Advancing the next generation of diagnostic device to sustain residuum health of individuals with limb absence
Bibliographic record
Abstract
Summary*: This study outlines the barriers and facilitators to the clinical and commercial developments of the In-Vivo Kinetic 4.0 diagnostic tool; capable of rendering morphometry, deformation, and stresses of the residuum tissues of an individual with limb absence under real-life weightbearing conditions. Introduction/Basics*: The well-being of individuals with limb absence depends on their residuum health, particularly the synergy between intrinsic and extrinsic determinants, being indirectly and directly associated with loading, respectively.[1] Clinicians struggle to prevent residuum neuromusculoskeletal dysfunctions and sustain successful coupling with a prosthesis. The efficacy and safety of their interventions are primarily assessed using questionnaires, physical examinations, medical tests, and static images. Unfortunately, interactions between determinants of residuum health are difficult to cross correlate and quantify under real-life conditions. Establishing causal relationships between the bespoke interventions and residuum health is challenging.[2] This study outlines the barriers and facilitators to the developments of the In-Vivo Kinetic System 4.0, diagnostic tool capable of rendering morphometry, deformation and stresses of the residuum’s tissues under real-life weightbearing conditions.[3] Material/methods*: Developments of the integrated, wearable, and non-invasive In-Vivo Kinetic System 4.0 (Figure 1) involved iterative research-evaluation cycles including: • Step 1 (Plan): Outline the roadmap for development and testing of device, involving 40+ experiments. • Step 2 (Do): Create physical and digital phantoms of a residuum limb, and design the alpha prototype which integrated measurement of the mechanical constrains applied on the residuum using iPecsLab and morphometry of the residual limb’s tissues using our novel Dynamic Anatomical Ultrasonography technique.[4, 5] • Step 3 (Study): Trial the alpha prototype to establish its proof of utility, efficacy, and safety. • Step 4 (Act): Prepare randomized clinical trials and investigating commercialization pathways. The barriers and facilitators to development, commercialization, and implementation of the device presented here were essentially identified during Step 1 through collective consensus-based reflection incorporating stakeholders. Results: The primary facilitator for development of the In-Vivo Kinetic System 4.0 was the urgent need for a better understanding of how to optimize residuum health when fitted with a conventional, endo / exo- skeletal implant alone or in combination with emerging bionics solutions. A key barrier to the development of the device was the lack of basic knowledge of the mechanical properties of human skin, adipose, and muscle tissues, as well as bone/implant coupling. The discrepancy between technology readiness levels of loading, Dynamic Anatomical Ultrasonography, and modelling elements were estimated to be at 7, 3, and 4, respectively. This complexify the integration of all part within the unique technological platform. Commercialization avenues of the device met limited interest from MedTech investors, because of their perception that the market is too small, and ambiguity around reimbursement pathways that is common to many instrumented medical devices used by patients autonomously. The In-Vivo Kinetic System 4.0 has the potential to productively disrupt the current model of rehabilitative and prosthetic care used for current and emerging bionic solutions, provided all its technological elements can be integrated and deemed acceptable to clinicians and end-users. Discussion/Conclusion for clinical practice*: The In-vivo Kinetic System 4.0+ can further facilitate assessment of stress and strain on all residuum tissues and, consequently, improve our understanding of the potential associations between residuum loading and neuromusculoskeletal disfunctions. The diagnostic device can be used for immediate gait retraining and improvements in computer controlled prosthetic knee functions (e.g., adjustments to residuum loading, stresses and strains). Furthermore, differential diagnoses could potentially improve cost-effectiveness of surgical, medical and prosthetic care interventions by reducing over-prescription and the lifetime financial burden on health care system.[6, 7] Altogether, In-vivo Kinetic System 4.0+ will create opportunities to extend precision medicine and guide personalized clinical treatments including design of costly sockets, selection of expensive components and rationalization of prosthetic fittings for conventional treatment and fast growing bionics bone-anchored prostheses. Acknowlegdements: This study was partially funded by 2019 US DoD RESTORE Award W81XWH2110215-DM190659, 2021 Bionics Queensland Challenge Major Prize – Mobility, and 2021 ANMS-ISPO Research Grant. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by these funding bodies (i.e., FY19 Defense Medical Research and Development Program Bionics Queensland, ANMS ISPO). Literature references: 1. Frossard, L., S. Conforto, and O.C. Aszmann, Editorial: Bionics limb prostheses: Advances in clinical and prosthetic care. Front Rehabil Sci, 2022. 3: p. 950481. 2. Frossard, L., et al., Next-generation devices to diagnose residuum health of individuals suffering from limb loss: A narrative review of trends, opportunities, and challenges. J Sci Med Sport, 2023. 26 Suppl 1: p. S22-S29. 3. Frossard, L. and D. Lloyd, The future of bionic limbs. Research Features, 2021. 134: p. 54-57. 4. Langton, C.M., et al., A 3D-printed phantom twin and multi-transducer holder for dynamic anatomical ultrasonography of the lower limb. Journal of 3D Printing in Medicine, 2023. 7(2): p. 3DP009. 5. Frossard, L., et al., Load applied on osseointegrated implant by transfemoral bone-anchored prostheses fitted with state-of-the-art prosthetic components. Clin Biomech (Bristol, Avon), 2021. 89: p. 105457. 6. Frossard, L., Trends and opportunities in health economic evaluations of prosthetic care innovations. Canadian Prosthetics & Orthotics Journal, 2021. 4(2): p. 1-17. 7. Frossard, L., A preliminary cost-utility analysis of the prosthetic care innovations: basic framework. Canadian Prosthetics & Orthotics Journal, 2021. 4(2): p. 1-13.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".