RibFractureSys: A gem in the face of acute rib fracture diagnoses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rib fracture patients, common in trauma wards, have different mortality rates and comorbidities depending on how many and which ribs are fractured. This knowledge is therefore paramount to make accurate prognoses and prioritize patient care. However, tracking 24 ribs over upwards 200+ frames in a patient’s scan is time-consuming and error-prone for radiologists, especially depending on their experience. We propose an automated, modular, three-stage solution to assist radiologists. Using 9 fully annotated patient scans, we trained a multi-class U-Net to segment rib lesions and common anatomical clutter. To recognize rib fractures and mitigate false positives, we fine-tuned a ResNet-based model using 5698 false positives, 2037 acute fractures, 4786 healed fractures, and 14,904 unfractured rib lesions. Using almost 200 patient cases, we developed a highly task-customized multi-object rib lesion tracker to determine which lesions in a frame belong to which of the 12 ribs on either side; bounding box intersection over union- and centroid-based tracking, a line-crossing methodology, and various heuristics were utilized. Our system accepts an axial CT scan and processes, labels, and color-codes the scan. Over an internal validation dataset of 1000 acute rib fracture and 1000 control patients, our system, assessed by a 3-year radiologist resident, achieved 96.1% and 97.3% correct fracture classification accuracy for rib fracture and control patients, respectively. However, 18.0% and 20.8% of these patients, respectively, had incorrect rib labeling. Percentages remained consistent across sex and age demographics. Labeling issues include anatomical clutter being mislabeled as ribs and ribs going unlabeled. • Developed a modular 3-stage system to classify rib fractures with per-rib granularity. • U-Net to segment rib lesions & ResNet to classify lesions as unfractured, old, acute. • Rib lesions tracked across frames & ribs enumerated via custom line-crossing method. • 75% & 79% labeling+fracture accuracy across 1000 acute & 1000 control axial CT scans. • Patient scan processed & marked output video generated in 198/31 s (CPU/GPU).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it