Analysis of abnormal CT scans using medical imaging software ‘Invivo5.4 Medical Design Suite’ by Anatomage
Bibliographic record
Abstract
Medical imaging techniques such as computerized tomography (CT) scans have a wide variety of uses, including forensic applications. There are limited softwares available that are advanced enough to give medical and forensic professionals the ability to manipulate CT images to where they can be used for demonstration and analysis. This study is the analysis of two CT scans through the newest version of the three-dimensional medical imaging software ‘Invivo5.4 Medical Design Suite’ by Anatomage. Abnormal scans of the abdominal and thoracic regions were compared to reference images of the same area. Multiple large air pockets were found in the abdominal area of one individual, as well as a mass on the right kidney. The other individual displayed indications that contrast dye was injected to better visualize the heart and blood vessels in the CT scan image. Easy-to-use tools available within this software allow for professionals to give clear demonstration and learning opportunities to students and other individuals in the field of forensic medicine and pathology. Other applications include the diagnosis and analysis of disease, injury, trauma, and cause of death and can even be used in jury demonstration.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".