Functional implant positioning in total hip arthroplasty and the role of robotic-arm assistance

Is it feasible to develop a supervised learning algorithm incorporating spinopelvic mobility to predict impingement in patients undergoing total hip arthroplasty?

Aims

Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement.

Methods

This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy.

Results

We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM's prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%).

Conclusion

This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential.

Two-Dimensional Versus Three-Dimensional Preoperative Planning in Total Hip Arthroplasty

BACKGROUND

Preoperative planning in total hip arthroplasty (THA) involves utilizing radiographs or advanced imaging modalities, including computerized tomography scans, for precise prediction of implant sizing and positioning. This study aimed to compare 3-dimensional (3D) versus 2-dimensional (2D) preoperative planning in primary THA with respect to key surgical metrics, including restoration of the horizontal and vertical center of rotation (COR), combined offset, and leg length.

METHODS

This study included 60 patients undergoing primary THA for symptomatic hip osteoarthritis (OA), randomly allocated to either robotic arm-assisted or conventional THA. Digital 2D templating and 3D planning using the robotic software were performed for all patients. All measurements to evaluate the accuracy of templating methods were conducted on the preoperative computerized tomography scanogram, using the contralateral hip as a reference. Sensitivity analyses explored differences between 2D and 3D planning in patients who had superolateral or medial OA patterns.

RESULTS

Compared to 2D templating, 3D templating was associated with less medialization of the horizontal COR (-1.2 versus -0.2 mm, P = .002) and more accurate restoration of the vertical COR (1.63 versus 0.3 mm, P < .001) with respect to the contralateral side. Furthermore, 3D templating was superior for planned restoration of leg length (+0.23 versus -0.74 mm, P = .019). Sensitivity analyses demonstrated that in patients who had medial OA, 3D planning resulted in less medialization of horizontal COR and less offset reduction. Conversely, in patients who had superolateral OA, there was less lateralization of horizontal COR and less offset increase using 3D planning. Additionally, 3D planning showed superior reproducibility for stem, acetabular cup sizes, and neck angle, while 2D planning often led to smaller stem and cup sizes.

CONCLUSIONS

Our findings indicated higher accuracy in the planned restoration of native joint mechanics using 3D planning. Additionally, this study highlights distinct variances between the 2 planning methods across different OA pattern subtypes, offering valuable insights for clinicians employing 2D planning.

Decreased Instability in High-Risk (Hip-Spine 2B) Patients After Modifications of Surgical Planning and Technique

BACKGROUND

Patients undergoing primary total hip arthroplasty (THA) who have spinal deformity and a stiff spine are the highest-risk group for instability. Despite the increasing use of dual-mobility cups and large femoral heads, dislocation remains a major complication after THA. Preoperative planning becomes a critical aspect of ensuring precise component positioning within a safe zone. The purpose of this study was to investigate dislocation rates over a 9-year period.

METHODS

A retrospective review of 4,731 THAs performed by 3 orthopaedic surgeons between January 2014 and March 2023 was performed. Spinopelvic measurements were conducted to determine the hip-spine classification group for each patient. Only patients classified as 2B (pelvic incidence-lumbar lordosis > 10° and Δsacral slope < 10°) were eligible. Both absolute and relative dislocation frequencies were then analyzed using time-series analysis techniques and Fisher's exact tests.

RESULTS

A total of 281 hip-spine 2B patients undergoing primary THA were eligible for analysis (57% women; mean age, range: 66 years, 23 to 87; mean body mass index, range: 28, 16 to 45). The overall dislocation rate was 4.3%. Use of femoral head sizes ≥ 40 mm increased from 4% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001), while the use of dual-mobility cups decreased from 100% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001). Acetabular component planning was changed from the supine plane to the standing plane in February 2020. Those changes in surgical practice were notably correlated with a significant decrease in dislocation rates from 6.8% in 2014 to 2019 to 1.5% in 2020 to 2023 (P = .03).

CONCLUSIONS

Our study demonstrates that the introduction of advanced preoperative THA planning to the standing plane, coupled with precise intraoperative technology for implant placement, can significantly reduce the risk of instability in high-risk THA patients. Notably, we observed a significant decrease in dislocation rates, which aligned with the shift in surgical practice.

LEVEL OF EVIDENCE

IV.

A prospective randomized controlled trial comparing CT-based planning with conventional total hip arthroplasty versus robotic arm-assisted total hip arthroplasty

Aims

Achieving accurate implant positioning and restoring native hip biomechanics are key surgeon-controlled technical objectives in total hip arthroplasty (THA). The primary objective of this study was to compare the reproducibility of the planned preoperative centre of hip rotation (COR) in patients undergoing robotic arm-assisted THA versus conventional THA.

Methods

This prospective randomized controlled trial (RCT) included 60 patients with symptomatic hip osteoarthritis undergoing conventional THA (CO THA) versus robotic arm-assisted THA (RO THA). Patients in both arms underwent pre- and postoperative CT scans, and a patient-specific plan was created using the robotic software. The COR, combined offset, acetabular orientation, and leg length discrepancy were measured on the pre- and postoperative CT scanogram at six weeks following surgery.

Results

There were no significant differences for any of the baseline characteristics including spinopelvic mobility. The absolute error for achieving the planned horizontal COR was median 1.4 mm (interquartile range (IQR) 0.87 to 3.42) in RO THA versus 4.3 mm (IQR 3 to 6.8; p < 0.001); vertical COR mean 0.91 mm (SD 0.73) in RO THA versus 2.3 mm (SD 1.3; p < 0.001); and combined offset median 2 mm (IQR 0.97 to 5.45) in RO THA versus 3.9 mm (IQR 2 to 7.9; p = 0.019). Improved accuracy was observed with RO THA in achieving the desired acetabular component positioning (root mean square error for anteversion and inclination was 2.6 and 1.3 vs 8.9 and 5.3, repectively) and leg length (mean 0.6 mm vs 1.4 mm; p < 0.001). Patient-reported outcome measures were comparable between the two groups at baseline and one year. Participants in the RO THA group needed fewer physiotherapy sessions postoperatively (median six (IQR 4.5 to 8) vs eight (IQR 6 to 11; p = 0.005).

Conclusion

This RCT suggested that robotic-arm assistance in THA was associated with improved accuracy in restoring the native COR, better preservation of the combined offset, leg length correction, and superior accuracy in achieving the desired acetabular component positioning. Further evaluation through long-term and registry data is necessary to assess whether these findings translate into improved implant survival and functional outcomes.

The Value of Computed Tomography Scan in Three-dimensional Planning and Intraoperative Navigation in Primary Total Hip Arthroplasty

Total hip arthroplasty (THA) is a frequently performed procedure; the objective is restoration of native hip biomechanics and achieving functional range of motion (ROM) through precise positioning of the prosthetic components. Advanced three-dimensional (3D) imaging and computed tomography (CT)-based navigation are valuable tools in both the preoperative planning and intraoperative execution. The aim of this study is to provide a thorough overview on the applications of CT scans in both the preoperative and intraoperative settings of primary THA. Preoperative planning using CT-based 3D imaging enables greater accuracy in prediction of implant sizes, leading to enhancement of surgical workflow with optimization of implant inventory. Surgeons can perform a more thorough assessment of posterior and anterior acetabular wall coverage, acetabular osteophytes, anatomical landmarks, and thus achieve more functional implant positioning. Intraoperative CT-based navigation can facilitate precise execution of the preoperative plan, to attain optimal positioning of the prosthetic components to avoid impingement. Medial reaming can be minimized preserving native bone stock, which can enable restoration of femoral, acetabular, and combined offsets. In addition, it is associated with greater accuracy in leg length adjustment, a critical factor in patients' postoperative satisfaction. Despite the higher costs and radiation exposure, which currently limits its widespread adoption, it offers many benefits, and the increasing interest in robotic surgery has facilitated its integration into routine practice. Conducting additional research on ultra-low-dose CT scans and examining the potential for translation of 3D imaging into improved clinical outcomes will be necessary to warrant its expanded application.

Accuracy of Preoperative 3D vs 2D Digital Templating for Cementless Total Hip Arthroplasty Using a Direct Anterior Approach

Background

An important aspect of preoperative planning for total hip arthroplasty is templating. Although two-dimensional (2D) templating remains the gold standard, computerized tomography (CT)-based three-dimensional (3D) templating is a novel preoperative planning technique. This study aims to compare the accuracy of a 2D and 3D plan using an anterior approach for the placement of the same uncemented prosthesis.

Methods

Two consecutive cohorts of 100 patients each were retrospectively analyzed. We analyzed the accuracy of the size of the implant (stem, cup, head), the length of head, and offset. As a secondary criterion, we analyzed the rates of stems with more than 3° of varus, fracture, and/or subsidence at 3 months postoperatively.

Results

Within the exact size, the accuracy of the stem and cup size with the 2D plan was 69% and 56%, respectively. With the 3D plan accuracy being 88% (P = .0046) and 96% (P < .0001), respectively. Regarding size and length of the implant head, accuracy was 86% and 82% with the 2D plan and 100% (P < .0001) and 94% (P = .016), respectively, with the 3D plan. The offset of the implants increased beyond 3 mm in 23% of patients in the 2D group and in 5% of patients in the 3D group (P = .0003). The rate of varus stems was 10% in the 2D group and 2% in the 3D group (P = .03). Two fractures and one case of subsidence occurred in the 2D group. None were identified in the 3D cohort.

Conclusions

A CT-based 3D plan is more accurate for implant size selection, allows better prosthetic offset, and reduces the rate of varus stems.

A review of robotic-assisted total hip arthroplasty

Total hip arthroplasty (THA) is a successful surgical method for hip replacement but still poses challenges and risks. Robotic-assisted THA (rTHA) using new generation robotic systems has emerged to improve surgical precision and outcomes. The purpose of this paper is to review the literature on rTHA, with a focus on its advantages, such as individualized preoperative planning, intraoperative assistance, and improved accuracy in implantation, especially in complex cases. Additionally, it aims to explore the disadvantages associated with the use of rTHA, including high costs, the learning curve, and prolonged operation time compared to manual THA (mTHA), which are critical drawbacks that require careful consideration and efforts for minimization. Some financial analyses suggest that rTHA may offer cost-effectiveness and reduced postoperative costs compared to mTHA. While technological advancements are expected to reduce technical complications, there are still debates surrounding long-term outcomes. Practical limitations, such as limited availability and accessibility, also warrant attention. Although the development of rTHA shows promise, it is still in its early stages, necessitating critical evaluation and further research to ensure optimal patient benefits.

Artificial intelligence in orthopaedic surgery

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as 'big data', AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI's limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.