The area method for measuring acetabular cup anteversion: An accurate and autonomous solution

Is the Acetabular Cup Orientation Different in Robot-Assisted and Conventional Total Hip Arthroplasty With Right-Handed Surgeons Using an Anterolateral Approach?

Introduction Total hip arthroplasty (THA) is one of the most successful orthopaedic procedures. Survival rates from 90% at 10 years to 93% at 20 years have been reported in different studies. Differences in implant and patient characteristics can undoubtedly explain some of this variability observed in prosthesis durability, but the effect of surgical technique and implant orientation cannot be ignored. Therefore, many intraoperative methods (anatomic landmarks, intraoperative x-ray, fluoroscopy, navigation, and robotic surgery) have been attempted to avoid acetabular component malpositioning. Although postoperative computed tomography (CT) is accepted as the gold standard for the measurement of acetabular anteversion, it remains controversial in respect of costs and radiation exposure. The aim of this study was to examine how acetabular component orientation was affected in robotic and conventional THA operations performed by two surgeons with right-hand dominance. Material and methods The study included 113 primary THA operations performed on 113 patients between 2017 and 2022 in two groups: (i) robotic THA (Mako, Stryker Corporation, Kalamazoo, Michigan, United States) (55 patients) and (ii) conventional THA (58 patients). The patients comprised 51 males and 62 females. THA was performed on 54 right-side hips and 59 left-side hips. The operations were performed by two orthopaedic surgeons, each with 20 years of arthroplasty experience, on all the patients in the lateral decubitus position with an anterolateral approach. In all the cases, the orientation of the acetabular component was 40° inclination and 20° anteversion.  Difficult THA procedures (patients with developmental dysplasia of the hip (DDH), a history of hip surgery, revision THA, defect or deformity of the acetabulum, a history of scoliosis or lumbar posterior surgery, or those requiring proximal femoral osteotomy) were excluded from the study. Using the Liaw and Lewinnek methods, the acetabular component anteversion was measured on the radiographs taken in the optimal position postoperatively and the acetabular cup inclination angles were measured on the pelvis radiographs. The groups were compared using the Kolmogorov-Smirnov, Pearson Chi-square and Mann-Whitney U statistical tests. The limits were accepted as 40±5° for inclination and 20±5° for anteversion. Results No statistically significant difference was determined between the groups in respect of age, gender, or operated side. No statistically significant difference was determined between the optimal acetabular cup inclination angles of the robotic and conventional THA groups (p = 0.79). No statistically significant difference was determined between the optimal acetabular cup anteversion angles of the left and right conventional THA groups. Statistically significantly better results were determined in the robotic group in respect of acetabular cup anteversion (p<0,001).  Conclusion The optimal orientation of the acetabular component is a key factor for successful THA. Otherwise, revision surgery is inevitable for reasons such as instability, impingement, or increased wear. The results of this study demonstrated that robotic surgery was superior to the conventional method in the placement of the acetabular component in the desired orientation.

Artificial Intelligence Autonomously Measures Cup Orientation, Corrects for Pelvis Orientation, and Identifies Retroversion From Antero-Posterior Pelvis Radiographs

BACKGROUND

Measuring cup orientation is time consuming and inaccurate, but orientation influences the risk of impingement and dislocation following total hip arthroplasty (THA). This study designed an artificial intelligence (AI) program to autonomously determine cup orientation, correct for pelvis orientation, and identify cup retroversion from an antero-posterior pelvic radiographs.

METHODS

There were 2,945 patients between 2012 and 2019 identified to have 504 computed tomographic (CT) scans of their THA. A 3-dimensional (3D) reconstruction was performed on all CTs, where cup orientation was measured relative to the anterior pelvic plane. Patients were randomly allocated to training (4,000 x-rays), validation (511 x-rays), and testing (690 x-rays) groups. Data augmentation was applied to the training set (n = 4,000,000) to increase model robustness. Statistical analyses were performed only on the test group in their accuracy with CT measurements.

RESULTS

AI predictions averaged 0.22 ± 0.03 seconds to run on a given radiograph. Pearson correlation coefficient was 0.976 and 0.984 for AI measurements with CT, while hand measurements were 0.650 and 0.687 for anteversion and inclination, respectively. The AI measurements more closely represented CT scans when compared to hand measurements (P < .001). Measurements averaged 0.04 ± 2.21°, 0.14 ± 1.66°, -0.31 ± 8.35°, and 6.48° ± 7.43° from CT measurements for AI anteversion, AI inclination, hand anteversion, and hand inclination, respectively. AI predictions identified 17 radiographs as retroverted with 100.0% accuracy (total retroverted, n = 45).

CONCLUSION

The AI algorithms may correct for pelvis orientation when measuring cup orientation on radiographs, outperform hand measurements, and may be implemented in a timely fashion. This is the first method to identify a retroverted cup from a single AP radiograph.

Concerns surrounding application of artificial intelligence in hip and knee arthroplasty

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.