English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We demonstrate the feasibility of a fully automatic computer-aided diagnosis(CAD) tool, based on deep learning, that localizes and classifies proximalfemur fractures on X-ray images according to the AO classification. Theproposed framework aims to improve patient treatment planning and providesupport for the training of trauma surgeon residents. A database of 1347clinical radiographic studies was collected. Radiologists and trauma surgeonsannotated all fractures with bounding boxes, and provided a classificationaccording to the AO standard. The proposed CAD tool for the classification ofradiographs into types "A", "B" and "not-fractured", reaches a F1-score of 87%and AUC of 0.95, when classifying fractures versus not-fractured cases itimproves up to 94% and 0.98. Prior localization of the fracture results in animprovement with respect to full image classification. 100% of the predictedcenters of the region of interest are contained in the manually providedbounding boxes. The system retrieves on average 9 relevant images (from thesame class) out of 10 cases. Our CAD scheme localizes, detects and furtherclassifies proximal femur fractures achieving results comparable toexpert-level and state-of-the-art performance. Our auxiliary localization modelwas highly accurate predicting the region of interest in the radiograph. Wefurther investigated several strategies of verification for its adoption intothe daily clinical routine. A sensitivity analysis of the size of the ROI andimage retrieval as a clinical use case were presented.

Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning