João Folgado

This study aimed to develop and evaluate algorithms for reconstructing subject-specific thorax geometries from a limited set of skin landmarks, typically collected in motion analysis studies, using statistical shape models (SSMs). A dataset of DICOM CT scans from 76 individuals was used to generate thorax geometries, comprising the sternum, ribs, and vertebrae C6 to T10, and to collect five bone landmarks and their corresponding skin landmarks. Two thorax SSMs were developed from the CT-derived bone geometries, differing in whether skin landmarks were embedded within the SSM. Based on these models, two reconstruction strategies were implemented using an optimization framework: (i) a bone-landmark–based approach in which multiple linear regression was used to map skin landmarks to bone landmarks (SSM-BL), which served as optimization targets, and (ii) a skin-landmark–embedded approach that used skin landmarks directly as optimization targets (SSM-SL). Skin-to-bone mapping accuracy was quantified using mean absolute error (MAE) and root-mean-square error (RMSE). Thorax reconstruction accuracy was assessed using point-to-point surface errors, reported using MAE and RMSE. Skin-to-bone regression models achieved MAE and RMSE below 5 mm for most landmark coordinates. No statistically significant differences were observed between reconstruction strategies. Thorax reconstruction errors ranged from 8.26 ± 1.96 mm to 8.36 ± 1.70 mm (MAE) and from 9.10 ± 1.12 mm to 9.32 ± 1.49 mm (RMSE), depending on the number of retained principal components. The proposed SSM-based methods enable thorax reconstruction from sparse data, capturing global thorax morphology. However, local anatomical details were less accurately represented. Graphical Abstract

Purpose: Arthroscopic superior capsule reconstruction (ASCR) is a surgical option for the treatment of irreparable rotator cuff tears. This systematic review aimed to synthesize the available biomechanical evidence on ASCR, especially on the influence of surgical and graft‐related factors on shoulder biomechanics and graft mechanics. It was hypothesized that graft type, dimensions and concomitant procedures affect postoperative biomechanics following ASCR. Methods: A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines using PubMed, Web of Science, Scopus, Cochrane Library and Ovid databases. Eligible studies included cadaveric, in vitro, in vivo and in silico biomechanical studies evaluating ASCR‐related factors. Animal studies, clinical outcome studies and comparisons with alternative techniques were excluded. Results: A total of 37 biomechanical studies were included. Investigated factors comprised concomitant procedures (29.3%), graft type (26.8%), shoulder position of fixation (22.0%), graft thickness (12.2%) and fixation technique (9.8%). Most studies used cadaveric models (86.5%) under quasi‐static conditions with simplified muscle‐loading protocols and assessed outcomes at three to five shoulder positions in the frontal plane. Most studies focused on the spacer role of the graft in limiting superior humeral head translation, while its contribution to humeral head centring and dynamic force redistribution was overlooked. Fascia lata grafts were generally associated with improved joint stability and reduced subacromial contact pressures. The shoulder position of fixation influenced joint stability, and concomitant procedures were required to restore anterior–posterior stability. Substantial heterogeneity in experimental protocols, outcome measures and reporting practices limited a direct comparison across studies. Conclusions: The current biomechanical evidence on ASCR remains constrained by methodological simplifications and the isolated, rather than combined, assessment of ASCR‐related factors. Available data suggest that graft type and dimensions, and the performance of concomitant procedures restoring capsule continuity, influence the recovery of joint stability. Level of Evidence: Basic science study: biomechanics.

Purpose: The aim of this study was to develop an algorithm for the reconstruction of scapula bone shapes from skin landmarks, using a statistical shape model (SSM). Methods: A sample of 56 scapula segmentations was used, as well as 4 scapular bone and skin landmarks. Regression models were built to predict the coordinates of bone landmarks from skin landmarks using subject-specific variables, namely skin landmark coordinates, sex, age, weight, and height. The scapula shapes were reconstructed by fitting the bone landmarks of the SSM’s mean shape to the predicted bone landmarks of the subject. Results: The developed regression models registered a R ² ranging from 0.70 to 0.98, with a maximum median error of 4 mm. The average surface-to-surface errors were equal to 2.41 and 2.45 mm using digitized and predicted bone landmarks, respectively. No significant statistical differences were observed between scapula shapes reconstructed from digitized and predicted bone landmarks. Conclusion: This study demonstrated the reliability of the developed algorithm in deriving subject-specific scapula shapes from experimentally acquired data, highlighting that scapula shape reconstructions based on a limited set of landmarks can effectively generate subject-specific computational models without the need for additional medical imaging.