Dr. J. Herman Kan Profile

Dr. J. Herman Kan

at Texas Childrens Hospital

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Proceedings Article | 16 March 2020 Paper

CNN-based detection of distal tibial fractures in radiographic images in the setting of open growth plates

Zbigniew Starosolski, J. Herman Kan, Ananth Annapragada

Proceedings Volume 11314, 113143M (2020) https://doi.org/10.1117/12.2549297

KEYWORDS: Radiography, Data modeling, Performance modeling, Radiology, Convolutional neural networks, Pathology, Medical imaging, Bone, Network architectures, Artificial intelligence

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The goal of this study was to assess the performance of a deep convolutional neural network trained with a limited number of cases for the detection of distal tibial fractures in children.We identified 516 ankle and leg radiographic exams in children (6.4±4.4 (mean±s.d) years) containing 2118 individual images. Radiographs with implants, casts, advanced healing, and including other pathology such as intra-osseous bone lesions were excluded. After these exclusions, 490 positive distal tibial fracture radiographs were identified and a matching number of normal radiographs ware selected, creating a dataset of 980 radiographs. These were sequentially partitioned, in 10 permutations, into a training set (784 radiographs), validation set (98 radiographs) and a test set (98 radiographs), with equal numbers of fracture and normal radiographs in each set. A modified transfer learning network based on the Xception-V3 architecture with additional fully convoluted reasoning layers was trained against each of the subsets. The best performing trained network successfully recognized 47 of 49 fractures and 47 of 49 normal exams (95.9% accuracy). The best three performing networks were all very similar, with accuracy of 95.6 ± 0.6%. In no instances were normal physes or normal developmental epiphyseal or medial malleolus fragmentation misclassified as a fracture. Using a pre-trained deep convolutional neural network adapted to identifying or excluding distal tibial fractures in children using only a small number of radiographs is feasible and highly accurate without the need for the large training sets typically needed for network training.

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