Development of a 4‐dimensional model of the human heart using cardiac CT data
Bibliographic record
Abstract
A new and interactive model for presenting human anatomy may allow students, instructors, and clinicians to better understand and integrate knowledge of the body. The aim is to create an anatomically correct three‐dimensional (3‐D) model of a human heart that will be able to rhythmically contract with time, resulting in a four‐dimensional (4‐D) heart model. ECG‐gated cardiac computed tomography (CCT) scans taken at ten time phases (5%‐95%, at 10% intervals) were processed by 3‐D visualization and modeling software (AMIRA). Using manual and automatic segmentation techniques, voxels with gray scale values representing different parts of the heart anatomy were selected and compiled to create the model. A model was constructed for each heartbeat phase, and all phase models were consolidated into one dynamic moving model. The resulting stereoscopic 4‐D model will retain the structure of the original anatomy. An effort will be made to detect any pathological anomalies in the patient using the model. It will feature interactive and explorative capability, eg. zooming in/out, addition and removal of different areas of the heart anatomy, and alteration of model opacity for better viewing of inner compartments. Grant Funding Source Internal
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".