aceDeep Learning-Based Automated Computed Tomography Classification of Posterior Malleolar Fractures for a Standardized Diagnostic Framework
Despite advances in computed tomography (CT)-based classification systems for posterior malleolar fractures (PMFs), diagnostic consistency remains limited because these systems are still highly dependent on the observer's experience. This study aimed to develop a deep learning-based automated model capable of classifying PMFs according to the Haraguchi, Mason-Molloy, and Bartoníček-Rammelt clasification systems using CT scans, and to compare the classification performance of different convolutional neural networ…
Convolutional neural networks classified posterior malleolar fractures on CT with strong discrimination, suggesting automated grading could reduce observer-dependent variability.
The data were retrospectively collected preoperative CT scans, and the labels rely on consensus of three surgeons, so the findings show feasibility rather than demonstrated clinical benefit.
Evidence
- Peer-reviewedJournal of Imaging Informatics in Medicine2026-10-13
How should this claim be treated?
Truvace Impact Record TRV-2026-2019, v1: “Deep Learning-Based Automated Computed Tomography Classification of Posterior Malleolar Fractures for a Standardized Diagnostic Framework.” Truvace, 2026-10-20. /record/TRV-2026-2019 (accessed at citation time). sha256 dee4557bb0a80ac1…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2019 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.