Automated three-dimensional measurements of version, inclination, and subluxation

Artificial intelligence-based three-dimensional templating for total joint arthroplasty planning: a scoping review

PURPOSE

The purpose of this review is to evaluate the current status of research on the application of artificial intelligence (AI)-based three-dimensional (3D) templating in preoperative planning of total joint arthroplasty.

METHODS

This scoping review followed the PRISMA, PRISMA-ScR guidelines, and five stage methodological framework for scoping reviews. Studies of patients undergoing primary or revision joint arthroplasty surgery that utilised AI-based 3D templating for surgical planning were included. Outcome measures included dataset and model development characteristics, AI performance metrics, and time performance. After AI-based 3D planning, the accuracy of component size and placement estimation and postoperative outcome data were collected.

RESULTS

Nine studies satisfied inclusion criteria including a focus on computed tomography (CT) or magnetic resonance imaging (MRI)-based AI templating for use in hip or knee arthroplasty. AI-based 3D templating systems reduced surgical planning time and improved implant size/position and imaging feature estimation compared to conventional radiographic templating. Several components of data processing and model development and testing were insufficiently covered in the studies included in this scoping review.

CONCLUSIONS

AI-based 3D templating systems have the potential to improve preoperative planning for joint arthroplasty surgery. This technology offers more accurate and personalized preoperative planning, which has potential to improve functional outcomes for patients. However, deficiencies in several key areas, including data handling, model development, and testing, can potentially hinder the reproducibility and reliability of the methods proposed. As such, further research is needed to definitively evaluate the efficacy and feasibility of these systems.

Can We Completely Trust in Automated Software for Preoperative Planning of Shoulder Arthroplasty? Software Update May Modify Glenoid Version, Glenoid Inclination and Humeral Head Subluxation Values

Background: The purpose of this study was to evaluate the impact of software updating on measurements of the glenoid inclination and version, along with humeral head subluxation performed by an automated 3D planning program. The hypothesis was that the software update could significantly modify the values of the glenoid inclination and version, as well as of the humeral head subluxation. Methods: A comprehensive pool of 76 shoulder computed tomography (CT) scans of patients who underwent total shoulder arthroplasty (TSA) or reverse total shoulder arthroplasty (RTSA) were analyzed with the automated program Blueprint in 2018 and again in 2020 after a software update. Results: A statistically significant difference of 8.1 ± 8.2 and 5.4 ± 7.8 (mean difference of −2.8 ± 5.0, p < 0.001) was indeed reached when comparing the mean glenoid inclination achieved with Blueprint 2018 and Blueprint 2020, respectively. The glenoid version, as well as the humeral head subluxation evaluations, were not significantly different between the two software versions, with mean values being −9.4 ± 8.9 and −9.0 ± 7.4 and 60.1 ± 12.6 and 61.8 ± 12.0, respectively (p = 0.708 and p = 0.115, respectively). In 22% of CT scans, the software update determined a variation of the glenoid inclination of more than 5° or 10°. Conclusion: The present study shows the software update of an automated preoperative planning program may significantly modify the values of glenoid inclination. Even though without a significant difference, variations were also found for the glenoid version and humeral head subluxation. Accordingly, these results should further advise surgeons to carefully and critically evaluate data acquired with automated software.

Quantitative statistical shape model-based analysis of humeral head migration, Part 2: Shoulder osteoarthritis

We wanted to investigate the quantitative characteristics of humeral head migration (HHM) in shoulder osteoarthritis (OA) and their possible associations with scapular morphology. We quantified CT-scan-based-HHM in 122 patients with a combination of automated 3D scapulohumeral migration (=HHM with respect to the scapula) and glenohumeral migration (=HHM with respect to the glenoid) measurements. We divided OA patients in Group 1 (without HHM), Group 2a (anterior HHM) and Group 2b (posterior HHM). We reconstructed and measured the prearthropathy scapular anatomy with a statistical shape model technique. HHM primarily occurs in the axial plane in shoulder OA. We found "not-perfect" correlation between subluxation distance AP and scapulohumeral migration values (r_s  = 0.8, p < 0.001). Group 2b patients had a more expressed prearthropathy glenoid retroversion (13° vs. 7°, p < 0.001) and posterior glenoid translation (4 mm vs. 6 mm, p = 0.003) in comparison to Group 1. Binary logistic regression analysis indicated prearthropathy glenoid version as a significant predictor of HHM (χ² = 27, p < 0.001). Multivariate regression analysis showed that the pathologic version could explain 56% of subluxation distance-AP variance and 75% of the scapulohumeral migration variance (all p < 0.001). Herewith, every degree increase in pathologic glenoid retroversion was associated with an increase of 1% subluxation distance-AP, and scapulohumeral migration. The occurrence of posterior HHM is associated with prearthropathy glenoid retroversion and more posterior glenoid translation. The reported regression values of HHM in the function of the pathologic glenoid version could form a basis toward a more patient-specific correction of HHM.

Anatomic Total Shoulder Arthroplasty: Component Size Prediction with 3-Dimensional Pre-Operative Digital Planning

Background

The rate, complexity, and cost of total shoulder arthroplasty (TSA) continues to grow. Technology has advanced pre-operative templating. Reducing cost of TSA has positive impact for the patient, manufacturer, and hospital. The aim of this study was to evaluate the accuracy of implant size selection based on 3-D templating. Our hypothesis was that pre-operative templating would enable accurate implant prediction within one size.

Methods

Multicenter retrospective study of anatomic TSAs templated utilizing 3-D virtual planning technology. This program uses computed tomography (CT) scans allowing the surgeon to predict component sizes of the glenoid and humeral head and stem. Pre-operative templated implant size were compared to actual implant size at the time of surgery. Primary data analysis utilized unweighted Cohen's Kappa test.

Results

111 TSAs were analyzed from five surgeons. Pre-operative templated glenoid sizes were within one size of actual implant in 99% and exactly matched in 89%. For patients requiring a posterior glenoid augment (n = 14), 100% of implants were within one size of the template and 93% matched exactly. For stemless humeral components (n = 87) implanted, 98% matched the pre-operative template within one size with 79% exactly matched. For stemmed components (n = 24), 88% of cases were within one size of the preoperative plan and exactly matching in 83%. Humeral head diameter matched within one size of the pre-operative template in 84% of cases and exactly matched in 72%.

Conclusion

Pre-operative 3-D templating for TSAs can accurately predict glenoid and humeral component size. This study sets the groundwork for utilization of pre-operative 3-D templating as a potential method to reduce overall TSA costs by managing cost of implants, reducing inventory needs, and improving surgical efficiency.

SciKit-SurgeryGlenoid, an Open Source Toolkit for Glenoid Version Measurement

Correct understanding of the geometry of the glenoid (the socket of the shoulder joint) is key to successful planning of shoulder replacement surgery. This surgery typically involves placing an implant in the shoulder joint to restore joint function. The most relevant geometry is the glenoid version, which is the angular orientation of the glenoid surface relative to the long axis of the scapula in the axial plane. However, measuring the glenoid version is not straightforward and there are multiple measurement methods in the literature and used in commercial planning software. In this paper we introduce SciKit-SurgeryGlenoid, an open source toolkit for the measurement of glenoid version. SciKit-SurgeryGlenoid contains implementations of the 4 most frequently used glenoid version measurement algorithms enabling easy and unbiased comparison of the different techniques. We present the results of using the software on 10 sets of pre-operative CT scans taken from patients who have subsequently undergone shoulder replacement surgery. We further compare these results with those obtained from a commercial implant planning software. SciKit-SurgeryGlenoid currently requires manual segmentation of the relevant anatomical features for each method. Future work will look at automating the segmentation process to build an automatic and repeatable pipeline from CT or radiograph to quantitative glenoid version measurement.

Computer-Assisted Preoperative Planning and Patient-Specific Instrumentation for Glenoid Implants in Shoulder Arthroplasty

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Glenoid component positioning affects implant survival after total shoulder arthroplasty, and accurate glenoid-component positioning is an important technical aspect.

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The use of virtual planning and patient-specific instrumentation has been shown to produce reliable implant placement in the laboratory and in some clinical studies.

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Currently available preoperative planning software programs employ different techniques to generate 3-dimensional models and produce anatomic measurements potentially affecting clinical decisions.

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There are no published data, to our knowledge, on the effect of preoperative computer planning and patient-specific instrumentation on long-term clinical outcomes.

Subluxation in the Arthritic Shoulder

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The term "subluxation" means partial separation of the joint surfaces. In the arthritic shoulder, "arthritic glenohumeral subluxation" refers to displacement of the humeral head on the surface of the glenoid.

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The degree of arthritic glenohumeral subluxation can be measured using radiography with standardized axillary views or computed tomography (CT).

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Shoulders with a type-B1 or B2 glenoid may show more posterior subluxation on an axillary radiograph that is made with the arm in an elevated position than on a CT scan that is made with the arm at the side.

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The degree of arthritic glenohumeral subluxation is not closely related to glenoid retroversion.

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The position of the humeral head with respect to the plane of the scapula is related to glenoid retroversion and is not a measure of glenohumeral subluxation.

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Studies measuring glenohumeral subluxation before and after arthroplasty should clarify its importance to the clinical outcomes of shoulder reconstruction.

A novel method for localization of the maximum glenoid bone defect during reverse shoulder arthroplasty

Background

Management of glenoid bone defects during reverse shoulder arthroplasty remains a challenge. The aim of our study was to preoperatively localize the maximal depth of glenoid bone defects in relation to glenoid reaming.

Methods

Thirty preoperative shoulder computed tomography scans were collected. Three assessors created standardized surgical plans, using 3-dimensional (3D) computed tomography–based Blueprint planning software in which the reaming axis was held constant at zero degrees of version and inclination. Each plan resulted in a 2-dimensional (2D) image of the reamer’s contact on the glenoid and a corresponding 3D representation of the glenoid bone defect. The position of the maximum glenoid defect was localized on both the 2D and 3D images. Descriptive statistics were calculated. The correlation between angles from 2D and 3D images was assessed, and intraclass correlation was used to assess inter-rater and intrarater reliability.

Results

Twenty-eight patients were included. The overall mean difference between 2D and 3D angles was 5.4° (standard deviation 5.2°). The correlation between 2D and 3D angles was almost perfect. Intraclass correlation results demonstrated near-perfect agreement. The maximal glenoid defect was within 5% of a circle (or +/- 9°) from perpendicular to the high-side ream line in 85.1% of comparisons and was within 10% of a circle in 97.6% of comparisons.

Conclusion

Using Blueprint planning software, we have demonstrated with almost perfect agreement among 3 assessors that when the reaming axis is held constant, the maximum glenoid bone defect is reliably located perpendicular to the glenoid ream line.