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Enregistrement W4399301601 · doi:10.54985/peeref.2406p3045050

Advancing the next generation of diagnostic device to sustain residuum health of individuals with limb absence

2024· article· en· W4399301601 sur OpenAlexaboutno aff
Laurent Frossard, Christian M. Langton, S. Feih, David G. Lloyd

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEngineering
ThématiqueMuscle activation and electromyography studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésResiduumPhysical medicine and rehabilitationData scienceComputer scienceMedicineEnvironmental science

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,026

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0080,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,262
Écart entre enseignants0,238 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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