Development of a web-based application for initial hand evaluation and 3D scanning: A pilot feasibility and validity study
Bibliographic record
Abstract
BACKGROUND: The rapid evolution of digital health technologies has created new opportunities for remote evaluation and management of upper extremity conditions. However, few tools offer a comprehensive, validated, and user-friendly platform for remote hand assessment, including 3D scanning capabilities. PURPOSE: To develop and validate a novel web-based application ("HAND SCANS") for remote hand evaluation and 3D scanning and to assess its feasibility, accuracy, and user satisfaction among clinicians and patients. STUDY DESIGN: Prospective validation and feasibility study. METHODS: A web-based application was designed to collect patient-reported outcomes and guide users through image capture for 3D hand scanning using smartphone cameras. The tool's ability to measure range of motion, sensory function, pain, and grip strength was compared with standard clinical instruments. The accuracy of 3D scans was validated against conventional photogrammetry. Patient and clinician feedback was gathered to evaluate feasibility and usability. RESULTS: The application strongly agreed with clinical gold standards for measuring range of motion, sensory testing, and pain levels. 3D hand scans obtained using smartphones showed high dimensional accuracy compared to standard photogrammetry. Participants reported high satisfaction with the application, citing ease of use and potential for remote care. Areas for improvement included image quality and user interface. CONCLUSIONS: The HAND SCANS application is a valid and feasible tool for remote hand evaluation and 3D scanning. Its integration into telehealth models may expand access to specialized hand assessments, especially in underserved or remote settings. Further development is warranted to enhance image quality and user experience.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".