DOI: 10.1101/506154Dec 26, 2018Paper

CNN-based radiographic acute tibial fracture detection in the setting of open growth plates

BioRxiv : the Preprint Server for Biology
Zbigniew A StarosolskiAnanth V Annapragada


Pediatric tibial fractures are commonly diagnosed by radiographs and constitute one of the common tasks performed by pediatric radiologists. Here, we assess the performance of a convolutional neural network for the detection of acute tibial fractures trained with a limited number of cases in skeletally immature patients. This retrospective study was performed on radiology reports manually classified as normal or tibial fracture. Classified images of orthopaedic implants, casting, and images including other pathology were excluded. The remaining cases constituted 516 studies containing 2118 radiographs. These radiographs were truncated to include a limited investigated field of view which included the distal third of the leg, inclusive of the distal physis. After exclusions, the culled dataset was randomly divided into a training set containing 784 radiographs, a validation set containing 98 radiographs, and a test set 98 radiographs. We used a modified transfer learning approach based on the Xception architecture with additional fully convoluted reasoning and drop-out layers. Of 49 distal Tibial fractures, two were misdiagnosed as normal. Of 49 normal exams, none were misdiagnosed. This led to model accuracy of 97.9%, sensitivi...Continue Reading

Related Concepts

Retrospective Studies
Tibial Fractures
Psychological Transfer
Medial Malleolus

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