Creating Neonatal Ultrasound Simulations using 3D printing
Bibliographic record
Abstract
Medical imaging techniques are an important tool for physicians and technicians to use when diagnosing and treating illness. CT, MRI and Ultrasound are commonplace in most medical facilities due to their ease of use and far reaching applications. Ultrasound, in particular, is the ideal modality for diagnosing disease in children and infants due to several factors. For example, Ultrasound does not introduce ionizing radiation into the patient in the way that CT does, which is extremely important for developing children. Due to the considerations associated with neonatal ultrasound, it is beneficial for physicians and students to have access to training simulations that familiarize them with up-to-date ultrasound techniques. Such a simulation would ideally be low cost to produce and operate, easily communicable within the medical community, adaptable in order to simulate various conditions, and would make use of the same tools that are used when performing a live ultrasound. Currently, there exists no neonatal ultrasound simulation. This project proposes using 3D printing manufacturing to create a cheap, accurate model of a neonate brain to be used as a simulation tool. 3D printing presents a novel opportunity to create a simulation that satisfies all of the previously listed ideal characteristics. This project discusses the practicality, approach and challenges associated with using 3D printing to create a neonatal ultrasound simulation. * Indicates faculty mentor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".