Application of statistical shape modeling to the human hip joint: a scoping review
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review was to identify all examples of the application of statistical shape models to the human hip joint, with a focus on applications, population, methodology, and validation. INTRODUCTION: Clinical radiographs are the most common imaging tool for management of hip conditions, but it is unclear whether radiographs can adequately diagnose or predict outcomes of 3D deformity. Statistical shape modeling, a method of describing the variation of a population of shapes using a small number of variables, has been identified as a useful tool to associate 2D images with 3D anatomy. This could allow clinicians and researchers to validate clinical radiographic measures of hip deformity, develop new ones, or predict 3D morphology directly from radiographs. In identifying all previous examples of statistical shape modeling applied to the human hip joint, this review determined the prevalence, strengths, and weaknesses, and identified gaps in the literature. INCLUSION CRITERIA: Participants included any human population. The concept included development or application of statistical shape models based on discrete landmarks and principal component analysis. The context included sources that exclusively modeled the hip joint. Only peer-reviewed original research journal articles were eligible for inclusion. METHODS: We searched MEDLINE, Embase, Cochrane CENTRAL, IEEE Xplore, Web of Science Core Collection, OCLC PapersFirst, OCLC Proceedings, Networked Digital Library of Theses and Dissertations, ProQuest Dissertations and Theses Global, and Google Scholar for sources published in English between 1992 and 2021. Two reviewers screened sources against the inclusion criteria independently and in duplicate. Data were extracted by 2 reviewers using a REDCap form designed to answer the review study questions, and are presented in narrative, tabular, and graphical form. RESULTS: A total of 104 sources were considered eligible based on the inclusion criteria. From these, 122 unique statistical shape models of the human hip were identified based on 86 unique training populations. Models were most often applied as one-off research tools to describe shape in certain populations or to predict outcomes. The demographics of training populations were skewed toward older patients in high-income countries. A mean age between 60 and 79 years was reported in 29 training populations (34%), more than reported in all other age groups combined, and 73 training populations (85%) were reported or inferred to be from Europe and the Americas. Only 4 studies created models in a pediatric population, although 15 articles considered shape variation over time in some way. There were approximately equal numbers of 2D and 3D models. A variety of methods for labeling the training set was observed. Most articles presented some form of validation such as reporting a model's compactness (n = 71), but in-depth validation was rare. CONCLUSIONS: Despite the high volume of literature concerning statistical shape models of the human hip, there remains a need for further research in key areas. We identified the lack of models in pediatric populations and low- and middle-income countries as a notable limitation to be addressed in future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".