by the 1D templating size estimations.

RESULTS

A total of 141 consecutive TKAs in 113 patients were included. Accuracy of 1D templating was as follows: exact match in 53% femoral and 63% tibial components, within one size in 96% femoral and 98% tibial components. Overall 58% of TKA components were planned correctly and 97% within one size. Inter- and intra-rater reliability was good (κ = 0.66) and very good (κ = 0.82), respectively. This templating process can reduce instrumentation from six to three trays per case and therefore halve sterilisation costs.

CONCLUSIONS

The new 1D templating method using EOS AP imaging predicts component sizes in TKA within one size 97% of the time and can halve the number of instrumentation trays and sterilisation costs.

Instability Is the Most Common Indication for Revision Hip Arthroplasty in the United States: National Trends From 2012 to 2018

Background

As primary total hip arthroplasty volume continues to increase, so will the number of revision total hip arthroplasty (rTHA) procedures. These complex cases represent a significant clinical and financial burden to the health-care system.

Methods

This was a retrospective review using the National Inpatient Sample. International Classification of Diseases, 9th and 10^th revision codes were used to identify patients who underwent rTHA and create cohorts based on rTHA indications from 2012 to 2018. National and regional trends for length of stay (LOS), cost, and discharge location were evaluated.

Results

A total of 292,250 rTHA procedures were identified. The annual number of rTHA procedures increased by 28.1% from 2012 to 2018 (37,325 to 47,810). The top 3 indications for rTHA were instability (20.4%), aseptic loosening (17.8%), and infection (11.1%). Over the study period, the proportion of patients discharged to skilled nursing facility decreased from 44.2% to 38% (P < .001). Hospital LOS decreased on average from 4.8 to 4.4 days (P < .001). Infections had the highest average LOS (7.3 days) followed by periprosthetic fractures (6.5 days). Hospital costs decreased over the study period, from $25,794 to $24,555 (P < .001). The proportion of rTHA cases performed at urban academic centers increased (58.0% to 75.3%, P < .001) while the proportion performed at urban nonacademic centers decreased (35.5% to 19.4%, P < .001).

Conclusion

Instability was the most common indication for rTHA between 2012 and 2018. The proportion of rTHA performed in urban academic centers has increased substantially, away from urban nonacademic centers. While cost and LOS have decreased, significant geographic variability exists.

Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty

Background

Despite reasonable accuracy with preoperative templating, the search for an optimal planning tool remains an unsolved dilemma. The purpose of the present study was to apply machine learning (ML) using preoperative demographic variables to predict mismatch between templating and final component size in primary total knee arthroplasty (TKA) cases.

Methods

This was a retrospective case-control study of primary TKA patients between September 2012 and April 2018. The primary outcome was mismatch between the templated and final implanted component sizes extracted from the operative database. The secondary outcome was mismatch categorized as undersized and oversized. Five supervised ML algorithms were trained using 6 demographic features. Prediction accuracies were obtained as a metric of performance for binary mismatch (yes/no) and multilevel (undersized/correct/oversized) classifications.

Results

A total of 1801 patients were included. For binary classification, the best-performing algorithm for predicting femoral and tibial mismatch was the stochastic gradient boosting model (area under the curve: 0.76/0.72, calibration intercepts: 0.05/0.05, calibration slopes: 0.55/0.7, and Brier scores: 0.20/0.21). For multiclass classification, the best-performing algorithms had accuracies of 83.9% and 82.9% for predicting the concordance/mismatch of the femoral and tibial implant, respectively. Model predictions of greater than 51.0% and 47.9% represented high-risk thresholds for femoral and tibial sizing mismatch, respectively.

Conclusions

ML algorithms predicted templating mismatch with good accuracy. External validation is necessary to confirm the performance and reliability of these algorithms. Predicting sizing mismatch is the first step in using ML to aid in the prediction of final TKA component sizes. Further studies to optimize parameters and predictions for the algorithms are ongoing.

Demographic data is more predictive of component size than digital radiographic templating in total knee arthroplasty

Background

Preoperative radiographic templating for total knee arthroplasty (TKA) has been shown to be inaccurate. Patient demographic data, such as gender, height, weight, age, and race, may be more predictive of implanted component size in TKA.

Materials and methods

A multivariate linear regression model was designed to predict implanted femoral and tibial component size using demographic data along a consecutive series of 201 patients undergoing index TKA. Traditional, two-dimensional, radiographic templating was compared to demographic-based regression predictions on a prospective 181 consecutive patients undergoing index TKA in their ability to accurately predict intraoperative implanted sizes. Surgeons were blinded of any predictions.

Results

Patient gender, height, weight, age, and ethnicity/race were predictive of implanted TKA component size. The regression model more accurately predicted implanted component size compared to radiographically templated sizes for both the femoral (P = 0.04) and tibial (P < 0.01) components. The regression model exactly predicted femoral and tibial component sizes in 43.7 and 43.7% of cases, was within one size 90.1 and 95.6% of the time, and was within two sizes in every case. Radiographic templating exactly predicted 35.4 and 36.5% of cases, was within one size 86.2 and 85.1% of the time, and varied up to four sizes for both the femoral and tibial components. The regression model averaged within 0.66 and 0.61 sizes, versus 0.81 and 0.81 sizes for radiographic templating for femoral and tibial components.

Conclusions

A demographic-based regression model was created based on patient-specific demographic data to predict femoral and tibial TKA component sizes. In a prospective patient series, the regression model more accurately and precisely predicted implanted component sizes compared to radiographic templating.

Level of evidence

Prospective cohort, level II.

Patient Demographics and Anthropometric Measurements Predict Tibial and Femoral Component Sizing in Total Knee Arthroplasty

Background

Accurate sizing is critical for the overall success of a total knee arthroplasty (TKA). This study's primary purpose was to investigate the ability to predict the tibial and femoral component size in a single implant system from patient demographics and anthropometric data. A secondary goal was to compare the predicted tibial and femoral component sizes from our statistical model with a previously validated electronic application used to predict the implant size.

Methods

A consecutive series of 484 patients undergoing a primary TKA at a single institution was reviewed. Data on height, weight, body mass index, sex, age, and component size were collected. A proportional odds model was developed to predict tibial and femoral component sizes. The relationship between the proportional odds model predictions was also compared with the component sizes determined by the Arthroplasty Size Predictor electronic application.

Results

Weight, height, and sex predicted the implanted component size with an accuracy of 54.0% (n = 247/484) for the tibia and 51.1% (n = 231/484) for the femur. The accuracy improved to 94.4% (n = 457/484) for the tibia and 93.4% (n = 452/484) for the femur within ±1 component size. Our data are highly correlated to the Arthroplasty Size Predictor for the predicted tibial component size (ρ = 0.91, P < .001) and femoral component size (ρ = 0.89, P < .001).

Conclusions

Our novel templating model may improve operative efficiency for a single TKA system. Our findings have a high concordance with a widely available electronic application used to predict implant sizes for a variety of TKA systems.

Planning on CT-Based 3D Virtual Models Can Accurately Predict the Component Size for Total Knee Arthroplasty

The ability to predict accurate sizing of the implant components for total knee arthroplasty surgery can have several benefits in the operating room, in terms of simplifying the workflow and reducing the number of required instrument trays. Planning on a three-dimensional (3D) virtual model can be used to predict size. The aim of this study was to quantify the accuracy of the surgeon-validated plan prediction on a computed tomography (CT)-based system. The clinical records of 336 cases (267 patients), operated using a CT-based patient-specific instrumentation, have been reviewed for the size of implanted components. Preoperative default planning (according to the preferences of the surgeon) and approved planning have been compared with the size of implanted components for both the femur and tibia. The prosthesis size, preplanned by the manufacturers, was modified by the surgeon during the validation process in 0.9% of cases for the femoral component and in 2.7% of cases for the tibial component. The prosthesis size, preplanned by the surgeon after the validation process, was used in 95.8% for the femur and 92.6% for the tibia. Concordance on the size of the surgeon-validated plan and the finally implanted size was perfect for both, the femoral (κ  =  0.951; 95% confidence interval [CI]: 0.92-0.98) and the tibial component (κ  =  0.902; 95% CI: 0.86-0.94). The most frequent change of size (51%) was an increase by one size of the planned tibial component. Planning of knee arthroplasty surgery on a 3D virtual, CT-based model is useful to surgeons to help predict the size of the implants to be used in surgery. The system we have used can accurately predict the component size for both the femur and tibia. This study reflects a study of level III evidence.

Agreement in component size between preoperative measurement, navigation and final implant in total knee replacement

Background

One of the possible causes of dissatisfaction reported by many patients after total knee replacement (TKR) is the lack of agreement between component size and bone structure. To avoid this complication and facilitate the procedure, preoperative planning with digitized templates is recommended. Surgical navigation indicates the best position and the most adequate size of arthroplasty and may therefore replace preoperative radiographic measurement. The objective of the study was to check agreement between the sizes of TKR components measured before surgery with digitized templates, the size recommended by the navigation and sizes actually implanted.

Methods

In 103 patients scheduled for TKR, preoperative full-limb radiography was performed to measure the mechanical and anatomical axes of the limb, femur and tibia. The most adequate size of the femoral and tibial components was planned by superimposing digitized templates. The size recommended in navigation and the size of the finally implanted components were also recorded.

Results

A high level of agreement was found between the sizes of femoral and tibial components measured by X-rays and in navigation (0.750 and 0.772, respectively) (intraclass correlation and Cronbach's alpha). Agreement between the sizes recommended by X-rays and navigation and those finally implanted was 0.886 for the femur and 0.891 for the tibia. Agreement levels were not different in cases with prior deformities of limb axis.

Conclusions

The high level of agreement found in component sizes between radiographic measurement with digitized templates and navigation suggests that preoperative X-ray measurement is not needed when navigation is used for placement of implants during TKR.

The translational potential of this article

Computer-assisted surgery may avoid preoperative measurement with templates in TKR.

Prospective Validation of a Demographically Based Primary Total Knee Arthroplasty Size Calculator

BACKGROUND

Preoperative planning for total knee arthroplasty (TKA) is essential for streamlining operating room efficiency and reducing costs. Digital templating and patient-specific instrumentation have shown some value in TKA but require additional costs and resources. The purpose of this study was to validate a previously published algorithm that uses only demographic variables to accurately predict TKA tibial and femoral component sizes.

METHODS

Four hundred seventy-four consecutive patients undergoing elective primary TKA were prospectively enrolled. Four surgeons were included, three of which were unaffiliated with the retrospective cohort study. Patient sex, height, and weight were entered into our published Arthroplasty Size Prediction mobile application. Accuracy of the algorithm was compared with the actual sizes of the implanted femoral and tibial components from 5 different implant systems. Multivariate regression analysis was used to identify independent risk factors for inaccurate outliers for our model.

RESULTS

When assessing accuracy to within ±1 size, the accuracies of tibial and femoral components were 87% (412/474) and 76% (360/474). When assessing accuracy to within ±2 sizes of predicted, the tibial accuracy was 97% (461/474), and the femoral accuracy was 95% (450/474). Risk factors for the actual components falling outside of 2 predicted sizes include weight less than 70 kg (odds ratio = 2.47, 95% confidence interval [1.21-5.06], P = .01) and use of an implant system with <2.5 mm incremental changes between femoral sizes (odds ratio = 5.50, 95% confidence interval [3.33-9.11], P < .001).

CONCLUSIONS

This prospective series of patients validates a simple algorithm to predict component sizing for TKA with high accuracy based on demographic variables alone. Surgeons can use this algorithm to simplify the preoperative planning process by reducing unnecessary trays, trials, and implant storage, particularly in the community or outpatient setting where resources are limited. Further assessment of components with less than 2.5-mm differences between femoral sizes is required in the future to make this algorithm more applicable worldwide.

Templating Hip Arthroplasty

BACKGROUND:

Different methods have been developed and employed with variable degrees of success in pre-operative templating for total hip arthroplasty. Preoperative templating, especially digital templating, has been claimed to have increased the effectiveness of total hip arthroplasty by improving the precision of prediction of prosthetic implant size.

AIMS:

The overall aim of this systematic review is to identify whether the use of pre-operative templating in total hip arthroplasty procedures has resulted in increased accuracy, reliability and precision of the procedure. Various methods of templating, like traditional acetate overlay and digital method of templating that includes a single radiographic marker and double radiographic marker methods, have been compared to establish the most reliable method of templating.

METHODS:

We searched the PubMed, Google Scholar Cochrane Central Register of Controlled Trials (CENTRAL), and MEDLINE (1966 to present), EMBASE (1980 to present), CINAHL (1982 to present), Psych INFO (1967 to present) and Clinical Trials Gov.

CONCLUSION:

The results of this systemic review suggest that preoperative templating is resulting in an enormous increase in the accuracy of total hip arthroplasty and among various methods, King Mark is the most reliable method.

Favourable alignment outcomes with MRI-based patient-specific instruments in total knee arthroplasty

PURPOSE

Patient-specific instruments (PSIs) are already in relatively common use, and their post-operative radiographic results are equal to those for total knee arthroplasty (TKA) with conventional instrumentation. PSI use requires a preoperative MRI scan, CT scan, or a combination of MRI and a long-leg standing radiograph. However, there is no consensus as to which of these modalities, MRI or CT, is the preferred imaging modality when performing TKA with PSIs.

METHODS

This systematic literature review and meta-analysis studied the differences in alignment outliers between CT- and MRI-based PSI for TKA. A search of the Cochrane Database of Systematic Reviews, MEDLINE/PubMed and Embase was conducted, without restriction on date of publication. Only level I evidence studies written in English that included TKA with the use of MRI- and CT-based PSI were selected. A meta-analysis was then performed of the rate of outliers in the biomechanical axis and individual femoral and tibial component alignment. Where considerable heterogeneity among studies was present or the data did not provide sufficient information for performing the meta-analysis, a qualitative synthesis was undertaken.

RESULTS

Twelve randomized controlled trials, studying 841 knees, were eligible for data extraction and meta-analysis. MRI-based PSI resulted in a significantly lower proportion of coronal plane outliers with regard to the lateral femoral component (OR 0.52, 95% CI 0.30-0.89, P = 0.02), without significant heterogeneity (n.s.). There were no significant differences regarding the biomechanical axis or frontal femoral and individual tibial component alignment.

CONCLUSION

This systematic review and meta-analysis demonstrate that alignment with MRI-based PSI is at least as good as, if not better than, that with CT-based PSI. To prevent for malalignment, MRI should be the imaging modality of choice when performing TKA surgery with PSI.

LEVEL OF EVIDENCE

I.

Computed tomography based 3D printed patient specific blocks for total knee replacement

OBJECTIVES

3D printing is an emerging technology and its use in orthopaedics is being explored. We discuss the role of computed tomography based 3D printed patient specific jigs in total knee replacement. We also discuss the various advantages of 3D printed patient specific jigs and the future scope of their use in total knee replacement.

METHODS

A search of English literature was done and articles discussing the role of CT scan based 3D printed patient specific jigs in total knee replacement were included in the study.

RESULTS

The role of 3D printed jigs in total knee replacement have been found in the prediction of femoral valgus angle, component sizing and in retained hardware. They have shown promise with studies suggesting they might improve the overall mechanical alignment of the knee. There are studies which have also studied the combined role of patient specific instruments with navigation.

CONCLUSION

3D printed jigs hold promise in total knee replacement. Their use in total knee replacement in the presence of retained hardware is useful for the surgeon. They have also showed promise in improving prediction of component sizing and improving mechanical alignment of the knee. Further studies with longer follow up and larger sample size will help in establishing their role in total knee replacement.

Revision of partial knee to total knee arthroplasty with use of patient-specific instruments results in acceptable femoral rotation

PURPOSE

Patient-specific instruments (PSI) were initially developed for the alignment of both total knee- (TKA) and partial knee arthroplasty (PKA). We hypothesize that CT-based PSI for PKA-to-TKA revision surgery can restore biomechanical limb alignment and prosthetic component positioning in vivo as calculated pre-operatively, resulting in a limited percentages of outliers.

METHODS

An imaging analysis was performed using CT-based 3D measurement methods based on a pre- and post-revision CT scan. Imaging data were gathered on 10 patients who were operated for PKA-to-TKA revision with the use of PSI based on CT imaging. The planned femur and tibia component position in vivo were compared with the pre-revision planned component position. Outliers were defined as deviations >3.0° from pre-revision planned position for the individual implant components. Adjustments (e.g. resection level and implant size) during surgery were recorded.

RESULTS

The HKA axis was restored accurately in all patients with a mean post-operative HKA axis of 178.1° (1.4°). Five femoral (2 varus, 2 internal rotation and 1 extension) and 14 tibial guides (2 varus, 6 anterior slope, 3 internal rotation and 3 external rotation) on a total of 60 outcome measures were identified as outliers. During surgery, an intraoperative tibial resection of 2 mm extra was performed in three patients. In 80 and 70% for, respectively, the femur and tibia, the surgeon-planned size was implanted during surgery. All patient-specific guides fitted well in all patients. No intraoperative or post-operative complications related to surgery were registered.

CONCLUSIONS

This study introduced a unique new concept regarding PSI, PKA-to-TKA revision surgery. Based on the results, we were unable to fully confirm our hypothesis. PSI as a "new" tool for PKA-to-TKA revision surgery appears to be an accurate tool for the alignment of the TKA femur component. The tibial guide seems more susceptible to errors, resulting in a substantial percentage of outliers.

LEVEL OF EVIDENCE

Prospective cohort study, Level II.

Using Patient Demographics and Statistical Modeling to Predict Knee Tibia Component Sizing in Total Knee Arthroplasty

BACKGROUND

Preoperative planning is important to achieve successful implantation in primary total knee arthroplasty (TKA). However, traditional TKA templating techniques are not accurate enough to predict the component size to a very close range.

METHODS

With the goal of developing a general predictive statistical model using patient demographic information, ordinal logistic regression was applied to build a proportional odds model to predict the tibia component size. The study retrospectively collected the data of 1992 primary Persona Knee System TKA procedures. Of them, 199 procedures were randomly selected as testing data and the rest of the data were randomly partitioned between model training data and model evaluation data with a ratio of 7:3. Different models were trained and evaluated on the training and validation data sets after data exploration.

RESULTS

The final model had patient gender, age, weight, and height as independent variables and predicted the tibia size within 1 size difference 96% of the time on the validation data, 94% of the time on the testing data, and 92% on a prospective cadaver data set.

CONCLUSION

The study results indicated the statistical model built by ordinal logistic regression can increase the accuracy of tibia sizing information for Persona Knee preoperative templating. This research shows statistical modeling may be used with radiographs to dramatically enhance the templating accuracy, efficiency, and quality. In general, this methodology can be applied to other TKA products when the data are applicable.

Pre-operative templating for knee arthroplasty shows low accuracy with standard X-rays

OBJECTIVES

The purpose of this study was to evaluate the accuracy and reliability of pre-operative templating in predicting the size of femoral and tibial components and the effect of coronal deformity on templating accuracy.

METHODS

This was a retrospective study of 39 pre-operative templates prepared by three different surgeons with different levels of training. The accuracy and reliability measures were evaluated by alpha and kappa coefficients of agreement. The analysis of the effect of coronal deformity on the accuracy of the template was measured by the Spearman's correlation test.

RESULTS

Templating was accurate for the femoral component in 28.21% of anterposterior (AP) radiographs and 35.90% of lateral radiographs. Kappa coefficients were respectively 0.111 (95% confidence interval [95%CI]: -0.19 to 0.241) and 0.200 (95%CI: -0.010 to 0.401), indicating poor agreement. Templating accuracy for the tibial component were, respectively, 37.61% and 47.01% for AP and lateral views. Kappa coefficients were 0.186 (95%CI: -0.070 to 0.379) for the AP view and 0.315 (95%CI: -0.199 to 0.431) for the lateral view, showing poor and slight agreement respectively. Considering a margin of error of ±1 sizes, the agreement level improved for all components, particularly for tibia, where agreement levels become very good. The inter-observer agreement was fair for all components, except for the lateral view of the femoral component, whose agreement was good. The Spearman correlation test showed no correlation between accuracy of templating and coronal deformity.

CONCLUSION

Pre-operative templating is an unreliable and inaccurate tool. There is no relation between coronal deformity and accuracy of templating.

Fallacies of CT based component size prediction in total knee arthroplasty - Are patient specific instruments the answer?

BACKGROUND

The purpose of the study was to assess the accuracy of tibial and femoral component size prediction using computerised tomography (CT) based patient specific instruments in total knee arthroplasty.

METHODS

Eighty-eight knees in 58 patients underwent total knee arthroplasty (TKA) using CT based patient specific instruments between March 2015 to April 2016. All patients were assessed for the pre operative femoral and tibial component sizes predicted by the CT-based pre-operative plan. These sizes were compared with the actually implanted sizes during surgery, and the results were assessed. The data were evaluated using Wilcoxon signed rank tests, and p value set at <0.05 for significance.

RESULTS

Approximately 72% predicted tibia sizes matched the final implanted sizes whereas 66% femoral implants matched their pre-operative predicted sizes. The difference in the tibial implant size was not statistically significant (p-value > 0.05). However, the difference in the femoral size was statistically significant (p-value 0.009). The downsizing of the tibial component was needed in 14.8% knees whereas upsizing was required in 13.6% of the knees. At the femoral side, 22.7% components required downsizing at the time of implantation as compared to 11.4% components wherein a bigger component was used.

CONCLUSIONS

We conclude that size prediction using CT-based technology for patient specific instrumentation is not fool proof. The size prediction accuracy for femoral and tibial components at 66% and 72% are low and cannot be relied upon at present. The patient specific technology using CT scan based jigs holds promise for the future, needs refining and fine tuning.

Patient-specific instruments for total knee arthroplasty can accurately predict the component size as used peroperative

PURPOSE

Patients-specific instruments (PSI) for implantation of total knee arthroplasty (TKA) can be used to predict the implant size for both the femur and the tibia component. This study aims to determine the impact of approval of the PSI planning for TKA on the frequency of, and reason for intraoperative changes of implant sizes.

METHODS

The clinical records of 293 patients operated with MRI- (90.4 %) and CT-based (9.6 %) PSI were reviewed for actual used implant size. Preoperative default planning from the technician and approved planning by the operating surgeon were compared with the intraoperative implanted component size for both the femur and tibia. Intraoperative reason for not following the default sizes was outdated. Furthermore, MRI- and CT-based PSI were compared for these outcomes.

RESULTS

In 93.9 and 91.1 % for, respectively, the femur and tibia (n.s.), the surgeon planned size was implanted during surgery. The predicted size of the femur (p < 0.00) and the tibia (p < 0.00) component planned by a technician differed from the implanted component sizes in 62 (21.2 %) and 51 (17.4 %) patients, respectively. In 17 cases, the femoral component size was adapted intraoperative based on the expert opinion of the operating surgeon. In 26 cases, the tibia component was changed during the surgery because of a mediolateral overhang, sclerotic bone, medial or lateral release, limited extension and/or fixed varus deformity. The results between the MRI- and CT-based PSI did not differ (n.s.).

CONCLUSIONS

PSI is a tool to help the surgeon to achieve the best possible results during TKA. The planning made by a technician should always be validated and approved by the operating surgeon who has the ultimate responsibility regarding the operation. With PSI, the operating surgeon is able to minimize intraoperative implant size errors in advance to improve operating room efficiency with possible lowering hospital costs per procedure.

LEVELS OF EVIDENCE

III.

Reproducibility of three dimensional digital preoperative planning for the osteosynthesis of distal radius fractures

A three-dimensional (3D) digital preoperative planning system for the osteosynthesis of distal radius fractures was developed for clinical practice. To assess the usefulness of the 3D planning for osteosynthesis, we evaluated the reproducibility of the reduction shapes and selected implants in the patients with distal radius fractures. Twenty wrists of 20 distal radius fracture patients who underwent osteosynthesis using volar locking plates were evaluated. The 3D preoperative planning was performed prior to each surgery. Four surgeons conducted the surgeries. The surgeons performed the reduction and the placement of the plate while comparing images between the preoperative plan and fluoroscopy. Preoperative planning and postoperative reductions were compared by measuring volar tilt and radial inclination of the 3D images. Intra-class correlation coefficients (ICCs) of the volar tilt and radial inclination were evaluated. For the implant choices, the ICCs for the screw lengths between the preoperative plan and the actual choices were evaluated. The ICCs were 0.644 (p < 0.01) and 0.625 (p < 0.01) for the volar tilt and radial inclination in the 3D measurements, respectively. The planned size of plate was used in all of the patients. The ICC for the screw length between preoperative planning and actual choice was 0.860 (p < 0.01). Good reproducibility for the reduction shape and excellent reproducibility for the implant choices were achieved using 3D preoperative planning for distal radius fracture. Three-dimensional digital planning was useful to visualize the reduction process and choose a proper implant for distal radius fractures. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2646-2651, 2017.

Can Demographic Variables Accurately Predict Component Sizing in Primary Total Knee Arthroplasty?

BACKGROUND

As health care reform drives providers to reduce costs and improve efficiencies without compromising patient care, preoperative planning has become imperative. The purpose of this study is to determine whether height, weight, and gender can accurately predict total knee arthroplasty (TKA) sizing.

METHODS

A consecutive series of 3491 primary TKAs performed by 2 surgeons was reviewed. Height, weight, gender, implant, preoperative templating sizes, and final implant sizes were collected. Implant-specific dimensions were collected from vendors. Using height, weight, and gender, a multivariate linear regression was performed with and without the inclusion of preoperative templating. Accuracy of the model was reported for commonly used implants.

RESULTS

There was a significant linear correlation between height, weight, and gender for femoral (R² = 0.504; P < .001) and tibial sizes (R² = 0.610; P < .001). Adding preoperative templating to the regression analysis increased the overall model fit for both the femoral (R² = 0.756; P < .001) and tibial sizes (R² = 0.780; P < .001). Femoral and tibial sizes were accurately predicted within 1 size of the final implant 71%-92% and 81%-97% using demographics alone or 85%-99% and 90%-99% using both templating and demographics, respectively.

CONCLUSION

This novel TKA templating model allows final implants to be predicted to within 1 size. The model allows for simplified preoperative planning and potential implementation into a cost-savings program that limits inventory and trays required for each case.

Effect of Body Mass Index on Digital Templating for Total Hip Arthroplasty

BACKGROUND

Digital templating is becoming more prevalent in orthopedics. Recent investigations report high accuracy using digital templating in total hip arthroplasty (THA); however, the effect of body mass index (BMI) on templating accuracy is not well described.

METHODS

Digital radiographs of 603 consecutive patients (645 hips) undergoing primary THA by a single surgeon were digitally templated using OrthoView (Jacksonville, FL). A 25-mm metallic sphere was used as a calibration marker. Preoperative digital hip templates were compared with the final implant size. Hips were stratified into groups based on BMI: BMI <30 (315), BMI 30-35 (132), BMI 35-40 (97), and BMI >40 (101).

RESULTS

Accuracy between templating and final size did not vary by BMI for acetabular or femoral components. Digital templating was within 2 sizes of the final acetabular and femoral implants in 99.1% and 97.1% of cases, respectively.

CONCLUSION

Digital templating is an effective means of predicting the final size of THA components. BMI does not appear to play a major role in altering THA digital templating accuracy.

Factors associated with symptomatic pseudotumors following metal-on-metal total hip arthroplasty

Background

Pseudotumors associated with metal-on-metal hips can be symptomatic or asymptomatic. The purpose of this study was to identify the characteristics of pseudotumors associated with pain.

Methods

A total of 239 large-diameter, metal-on-metal total hip arthroplasties (THAs) were performed in 222 patients. Screening for pseudotumors was performed using magnetic resonance imaging (MRI) in all patients who underwent metal-on-metal THA, and 57 patients with 62 affected hips showed pseudotumors. There were 45 women with 49 hips and 12 men with 13 hips affected, with a mean age of 64 years and a mean body mass index (BMI) of 23.9 kg/m². Sixteen hips had symptomatic pseudotumors with pain, and 46 hips were asymptomatic. Pseudotumor size was determined. The anatomical position of pseudotumors was divided into anterior position and posterolateral position. Types of pseudotumors were divided into two types: cystic type; and mixed solid cystic and solid type without a cystic component. The follow-up study of pseudotumors was determined using MRI in 33 patients. The serum cobalt and chromium ion levels were measured in 38 patients after unilateral THA. Univariate and multivariate analyses were performed comparing symptomatic and asymptomatic patients to identify the characteristics of symptomatic pseudotumors.

Results

The mean BMI was 25.4 kg/m² in symptomatic patients and 23.4 kg/m² in asymptomatic patients; a higher BMI was associated with symptoms (P = 0.036). Symptomatic pseudotumors were significantly larger (three-fold) than asymptomatic pseudotumors (1812 mm² vs 642 mm², P = 0.003). Pseudotumors located in the anterior position were associated with symptoms (P = 0.032), and mixed solid cystic and solid type pseudotumors were associated with symptoms (P = 0.007). A multivariate analysis showed significant differences only in size (R² = 0.298, P = 0.031). No asymptomatic patients with pseudotumors became symptomatic during the follow-up period of MRI evaluation.

Conclusion

Larger size was a significant factor for pain on multivariate analysis.

Pre-operative digital templating in cemented hip hemiarthroplasty for neck of femur fractures

Pre-operative digital templating allows the surgeon to foresee any anatomical anomalies which may lead to intra-operative problems, and anticipate appropriate instruments and implants required during surgery. Although its role is well-established in successful elective total hip arthroplasty, little work has been done on its use in hip hemiarthroplasty in neck of femur fractures. We describe our initial experience of digital templating in 40 consecutive patients who have undergone cemented hip hemiarthroplasty, assessing templating accuracy between templated implant sizes to actual implant sizes. 81% of implanted heads were templated to within two head sizes, and 89% of implanted stems were templated to within two sizes. Although there was a moderately strong correlation of 0.52 between templated and actual head sizes, this correlation was not demonstrated in femoral stem sizes. Mean leg length discrepancy was -2.5mm (S.D. 8.5), and the mean difference in femoral offset between the operated and non-operated hip was -1mm (S.D. 4.4). Digital templating is a useful adjunct to the surgeon in pre-operative planning of hip hemiarthroplasty in the restoration of leg length and femoral offset. However, its accuracy is inferior to that of elective total hip arthroplasty.

2D versus 3D templating in total knee arthroplasty

BACKGROUND

Preoperative digital templating in total knee arthroplasty (TKA) helps to determine the need of non-standard implants, prophesies the bony resections and helps to anticipate the intraoperative plan. Templating within the process of patient specific instrumentation (PSI) is fairly new and 2D planning has not been compared to PSI templating.

METHODS

94 patients underwent unilateral primary TKA with magnetic resonance imaging (MRI) based preoperative templating and PSI cutting blocks. Parallel to this, three observers templated all cases using digital planning on standard preoperative x-rays. The examiners templated all cases independently and were blinded to the component sizes used intraoperatively.

RESULTS

Three-dimensional (3D) templating was accurate in predicting the correct implant size in 100% of the cases. The femoral and tibial two-dimensional (2D) digital templating varied from 43.6% to 59.5% and 52.1% to 68% of the cases. When allowing ±1 difference, femoral 2D digital templating varied from 93.6% to 97.8% of the cases and ranged from 94.6% to 98.9% on the tibial side. All observers show "very good" correlation. The coefficient indicates a very good agreement in between the three observers.

CONCLUSION

3D templating has very high accuracy for the actual implant size prediction. Compared to this, 2D digital templating is an accurate method to approximately (±1 size) determine the size of TKA components. However, we judge this technique accurate enough, that 2D templating allows launching Template-directed instrumentation (TDI), while the examiner does not need a high level of clinical experience.

CLINICAL RELEVANCE

Within the process of digital planning, the surgeonmight focus evenmore on the upcoming operation.

Template-Directed Instrumentation Reduces Cost and Improves Efficiency for Total Knee Arthroplasty: An Economic Decision Analysis and Pilot Study

Template-directed instrumentation (TDI) for total knee arthroplasty (TKA) may streamline operating room (OR) workflow and reduce costs by preselecting implants and minimizing instrument tray burden. A decision model simulated the economics of TDI. Sensitivity analyses determined thresholds for model variables to ensure TDI success. A clinical pilot was reviewed. The accuracy of preoperative templates was validated, and 20 consecutive primary TKAs were performed using TDI. The model determined that preoperative component size estimation should be accurate to ±1 implant size for 50% of TKAs to implement TDI. The pilot showed that preoperative template accuracy exceeded 97%. There were statistically significant improvements in OR turnover time and in-room time for TDI compared to an historical cohort of TKAs. TDI reduces costs and improves OR efficiency.

[Posterior lumbar interbody fusion implants. Software assisted planning--preliminary results]

BACKGROUND

Sagittal imbalance, adjacent segment degeneration, and loss of correction due to cage sintering are the main reasons for revision surgery after lumbar fusion. Based on the experience from hip and knee replacement surgery, preoperative software-assisted planning combined with the corresponding cages is helpful to achieve better long-term results.

OBJECTIVES

Evaluation of the procedure regarding intraoperative application of preoperative planning and examination to what extent the planning was correct.

MATERIALS AND METHODS

In all, 30 patients were included in the period from September 2012 to May 2013 in an observational study, planned preoperatively with the planning software, and treated with the corresponding PLIF cages. The radiological evaluation was performed by thin-layer CT after 3 months.

RESULTS

A total of 24 (80%) patients were followed up after 3 months. In these 24 patients, the preoperative planning actually was correct in 17 cases with the intraoperatively implanted cage, which corresponds to a match of about 71%. The fusion rate for these 24 patients who underwent full examinations was 91.7%.

CONCLUSION

The results of this observational study to evaluate the planning of intervertebral cages show positive experience with this novel therapeutic concept. Despite the limited number of participants, good results were observed for the intraoperative implementation of the planned cages and an adequate fusion rate was obtained. Irrespective of this, a software-based surgical planning must be questioned critically any time. Ultimately, it is the surgeon's responsibility to modify the planned procedure intraoperatively if necessary. Currently, the influence of this planning regarding the long-term course and the important question of adjacent segment instability remains unanswered.

Reliability and accuracy of digital templating for the humeral component of total shoulder arthroplasty

BACKGROUND

This experimental study evaluated the interobserver reliability and accuracy of pre-operative digital templating for humeral head size, stem size and neck angle for total shoulder arthroplasty.

METHODS

Twenty-five patients underwent a total shoulder arthroplasty with a single prosthesis. Four independent, blinded surgeons (two experienced shoulder surgeons and two PGY-6 fellows) used pre-operative radiographs and templating software to generate templates of the humeral head, stem and neck for each patient. Interobserver reliability was calculated using weighted kappa (κ) analysis. Accuracy was assessed by comparing templates to actual implant sizes.

RESULTS

Interobserver reliability was fair to substantial (κ = 0.26 to 0.71) for head size, fair to substantial (κ = 0.39 to 0.72) for stem size and slight to fair (κ = 0.16 to 0.34) for neck angle. Templated head size, stem size and neck angle had accuracies of 53%, 77% and 68% within one size variation, respectively. Experience did not affect accuracy (p = 0.11 to 0.48).

CONCLUSIONS

Digital templating is not a useful guide for pre-operative surgical planning and should not be used to select a prosthesis.

Longitudinal magnetic resonance imaging of pseudotumors following metal-on-metal total hip arthroplasty

The purpose of the study was to determine the natural history of pseudotumors following metal-on-metal total hip arthroplasty (THA) using magnetic resonance imaging (MRI). Initial MRI was conducted at a mean of 36months postoperatively. Follow-up MRI was performed at a mean of 20months after the detection of 24 asymptomatic pseudotumors. Pseudotumor size was determined on MRI. The mean pseudotumor size changed from 729mm(2) to 877mm(2). Pseudotumors increased in size in eight and decreased in six. Ten hips showed no changes. The bigger the pseudotumor size, the more likely the size would increase. In conclusion, pseudotumors frequently change in size. A single MRI study in the clinical decision-making process should be avoided and a longitudinal study should be performed.

The accuracy and reliability of pre-operative templating in revision total knee arthroplasty. A comparison of analogue and digital methods

AIMS

To determine whether the size of the prostheses used in revision knee arthroplasty may be accurately and reproducibly predicted using analogue or digital pre-operative templating techniques.

METHODS

Pre-operative radiographs were templated using analogue radiographs and acetate templates, digital radiographs and acetate templates and digital radiographs and digital templating software.

RESULTS

Overall accuracy of predicting the size of implant used at surgery was 44%. There was no significant difference in the accuracy of the various templating techniques (p = 0.098).

CONCLUSIONS

Templating in revision knee arthroplasty is neither of suitable accuracy nor reliability enough to safely recommend its use for implant size prediction.

Assessment of accuracy of marker ball placement in pre-operative templating for total hip arthroplasty

We report the accuracy of positioning of the calibration ball in the process of pre-operative templating for total hip arthroplasty (THA). The ball should be placed in the coronal plane of the hip to provide suitable accuracy. We reviewed 112 post-operative THA radiographs where a calibration ball had been placed. We templated the femoral head size of the implant after calibrating the templating system from the ball. The calibrated femoral head diameter was compared to the known prosthetic head size. A percentage error was calculated. Overall, incorrect placement of the calibration ball resulted in a mean percentage error in templating of 6.8% (median 5.7%; range 0-26%). Such error carries implications with the templating process and may result in incorrect component sizes, leg lengths and offset.

The influence of hip rotation on femoral offset in plain radiographs

BACKGROUND AND PURPOSE

Adequate restoration of femoral offset (FO) is critical for successful outcome after hip arthroplasty or fixation of hip fracture. Previous studies have identified that hip rotation influences the projected femoral offset (FOP) on plain anteroposterior (AP) radiographs, but the precise effect of rotation is unknown.

PATIENTS AND METHODS

We developed a novel method of assessing rotation-corrected femoral offset (FORC), tested its clinical application in 222 AP hip radiographs following proximal femoral nailing, and validated it in 25 cases with corresponding computed tomography (CT) scans.

RESULTS

The mean FORC was 57 (29-93) mm, which differed significantly (p < 0.001) from the mean FOP 49 (22-65) mm and from the mean femoral offset determined by the standard method: 49 (23-66) mm. FORC correlated closely with femoral offset assessed by CT (FOCT); the Spearman correlation coefficient was 0.94 (95% CI: 0.88-0.97). The intraclass correlation coefficient for the assessment of FORC by AP hip radiographs correlating the repeated measurements of 1 observer and of 2 independent blinded observers was 1.0 and 1.0, respectively.

INTERPRETATION

Hip rotation affects the FOP on plain AP radiographs of the hip in a predictable way and should be adequately accounted for.

Research status and application prospects of digital technology in orthopaedics

In the last 10 years, basic and clinical research in orthopaedics has developed rapidly. Understanding of orthopaedic disorders involves not only routine diagnosis, but also the pursuit of highly efficient and accurate three-dimensional imaging of the intra- and extra-medullary distribution, form and structure of orthopaedic disorders, thus allowing scientific evaluation of the indications for surgery, drawing up of the best surgical plan, minimization of operative trauma and the earliest possible restoration of limb function. Meanwhile, the most important type of basic research, which was previously biomechanical research, has gradually become computational biomechanics based on in vitro cadaver experiments. This review aims to summarize the research status and application prospects of digital technology in orthopaedics, including virtual reality technology, reverse engineering and rapid prototyping techniques, computational biomechanics, computer navigation technology and management of digitization of medical records.

Comparing the accuracy of radiographic preoperative digital templating for a second- versus a first-generation THA stem

This study assessed the accuracy of preoperative digital templating for a second-generation cementless stem compared with its first-generation design. A prospective cohort of 100 consecutive patients who had undergone a primary total hip arthroplasty using a new second-generation cementless stem was compared with the prior 100 hips that had received the first-generation stem. The authors believe that the second-generation stem may allow equal or more accurate digital templating compared with its predicate design.