3D printing prostheses using additive manufacturing and regenerative engineering
Bibliographic record
Abstract
Additive manufacturing is a manufacturing process utilized to make prosthetics. It offers an affordable means to create custom-made prostheses. Yet, the number of studies exploring the domain of 3D printing and bioprinting of prosthetics remains limited. In this paper, we provide a comprehensive review of the current research in additive manufacturing to produce prosthetic limbs, bionic eyes, temporomandibular joints (TMJ), cardiac valves, and skin. We concluded that the research gap lies in the long-term, periodic assessment of 3D-printed prosthetic limbs for durability. 3D-printed prosthetic sockets’ reinforcement materials are a particularly underexplored topic and the main shortcoming of 3D-printed prostheses is their failure under shear stresses. In bioprinting, research must focus on developing tissue-specific bioinks and hydrogels to overcome their existing scarcity. Bioprinting techniques like extrusion, inkjet, and laser-assisted bioprinting subject bioinks to conditions of high temperature and pressure. Bioinks must withstand the printing process and simultaneously retain their rheological properties and cell viability. Moreover, some bioprinting techniques are still quite expensive. However, 3D printing and bioprinting offer the prospect of customization to the patient’s unique anatomy, increasing the wear time of prostheses and offering unique benefits like improved tissue regeneration and adaptability to changing patient anatomy. 3D printing specifically reduces costs, and production time and improves accessibility in war-stricken areas with more amputees.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".