Performance of the Winning Algorithms of the RSNA 2022 Cervical Spine Fracture Detection Challenge.

Purpose To evaluate and report the performance of the winning algorithms of the Radiological Society of North America Cervical Spine Fracture AI Challenge. Materials and Methods The competition was open to the public on Kaggle from July 28 to October 27, 2022. A sample of 3112 CT scans with and without cervical spine fractures (CSFx) were assembled from multiple sites (12 institutions across six continents) and prepared for the competition. The test set had 1093 scans (private test set: n = 789; mean age, 53.40 years ± 22.86 [SD]; 509 males; public test set: n = 304; mean age, 52.51 years ± 20.73; 189 males) and 847 fractures. The eight top-performing artificial intelligence (AI) algorithms were retrospectively evaluated, and the area under the receiver operating characteristic curve (AUC) value, F1 score, sensitivity, and specificity were calculated. Results A total of 1108 contestants composing 883 teams worldwide participated in the competition. The top eight AI models showed high performance, with a mean AUC value of 0.96 (95% CI: 0.95, 0.96), mean F1 score of 90% (95% CI: 90%, 91%), mean sensitivity of 88% (95% Cl: 86%, 90%), and mean specificity of 94% (95% CI: 93%, 96%). The highest values reported for previous models were an AUC of 0.85, F1 score of 81%, sensitivity of 76%, and specificity of 97%. Conclusion The competition successfully facilitated the development of AI models that could detect and localize CSFx on CT scans with high performance outcomes, which appear to exceed known values of previously reported models. Further study is needed to evaluate the generalizability of these models in a clinical environment. Keywords: Cervical Spine, Fracture Detection, Machine Learning, Artificial Intelligence Algorithms, CT, Head/Neck Supplemental material is available for this article. © RSNA, 2024.

greylock: A Python Package for Measuring The Composition of Complex Datasets.

Machine-learning datasets are typically characterized by measuring their size and class balance. However, there exists a richer and potentially more useful set of measures, termed diversity measures, that incorporate elements' frequencies and between-element similarities. Although these have been available in the R and Julia programming languages for other applications, they have not been as readily available in Python, which is widely used for machine learning, and are not easily applied to machine-learning-sized datasets without special coding considerations. To address these issues, we developed greylock, a Python package that calculates diversity measures and is tailored to large datasets. greylock can calculate any of the frequency-sensitive measures of Hill's D-number framework, and going beyond Hill, their similarity-sensitive counterparts (Greylock is a mountain). greylock also outputs measures that compare datasets (beta diversities). We first briefly review the D-number framework, illustrating how it incorporates elements' frequencies and between-element similarities. We then describe greylock's key features and usage. We end with several examples - immunomics, metagenomics, computational pathology, and medical imaging - illustrating greylock's applicability across a range of dataset types and fields.