# Vertebra Level Fracture Recognition on RSNA Dataset Source: [Vertebra Level Fracture Recognition on RSNA Dataset]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:31:32.480Z Evaluation of model performance for recognizing vertebra level fractures using the RSNA Dataset, reporting metrics such as Accuracy, F1-Score, and mAP. A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Vertebra Level Fracture Recognition | | Dataset (including any stated split) | RSNA Dataset | | Metric | Accuracy | | Unit | PERCENT | | Direction | Higher is better | | Results loaded | 8 of 8 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [Accuracy](): Higher is better; unit: PERCENT. - [mAP (ROI)](): Higher is better; unit: SCALAR. - [Fracture Accuracy](): Higher is better; unit: PERCENT. - [F1-Score](): Higher is better; unit: PERCENT. - [mAP](): Higher is better; unit: SCALAR. ## Recorded results | Method | Accuracy | Result date | Report paper | | --- | --- | --- | --- | | Rasul et al. | 0.9975 | Not recorded | 6971af3ae25ad2fa9d1edc13 | | Yaseen et al. | 0.978 | Not recorded | 6971af3ae25ad2fa9d1edc13 | | Kim et al. | 0.949 | Not recorded | 6971af3ae25ad2fa9d1edc13 | | Proposed Method (Score Fusion) | 0.9451 | Not recorded | 6971af3ae25ad2fa9d1edc13 | | RSNA First Place Winner | 0.9405 | Not recorded | 6971af3ae25ad2fa9d1edc13 | | Proposed Method | 0.9354 | Not recorded | 6971af3ae25ad2fa9d1edc13 | ### Rasul et al. Accuracy: 0.9975. Result ID: 69979d2f5698665ec4f3167a. [Reporting paper]() Recorded evaluation configuration: {"Dataset Split":"Subset (4200 images)","Test Set Size":"400 Images","Major Drawback":"Dataset was balanced before splitting."}. This record does not assert independent reproduction. ### Yaseen et al. Accuracy: 0.978. Result ID: 69979d2f5698665ec4f3167c. [Reporting paper]() Recorded evaluation configuration: {"Dataset Split":"Subset (14434 slices)","Test Set Size":"2887 slices","Major Drawback":"Dataset was balanced before splitting."}. This record does not assert independent reproduction. ### Kim et al. Accuracy: 0.949. Result ID: 69979d2f5698665ec4f3167e. [Reporting paper]() Recorded evaluation configuration: {"Dataset Split":"Full","Test Set Size":"404 Patients"}. This record does not assert independent reproduction. ### Proposed Method (Score Fusion) Accuracy: 0.9451. Result ID: 69979d2f5698665ec4f31681. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Test Set Size":"2019 Patients"}. This record does not assert independent reproduction. ### RSNA First Place Winner Accuracy: 0.9405. Result ID: 69979d2f5698665ec4f3167f. [Reporting paper]() Recorded evaluation configuration: {"Dataset Split":"Full","Test Set Size":"2019 Patients","Cross Validation":"true"}. This record does not assert independent reproduction. ### Proposed Method Accuracy: 0.9354. Result ID: 69979d2f5698665ec4f31680. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Test Set Size":"2019 Patients","Cross Validation":"true"}. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.