Determination of pre-arthropathy scapular anatomy with a statistical shape model: part I—rotator cuff tear arthropathy

Verhaegen, Filip; Meynen, Alexander; Matthews, Harold; Claes, Peter; Debeer, Philippe; Scheys, Lennart

doi:10.1016/j.jse.2020.07.043

Cited by 12 publications

(22 citation statements)

References 35 publications

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“…However, based on our results, we could not confirm these associations: Prearthropathy glenoid inclination did not have any association with the amount or the occurrence of HHM. This is in line with our previously published findings, where we could not find an association between RCTA and glenoid inclination 22 . On the basis of these findings, glenoid inclination adaptation does not seem useful in terms of HHM correction.…”

Section: Discussionsupporting

confidence: 92%

“…From a mixed CT scan dataset of patients undergoing rTSA and patients without radiographic signs of HHM or arthropathy (=control group) as judged by an experienced shoulder surgeon (Filip Verhaegen), 64 patients with RCTA and 49 “control” patients (39 with rotator cuff pathology and 10 without specific clinical information available) were included based on the availability of the entire scapula and proximal humerus on the CT scan images 22 . All CT scan images were acquired with the patient in the supine position with the arm adducted towards the trunk, elbow extended, and the handheld against the thigh.…”

Section: Methodsmentioning

confidence: 99%

“…From all the CT scan images of the RCTA patients, complete 3D scapular bone models were created. In case of any sign of glenoid erosion or bone alterations, a previously reported SSM methodology was used to reconstruct the prearthropathy scapular morphology 22 . The hereby established point‐to‐point correspondences were used to automate the 3D measurements of the prearthropathy scapular anatomy on all RCTA patients, namely glenoid version and inclination, scapular offset, critical shoulder angle (CSA), 24 posterior acromial slope (PAS), 25 and lateral acromial angle (LAA) 26 …”

Section: Methodsmentioning

confidence: 99%

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Quantitative SSM‐based analysis of humeral head migration in rotator cuff tear arthropathy patients

Verhaegen¹,

Meynen

Plessers

et al. 2021

Journal Orthopaedic Research

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Rotator cuff tear arthropathy (RCTA) is characterized by massive rotator cuff tearing combined with humeral head migration (HHM). The aim of this study is to investigate the quantitative characteristics of this migration and its association with glenoid erosions and prearthropathy scapular anatomy. We quantified HHM and prearthropathy scapular anatomy of 64 RCTA patients with statistical shape modeling‐based techniques. Glenoid erosion was classified according to Sirveaux et al. A cutoff value for confirming HHM was 5 mm based on a control group of 49 patients. Group 1 (RCTA without HHM) consisted of 21 patients, with a mean subluxation distance (SLD) of 3 mm. Group 2 (RCTA with HHM) consisted of 43 patients, with mean SLD of 9 mm, SLD in the anteroposterior plane of −1 mm (SD ± 4 mm), SLD in the superoinferior plane of 7 mm (SD ± 3 mm), and subluxation angle (SLA) of −5° (SD ± 40°). Analysis with Fisher's exact test showed a clear association between HHM and glenoid erosions (p = 0.002). Multivariate regression analysis of Group 2 showed that prearthropathy lateral acromial angle combined with critical shoulder angle (p = 0.004) explained 21% of the observed variability in SLD. The prearthropathy glenoid version explained 23% of the variability in SLA (p = 0.001). HHM in RCTA patients has a wide variation in both magnitude and direction leading to a distorted glenohumeral relationship in the coronal and axial plane. HHM is highly associated with the occurrence of glenoid erosions. There is a correlation between the prearthropathy scapular anatomy and the magnitude and direction of HHM.

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Section: Discussionsupporting

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Quantitative SSM‐based analysis of humeral head migration in rotator cuff tear arthropathy patients

Verhaegen¹,

Meynen

Plessers

et al. 2021

Journal Orthopaedic Research

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“…Moreover, SSMs have a generative power: By changing the variables, new shape instances that are plausible within the given population can be created. Various studies have proven the efficacy of SSMs in clinical settings including implant design and treatment planning 9‐12 . Traditionally, SSMs model shape variation by principal component analysis (PCA) of a set of training shapes.…”

Section: Introductionmentioning

confidence: 99%

“…Various studies have proven the efficacy of SSMs in clinical settings including implant design and treatment planning. [9][10][11][12] Traditionally, SSMs model shape variation by principal component analysis (PCA) of a set of training shapes. PCA decomposes the complete 3D shape variation data into uncorrelated variables, each representing an individual's position along a complex linear transformation of the shape.…”

Section: Introductionmentioning

confidence: 99%

Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning

Nauwelaers

Matthews

Fan

et al. 2021

Orthod Craniofacial Res

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Objectives: Palatal shape contains a lot of information that is of clinical interest. Moreover, palatal shape analysis can be used to guide or evaluate orthodontic treatments. A statistical shape model (SSM) is a tool that, by means of dimensionality reduction, aims at compactly modeling the variance of complex shapes for efficient analysis. In this report, we evaluate several competing approaches to constructing SSMs for the human palate. Setting and Sample Population:This study used a sample comprising digitized 3D maxillary dental casts from 1,324 individuals. Materials and methods: Principal component analysis (PCA) and autoencoders (AE)are popular approaches to construct SSMs. PCA is a dimension reduction technique that provides a compact description of shapes by uncorrelated variables. AEs are situated in the field of deep learning and provide a non-linear framework for dimension reduction. This work introduces the singular autoencoder (SAE), a hybrid approach that combines the most important properties of PCA and AEs. We assess the performance of the SAE using standard evaluation tools for SSMs, including accuracy, generalization, and specificity.

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

Verhaegen

Meynen

Pitocchi

et al. 2022

Journal Orthopaedic Research

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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.

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Determination of pre-arthropathy scapular anatomy with a statistical shape model: part I—rotator cuff tear arthropathy

Cited by 12 publications

References 35 publications

Quantitative SSM‐based analysis of humeral head migration in rotator cuff tear arthropathy patients

Quantitative SSM‐based analysis of humeral head migration in rotator cuff tear arthropathy patients

Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning

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

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