3D Rapid Prototyping for Otolaryngology—Head and Neck Surgery: Applications in Image-Guidance, Surgical Simulation and Patient-Specific Modeling
Bibliographic record
Abstract
The aim of this study was to demonstrate the role of advanced fabrication technology across a broad spectrum of head and neck surgical procedures, including applications in endoscopic sinus surgery, skull base surgery, and maxillofacial reconstruction. The initial case studies demonstrated three applications of rapid prototyping technology are in head and neck surgery: i) a mono-material paranasal sinus phantom for endoscopy training ii) a multi-material skull base simulator and iii) 3D patient-specific mandible templates. Digital processing of these phantoms is based on real patient or cadaveric 3D images such as CT or MRI data. Three endoscopic sinus surgeons examined the realism of the endoscopist training phantom. One experienced endoscopic skull base surgeon conducted advanced sinus procedures on the high-fidelity multi-material skull base simulator. Ten patients participated in a prospective clinical study examining patient-specific modeling for mandibular reconstructive surgery. Qualitative feedback to assess the realism of the endoscopy training phantom and high-fidelity multi-material phantom was acquired. Conformance comparisons using assessments from the blinded reconstructive surgeons measured the geometric performance between intra-operative and pre-operative reconstruction mandible plates. Both the endoscopy training phantom and the high-fidelity multi-material phantom received positive feedback on the realistic structure of the phantom models. Results suggested further improvement on the soft tissue structure of the phantom models is necessary. In the patient-specific mandible template study, the pre-operative plates were judged by two blinded surgeons as providing optimal conformance in 7 out of 10 cases. No statistical differences were found in plate fabrication time and conformance, with pre-operative plating providing the advantage of reducing time spent in the operation room. The applicability of common model design and fabrication techniques across a variety of otolaryngological sub-specialties suggests an emerging role for rapid prototyping technology in surgical education, procedure simulation, and clinical practice.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".