Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network.

Abstract:

OBJECTIVE:To compare performances in diagnosing intertrochanteric hip fractures from proximal femoral radiographs between a convolutional neural network and orthopedic surgeons. MATERIALS AND METHODS:In total, 1773 patients were enrolled in this study. Hip plain radiographs from these patients were cropped to display only proximal fractured and non-fractured femurs. Images showing pseudarthrosis after femoral neck fracture and those showing artificial objects were excluded. This yielded a total of 3346 hip images (1773 fractured and 1573 non-fractured hip images) that were used to compare performances between the convolutional neural network and five orthopedic surgeons. RESULTS:The convolutional neural network and orthopedic surgeons had accuracies of 95.5% (95% CI = 93.1-97.6) and 92.2% (95% CI = 89.2-94.9), sensitivities of 93.9% (95% CI = 90.1-97.1) and 88.3% (95% CI = 83.3-92.8), and specificities of 97.4% (95% CI = 94.5-99.4) and 96.8% (95% CI = 95.1-98.4), respectively. CONCLUSIONS:The performance of the convolutional neural network exceeded that of orthopedic surgeons in detecting intertrochanteric hip fractures from proximal femoral radiographs under limited conditions. The convolutional neural network has a significant potential to be a useful tool for screening for fractures on plain radiographs, especially in the emergency room, where orthopedic surgeons are not readily available.

journal_name

Skeletal Radiol

journal_title

Skeletal radiology

authors

Urakawa T,Tanaka Y,Goto S,Matsuzawa H,Watanabe K,Endo N

doi

10.1007/s00256-018-3016-3

subject

Has Abstract

pub_date

2019-02-01 00:00:00

pages

239-244

issue

2

eissn

0364-2348

issn

1432-2161

pii

10.1007/s00256-018-3016-3

journal_volume

48

pub_type

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