Confronting historical legacies of biological anthropology in South Africa-Restitution, redress and community-centered science: The Sutherland Nine.

We describe a process of restitution of nine unethically acquired human skeletons to their families, together with attempts at redress. Between 1925-1927 C.E., the skeletonised remains of nine San or Khoekhoe people, eight of them known-in-life, were removed from their graves on the farm Kruisrivier, near Sutherland in the Northern Cape Province of South Africa. They were donated to the Anatomy Department at the University of Cape Town. This was done without the knowledge or permission of their families. The donor was a medical student who removed the remains from the labourers' cemetery on his family farm. Nearly 100 years later, the remains are being returned to their community, accompanied by a range of community-driven interdisciplinary historical, archaeological and analytical (osteobiographic, craniofacial, ancient DNA, stable isotope) studies to document, as far as possible, their lives and deaths. The restitution process began by contacting families living in the same area with the same surnames as the deceased. The restitution and redress process prioritises the descendant families' memories, wishes and desire to understand the situation, and learn more about their ancestors. The descendant families have described the process as helping them to reconnect with their ancestors. A richer appreciation of their ancestors' lives, gained in part from scientific analyses, culminating with reburial, is hoped to aid the descendant families and wider community in [re-]connecting with their heritage and culture, and contribute to restorative justice, reconciliation and healing while confronting a traumatic historical moment. While these nine individuals were exhumed as specimens, they will be reburied as people.

Reconstruction of the mandible from partial inputs for virtual surgery planning.

Statistical Shape Models (SSMs) and Sparse Prediction Models (SPMs) based on regressions between cephalometric measurements were compared against standard practice in virtual surgery planning for reconstruction of mandibular defects. Emphasis was placed on the ability of the models to reproduce clinically relevant metrics. CT scans of 50 men and 50 women were collected and split into training and testing datasets according to an 80:20 ratio. The scans were segmented, and anatomical landmarks were identified. SPMs were constructed based on direct regressions between measurements derived from the anatomical landmarks. SSMs were developed by establishing correspondence between the segmented meshes, performing alignment, and principal component analysis. Anterior and bilateral defects were simulated by removing sections of the mandibles in the testing set. Measurement errors after reconstruction ranged from 1.07˚ to 2.2˚ and 0.66 mm to 2.02 mm for mirroring, from 0.45˚ to 3.67˚ and 0.66 mm to 2.54 mm for the SSMs, and from 1.74˚ to 5.01˚ and 0.64 mm to 2.89 mm for the SPMs. Surface-to-surface errors ranged from 1.01 mm to 1.29 mm and 1.06 mm to 1.33 mm for mirroring and SSMs, respectively. Based on the results, SSMs are recommended for VSP in the absence of normal patient anatomy.

Dynamic multi feature-class Gaussian process models.

In model-based medical image analysis, three relevant features are the shape of structures of interest, their relative pose, and image intensity profiles representative of some physical properties. Often, these features are modelled separately through statistical models by decomposing the object's features into a set of basis functions through principal geodesic analysis or principal component analysis. However, analysing articulated objects in an image using independent single object models may lead to large uncertainties and impingement, especially around organ boundaries. Questions that come to mind are the feasibility of building a unique model that combines all three features of interest in the same statistical space, and what advantages can be gained for image analysis. This study presents a statistical modelling method for automatic analysis of shape, pose and intensity features in medical images which we call the Dynamic multi feature-class Gaussian process models (DMFC-GPM). The DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variations. Our method is defined in a continuous domain with a principled way to represent shape, pose and intensity feature-classes in a linear space, based on deformation fields. A deformation field-based metric is adapted in the method for modelling shape and intensity variation as well as for comparing rigid transformations (pose). Moreover, DMFC-GPMs inherit properties intrinsic to GPs including marginalisation and regression. Furthermore, they allow for adding additional pose variability on top of those obtained from the image acquisition process; what we term as permutation modelling. For image analysis tasks using DMFC-GPMs, we adapt Metropolis-Hastings algorithms making the prediction of features fully probabilistic. We validate the method using controlled synthetic data and we perform experiments on bone structures from CT images of the shoulder to illustrate the efficacy of the model for pose and shape prediction. The model performance results suggest that this new modelling paradigm is robust, accurate, accessible, and has potential applications in a multitude of scenarios including the management of musculoskeletal disorders, clinical decision making and image processing.

Cross-Modality Image Adaptation Based on Volumetric Intensity Gaussian Process Models (VIGPM).

Image-based diagnosis routinely depends on more that one image modality for exploiting the complementary information they provide. However, it is not always possible to obtain images from a secondary modality for several reasons such as cost, degree of invasiveness and non-availability of scanners. Three-dimensional (3D) morphable models have made a significant contribution to the field of medical imaging for feature-based analysis. Here we extend their use to encode 3D volumetric imaging modalities. Specifically, we build a Gaussian Process (GP) over transformations establishing anatomical correspondence between training images within a modality. Given, two different modalities, the GP's eigenspace (latent space) can then be used to provide a parametric representation of each image modality, and we provide an operator for cross-domain translation between the two. We show that the latent space yields samples that are representative of the encoded modality. We also demonstrate that a 3D volumetric image can be efficiently encoded in latent space and transferred to synthesize the corresponding image in another modality. The framework called VIGPM can be extended by designing a fitting process to learn an observation in a given modality and performing cross-modality synthesis. Clinical Relevance- The proposed method provides a way to access a multi modality image from one modality. Both the source and synthetic modalities are in anatomical correspondence giving access to registered complementary information.

Assessment of the implementation context in preparation for a clinical study of machine-learning algorithms to automate the classification of digital cervical images for cervical cancer screening in resource-constrained settings.

Introduction: We assessed the implementation context and image quality in preparation for a clinical study evaluating the effectiveness of automated visual assessment devices within cervical cancer screening of women living without and with HIV. Methods: We developed a semi-structured questionnaire based on three Consolidated Framework for Implementation Research (CFIR) domains; intervention characteristics, inner setting, and process, in Cape Town, South Africa. Between December 1, 2020, and August 6, 2021, we evaluated two devices: MobileODT handheld colposcope; and a commercially-available cell phone (Samsung A21ST). Colposcopists visually inspected cervical images for technical adequacy. Descriptive analyses were tabulated for quantitative variables, and narrative responses were summarized in the text. Results: Two colposcopists described the devices as easy to operate, without data loss. The clinical workspace and gynecological workflow were modified to incorporate devices and manage images. Providers believed either device would likely perform better than cytology under most circumstances unless the squamocolumnar junction (SCJ) were not visible, in which case cytology was expected to be better. Image quality (N = 75) from the MobileODT device and cell phone was comparable in terms of achieving good focus (81% vs. 84%), obtaining visibility of the squamous columnar junction (88% vs. 97%), avoiding occlusion (79% vs. 87%), and detection of lesion and range of lesion includes the upper limit (63% vs. 53%) but differed in taking photographs free of glare (100% vs. 24%). Conclusion: Novel application of the CFIR early in the conduct of the clinical study, including assessment of image quality, highlight real-world factors about intervention characteristics, inner clinical setting, and workflow process that may affect both the clinical study findings and ultimate pace of translating to clinical practice. The application and augmentation of the CFIR in this study context highlighted adaptations needed for the framework to better measure factors relevant to implementing digital interventions.

A user-centred design framework for mHealth.

BACKGROUND: Mobile health (mHealth) has the potential to improve access to healthcare, especially in developing countries. The proliferation of mHealth has not been accompanied by a corresponding growth in design guidelines for mHealth applications. This paper proposes a framework for mHealth application design that combines the Information Systems Research (ISR) framework and design thinking. We demonstrate a use case for the proposed framework in the form of an app to read the result of the tuberculin skin test (TST), which is used to screen for latent tuberculosis infection. The framework was used in the re-design of the TST reading app but could also be used in earlier stages of mHealth app design. METHODS: The ISR framework and design thinking were merged based on how the modes of design thinking integrate with the cycles of the ISR framework. Using the combined framework, we re-designed an mHealth app for TST reading, intended to be used primarily in a developing context by healthcare workers. Using the proposed framework, the app was iterated upon and developed with the aid of personas, observations, prototyping and questionnaires. RESULT: The combined framework was applied through engagement with end-users, namely ten healthcare workers and ten graduate students. Through review of the literature and iterations of the app prototype, we identified various usability requirements and limitations. These included challenges related to image capture and a misunderstanding of instructions. These insights influenced the development and improvement of the app. CONCLUSION: The combined framework allowed for engagement with end-users and for low-cost, rapid development of the app while addressing contextual challenges and needs. The integration of design thinking modes with the ISR cycles was effective in achieving the objectives of each approach. The combined framework acknowledges the importance of engaging users when implementing mHealth technologies, especially in developing and under-resourced contexts. Findings from this study support the use of this framework as a guide in the design of user-centred mHealth interventions.

Engaging Communities on Health Innovation: Experiences in Implementing Design Thinking.

Design thinking is an approach gaining momentum as a strategy for promoting empathy-driven, human-centered innovation. To evaluate the implementation of design thinking for engaging with communities about health and well-being, we undertook a qualitative analysis of an engagement between students and relevant community stakeholders during a project to develop a health intervention aimed at increasing medication compliance in an elderly community in South Africa. Major findings from this research indicated that design thinking offers opportunities for enriching community-university engagements. However, given constraints on time and procedure that are associated with the academy, the fast, dynamic style of design thinking is not optimally suited for developing the level of trust and rapport that is required for engagements in communities where social-cultural differences operate as barriers. Researchers who wish to utilize design thinking will need to devise and tailor additions to tool kits to meet the specific needs of engagements related to personal health and well-being.

Clinical relevance of augmented statistical shape model of the scapula in the glenoid region.

OBJECTIVE: To illustrate (a) whether a statistical shape model (SSM) augmented with anatomical landmark set(s) performs better fitting and provides improved clinical relevance over non-augmented SSM and (b) which anatomical landmark set provides the best augmentation strategy for predicting the glenoid region of the scapula. METHODS: Scapula SSM was built using 27 dry bone CT scans and augmented with three anatomical landmark sets (16 landmarks each) resulting in three augmented SSMs (aSSMproposed, aSSMset1, aSSMset2). The non-augmented and three augmented SSMs were then used in a non-rigid registration (regression) algorithm to fit to six external scapular shapes. The prediction error by each type of SSM was evaluated in the glenoid region for the goodness of fit (mean error, root mean square error, Hausdorff distance and Dice similarity coefficient) and for four anatomical angles (critical shoulder angle, lateral acromion angle, glenoid inclination, glenopoar angle). RESULTS: Inter- and intra-observer reliability for landmark selection was moderate to excellent (ICC>0.74). Prediction error was significantly lower for SSMnon-augmented for mean (0.9 mm) and root mean square (1.15 mm) distances. Dice coefficient was significantly higher (0.78) for aSSMproposed compared to all other SSM types. Prediction error for anatomical angles was lowest using the aSSMproposed for critical shoulder angle (3.4°), glenoid inclination (2.6°), and lateral acromion angle (3.2°). CONCLUSION AND SIGNIFICANCE: The conventional SSM robustness criteria or better goodness of fit do not guarantee improved anatomical angle accuracy which may be crucial for certain clinical applications in pre-surgical planning. This study provides insights into how SSM augmented with region-specific anatomical landmarks can provide improved clinical relevance.

Statistical Shape Modeling Approach to Predict Missing Scapular Bone.

Prediction of complete and premorbid scapular anatomy is an important aspect of successful shoulder arthroplasty surgeries to treat glenohumeral arthritis and which remains elusive in the current literature. We proposed to build a statistical shape model (SSM) of the scapula and use it to build a framework to predict a complete scapular shape from virtually created scapular bone defects. The bone defects were synthetically created to imitate bone loss in the glenoid region and missing bony part in inferior and superior scapular regions. Sixty seven dry scapulae were used to build the SSM while ten external scapular shapes (not used in SSM building) were selected to map scapular shape variability using its anatomical classification. For each external scapula, four virtual bone defects were created in the superior, inferior and glenoid regions by manually removing a part of the original mesh. Using these defective shapes as prior knowledge, original shapes were reconstructed using scapula SSM and Gaussian process regression. Robustness of the scapula SSM was excellent (generality = 0.79 mm, specificity = 1.74 mm, first 15 principal modes of variations accounted for 95% variability). The validity and quality of the reconstruction of complete scapular bone were evaluated using two methods (1) mesh distances in terms of mean and RMS values and (2) four anatomical measures (three angles: glenoid version, glenoid inclination, and critical shoulder angle, and glenoid center location). The prediction error in the angle measures ranged from 1.0° to 2.2°. For mesh distances, highest mean and RMS error was 0.97 mm and 1.30 respectively. DICE similarity coefficient between the original and predicted shapes was excellent (≥ 0.81). This framework provided high reconstruction accuracy and can be effectively embedded in the pre-surgical planning of shoulder arthroplasty or in morphology-based shoulder biomechanics modeling pipelines.

Statistical shape-kinematics models of the skeletal joints: Application to the shoulder complex.

Patient-specific biomechanical simulations of joints require accurate reconstruction of bony anatomy from medical image data. The articular geometries of the joints may influence their biomechanics. Statistical shape models (SSMs) have become ubiquitous in the literature and aim to capture the natural variation of biological objects. They work by learning the variation from training examples to define the space of valid biological shapes. However, the kinematic information descriptive of the anato-physiological relationship of two interacting objects is not generally encoded in the SSM. Here, we propose a framework for developing combined statistical shape and kinematics models (SSKMs) as Gaussian process morphable models to analyse the shape and kinematics relationship. We demonstrate the framework on a three-dimensional (3D) image data set consisting of ten right-handed cadaveric shoulder joints acquired using computed tomography. Additionally, we simulate specific bone motions to encode kinematics in the combined model. Our SSKM built from shoulder data (matching scapulae and humeri) correctly depicts a correlation between the shape and kinematics as hypothesized. We furthermore demonstrate the ability to marginalize from the SSKM to obtain shape-only variation and kinematics-only variation. Future work aims to use the SSKM framework to understand the relationships between kinematics and shape for various joints as well as to develop patient-specific computational models to evaluate joint biomechanics.

Review of 2-D/3-D Reconstruction Using Statistical Shape and Intensity Models and X-Ray Image Synthesis: Toward a Unified Framework.

Patient-specific three-dimensional (3-D) bone models are useful for a number of clinical applications such as surgery planning, postoperative evaluation, as well as implant and prosthesis design. Two-dimensional-to-3-D (2-D/3-D) reconstruction, also known as model-to-modality or atlas-based 2-D/3-D registration, provides a means of obtaining a 3-D model of a patient's bones from their 2-D radiographs when 3-D imaging modalities are not available. The preferred approach for estimating both shape and density information (that would be present in a patient's computed tomography data) for 2-D/3-D reconstruction makes use of digitally reconstructed radiographs and deformable models in an iterative, non-rigid, intensity-based approach. Based on a large number of state-of-the-art 2-D/3-D bone reconstruction methods, a unified mathematical formulation of the problem is proposed in a common conceptual framework, using unambiguous terminology. In addition, shortcomings, recent adaptations, and persisting challenges are discussed along with insights for future research.

A modified stakeholder participation assessment framework for design thinking in health innovation.

Increasing pressure to improve health outcomes of populations with limited resources has prompted an emphasis on innovation. Design thinking has been proposed as a systematic approach to innovation in health, owing to its human-centred methodology that prioritises deep empathy for the end-users' desires, needs and challenges, which results in a better understanding of the problem in order to develop more comprehensive and effective solutions. A key feature of design thinking is stakeholder participation. If design thinking is going to be used as an approach to design and implement effective, equitable and sustainable health solutions, assessing stakeholder participation should be integrated into the process. Therefore, the purpose of this review is to i) provide an analysis of the literature on assessing stakeholder participation in health and ii) propose a framework based on the literature analysis that can be used to assess stakeholder participation during the design thinking process in health innovation. Drawing from participatory research in health, where stakeholder participation is recognised as a core principle that facilitates the implementation of solutions, we integrate stakeholder evaluation tools into the design thinking approach. We draw on an assessment framework that describes levels of stakeholder participation by their involvement in making decisions about their health. Using a 5-point continuum where the lower end represents the medical approach (professionals make all the decisions) and the upper end represents the community development approach (all stakeholders are key decision makers), we propose a modified assessment framework to evaluate stakeholder participation during the design thinking process. The modified framework provides a simple and practical tool to evaluate stakeholder participation. Stakeholders can rate their perceived level of participation, as well as that of the other stakeholders. Evaluation of participation in a design thinking project may be used to improve participation, and therefore the uptake and sustainability of innovations. The framework may also be used in design thinking beyond health applications.

Mobile phone-based evaluation of latent tuberculosis infection: Proof of concept for an integrated image capture and analysis system.

BACKGROUND: The tuberculin skin test is the most widely used method for detecting latent tuberculosis infection in adults and active tuberculosis in children. We present the development of a mobile-phone based screening tool for measuring the tuberculin skin test induration. METHOD: The tool makes use of a mobile application developed on the Android platform to capture images of an induration, and photogrammetric reconstruction using Agisoft PhotoScan to reconstruct the induration in 3D, followed by 3D measurement of the induration with the aid of functions from the Python programming language. The system enables capture of images by the person being screened for latent tuberculosis infection. Measurement precision was tested using a 3D printed induration. Real-world use of the tool was simulated by application to a set of mock skin indurations, created by a make-up artist, and the performance of the tool was evaluated. The usability of the application was assessed with the aid of a questionnaire completed by participants. RESULTS: The tool was found to measure the 3D printed induration with greater precision than the current ruler and pen method, as indicated by the lower standard deviation produced (0.3 mm versus 1.1 mm in the literature). There was high correlation between manual and algorithm measurement of mock skin indurations. The height of the skin induration and the definition of its margins were found to influence the accuracy of 3D reconstruction and therefore the measurement error, under simulated real-world conditions. Based on assessment of the user experience in capturing images, a simplified user interface would benefit wide-spread implementation. CONCLUSIONS: The mobile application shows good agreement with direct measurement. It provides an alternative method for measuring tuberculin skin test indurations and may remove the need for an in-person follow-up visit after test administration, thus improving latent tuberculosis infection screening throughput.

Anatomic variations in glenohumeral joint: an interpopulation study.

BACKGROUND: This study focused on the unique aspect of investigating shoulder morphometric differences between 2 distinct populations. METHODS: We used 90 computed tomography images of cadaveric shoulders for this study; 45 scans belonged to the South African (SA) cohort (49.74 ± 15.4 years) and the rest were Swiss (CH; 53.8 ± 21 years). The articulating surfaces of the glenohumeral joint were extracted, and their morphometric features, such as head circular diameter, glenoid and humeral head radius of curvature, head height, and humeral height, were measured. RESULTS: The mean interpopulation difference in the circular diameter of the humerus was 2.0 mm (P = .017) and 1.86 mm (P > .05) in the anterior-posterior and superior-inferior directions, respectively. The difference in the radius of curvature between the populations was 1.17 mm (P = .037). The SA shoulders were found to be longer than the CH shoulders by 8.4 mm (P > .05). There was no significant difference in the glenoid radius of curvature. The SA shoulders had higher glenohumeral mismatch (P = .005) and lower conformity index (P = .001) in comparison to the CH shoulders. CONCLUSION: This study presents anatomic differences between African and European glenohumeral articulating surfaces. The results suggest that the glenohumeral geometry is both gender and population specific, and future joint replacements may be designed to address these differences.

Assessment of 3D morphological characteristics of the shoulder bones using statistical shape modeling: Prospective application to handedness.

In this study we have described the use of statistical shape modeling (SSM) technique in evaluating the morphological variation of shoulder bones from a South African population. The anatomical landmark selections were carried out, followed by the registration of the meshes which were validated before establishing the dense correspondence. The SSMs were built and average shape comparison from each side for each bone were made in order to evaluate handedness. In general, there was no error found around the gleno-humeral region which may suggest that the usage of contralateral healthy shoulder could serve as an informed decision making tool for surgery and prosthesis design.

Subject-specific shoulder muscle attachment region prediction using statistical shape models: A validity study.

Subject-specific musculoskeletal models can predict accurate joint and muscle biomechanics thereby helping clinicians and surgeons. Current modeling strategies do not incorporate accurate subject-specific muscle parameters. This study reports a statistical shape model (SSM) based method to predict subject-specific muscle attachment regions on shoulder bones and illustrates the concurrent validity of the predictions. Augmented SSMs of scapula and humerus bones were built using bone meshes and five muscle attachment (origin/insertion) regions which play important role in the shoulder motion and function. Muscle attachments included Subscapularis, Supraspinatus, Infraspinatus, Teres Major and Teres Minor on both the bones. The regions were represented by subset of vertices on the bone meshes and were tracked using vertex identifiers. Subject-specific muscle attachment regions were predicted using external set of bones not used in building the SSMs. Validity of predictions was determined by visual inspection and also by using four similarity measures between predicted and manually segmented regions. Excellent concurrent validity was found indicating the higher accuracy of predictions. This method can be effectively employed in modeling pipelines or in automatic segmentation of medical images. Further validations are warranted on all the muscles of the shoulder complex.

An automated statistical shape model developmental pipeline: application to the human scapula and humerus.

This paper presents development of statistical shape models based on robust and rigid-groupwise registration followed by pointset nonrigid registration. The main advantages of the pipeline include automation in that the method does not rely on manual landmarks or a regionalization step; there is no bias in the choice of reference during the correspondence steps and the use of the probabilistic principal component analysis framework increases the domain of the shape variability. A comparison between the widely used expectation maximization-iterative closest point algorithm and a recently reported groupwise method on publicly available data (hippocampus) using the well-known criteria of generality, specificity, and compactness is also presented. The proposed method gives similar values but the curves of generality and specificity are superior to those of the other two methods. Finally, the method is applied to the human scapula, which is a known difficult structure, and the human humerus.

Precision assessment of stereo-photogrammetrically derived facial landmarks in infants.

BACKGROUND: In order to examine the suitability of an anthropometric imaging device for a particular study population, it is necessary to assess measurement error on members of the population. AIM: We provide an analysis of the precision of a stereo-photogrammetric tool in locating 3D landmark coordinates on images of infants and present the results in a way that facilitates direct comparison with some of the precision results found in the literature. SUBJECTS AND METHODS: Intra- and inter-observer precision are examined for 26 landmarks on 21 infants. Mean absolute differences for landmark coordinates and linear distances, mean landmark coordinate errors, and relative errors of magnitude are calculated. RESULTS: Relative error of magnitude is less than 4% for the majority of intra- and inter-observer inter-landmark distance comparisons. Landmarks lying on the midline of the face and on smooth regions with gentle curvature, in particular the sellion, glabella and nasion, have relatively low precision. CONCLUSION: Our precision values compare well with those reported in published studies on landmark location in 3D surface images.

A review of facial image analysis for delineation of the facial phenotype associated with fetal alcohol syndrome.

The facial anomalies associated with fetal alcohol syndrome (FAS), some of which are also present in individuals with less severe forms of the broader category of fetal alcohol spectrum disorders (FASD), are typically identified with the aid of linear distance measurements taken between facial landmarks. Digital facial imaging methods are increasingly being used in syndrome delineation. Distance measurements derived from stereo-photogrammetry and facial surface imaging have been used to study the FAS facial anomalies. Geometric morphometric methods capture the spatial arrangement between landmarks, providing a statistical platform for comparison of facial shapes, and have been shown to hold promise for characterizing the FAS facial shape. We review the progression in the use of imaging and image analysis methods in studies on the facial phenotype associated with FAS.