Using AI to Improve Radiographic Fracture Detection

Radiology ◽  
2021 ◽  
Author(s):  
Thomas M. Link ◽  
Valentina Pedoia
Keyword(s):  
2021 ◽  
Vol 7 (7) ◽  
pp. 105
Author(s):  
Guillaume Reichert ◽  
Ali Bellamine ◽  
Matthieu Fontaine ◽  
Beatrice Naipeanu ◽  
Adrien Altar ◽  
...  

The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists.


2020 ◽  
Vol 6 (3) ◽  
pp. 196-199
Author(s):  
Alina Carabello ◽  
Constanze Neupetsch ◽  
Michael Werner ◽  
Christian Rotsch ◽  
Welf-Guntram Drossel ◽  
...  

AbstractTo increase learning success in surgical training, physical simulators are supplemented by measurement technology to generate and record objective feedback and error detection. An opportunity to detect fractures following hip stem implantation early can be measurement of occurring strains on bone surface. These strains can be determined while using strain gauges, digital image correlation (DIC) or photoelasticity. In this research strain gauges and DIC were compared regarding their suitability as strain measurement tools for use in physical simulators. Therefore a testing method was described to replicate the implantation of a hip stem. Testing devices modelled on a realistic prosthesis were pressed into prepared porcine femora in a two-step procedure with a material testing machine. The local strains occurring on bone surface were determined using an optical measurement system for DIC and strain gauges. The initial fractures in the tested femora are located medial-anterior in most cases (73,6%). With increasing indentation depth of the test device, the strains on bone surface increase. Comparing the local strains determined by DIC and strain gauges consistencies in curves are noticeable. Maximal determined strains before fracturing amount to 0,69% with strain gauges and 0,75% with DIC. In the range of the fracture gap, strain gradients are determined by using DIC. However the detected surfaces are of low quality caused by gaps and motion artefacts. The results show strains on bone surfaces for early fracture detection are measurable with strain gauges and DIC. DIC is assessed as less suitable compared to strain gauges. Furthermore strain gauges have greater level of integration and economic efficiency, so they are preferred the use in surgical training simulators.


2008 ◽  
Vol 13-14 ◽  
pp. 41-47 ◽  
Author(s):  
Rhys Pullin ◽  
Mark J. Eaton ◽  
James J. Hensman ◽  
Karen M. Holford ◽  
Keith Worden ◽  
...  

This work forms part of a larger investigation into fracture detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fracture signals and high levels of background noise. An artificial acoustic emission (AE) fracture source was developed and additionally five sources were used to generate differing AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Further to this, artificial fracture signals were recorded in the same component under airworthiness test load conditions. Principal component analysis (PCA) was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial fracture signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.


2022 ◽  
Vol 208 ◽  
pp. 109471
Author(s):  
Fatimah Alzubaidi ◽  
Patrick Makuluni ◽  
Stuart R. Clark ◽  
Jan Erik Lie ◽  
Peyman Mostaghimi ◽  
...  

2003 ◽  
Vol 22 (7) ◽  
pp. 680-683 ◽  
Author(s):  
Xiang-Yang Li ◽  
Yi-Jie Liu ◽  
Enru Liu ◽  
Feng Shen ◽  
Li Qi ◽  
...  

Geophysics ◽  
1988 ◽  
Vol 53 (1) ◽  
pp. 76-84 ◽  
Author(s):  
E. L. Majer ◽  
T. V. McEvilly ◽  
F. S. Eastwood ◽  
L. R. Myer

In a pilot vertical seismic profiling study, P-wave and cross‐polarized S-wave vibrators were used to investigate the potential utility of shear‐wave anisotropy measurements in characterizing a fractured rock mass. The caprock at The Geysers geothermal field was found to exhibit about an 11 percent velocity variation between SH-waves and SV-waves generated by rotating the S-wave vibrator orientation to two orthogonal polarizations for each survey level in the well. The effect is generally consistent with the equivalent anisotropy expected from the known fracture geometry.


2021 ◽  
Author(s):  
Jianfang Dai ◽  
Huiyong Li ◽  
Mingsheng Zhang ◽  
Dehai Qin ◽  
Shuguang Xiao

Sign in / Sign up

Export Citation Format

Share Document