scholarly journals Coexisting Gout and Chondrocalcinosis on Hand Radiograph

2018 ◽  
Vol 45 (5) ◽  
pp. 723-724
Author(s):  
FABRICE COUTIER ◽  
MAXIME SONDAG ◽  
DANIEL WENDLING
Keyword(s):  
Hand Radiograph

Creation and Validation of a Shorthand Magnetic Resonance Imaging Bone Age Assessment Tool of the Knee as an Alternative Skeletal Maturity Assessment

2021 ◽  
pp. 036354652110329
Author(s):  
Cary S. Politzer ◽  
James D. Bomar ◽  
Hakan C. Pehlivan ◽  
Pradyumna Gurusamy ◽  
Eric W. Edmonds ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Skeletal Maturity ◽  
Bone Age ◽  
Mri Scan ◽  
Perfect Agreement ◽  
Age Assessment ◽  
Left Hand ◽  
Knee Mri ◽  
Bone Age Assessment ◽  
Hand Radiograph

Background: In managing pediatric knee conditions, an accurate bone age assessment is often critical for diagnostic, prognostic, and treatment purposes. Historically, the Greulich and Pyle atlas (hand atlas) has been the gold standard bone age assessment tool. In 2013, a shorthand bone age assessment tool based on this atlas (hand shorthand) was devised as a simpler and more efficient alternative. Recently, a knee magnetic resonance imaging (MRI) bone age atlas (MRI atlas) was created to circumvent the need for a left-hand radiograph. Purpose: To create a shorthand version of the knee MRI atlas. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A shorthand bone age assessment method was created utilizing the previously published MRI atlas, which utilizes several criteria that are visualized across a series of images. The MRI shorthand draws on characteristic criteria for each age that are best observed on a single MRI scan. For validation, we performed a retrospective assessment of skeletally immature patients. One reader performed the bone age assessment using the MRI atlas and the MRI shorthand on 200 patients. Then, 4 readers performed the bone age assessment with the hand atlas, hand shorthand, MRI atlas, and MRI shorthand on a subset of 22 patients in a blinded fashion. All 22 patients had a knee MRI scan and a left-hand radiograph within 4 weeks of each other. Interobserver and intraobserver reliability, as well as variability among observers, were evaluated. Results: A total of 200 patients with a mean age of 13.5 years (range, 9.08-17.98 years) were included in this study. Also, 22 patients with a mean age of 13.3 years (range, 9.0-15.6 years) had a knee MRI scan and a left-hand radiograph within 4 weeks. The intraobserver and interobserver reliability of all 4 assessment tools were acceptable (intraclass correlation coefficient [ICC] ≥ 0.8; P < .001). When comparing the MRI shorthand with the MRI atlas, there was excellent agreement (ICC = 0.989), whereas the hand shorthand compared with the hand atlas had good agreement (ICC = 0.765). The MRI shorthand also had perfect agreement in 50% of readings among all 4 readers, and 95% of readings had agreement within 1 year, whereas the hand shorthand had perfect agreement in 32% of readings and 77% agreement within 1 year. Conclusion: The MRI shorthand is a simple and efficient means of assessing the skeletal maturity of adolescent patients with a knee MRI scan. This bone age assessment technique had interobserver and intraobserver reliability equivalent to or better than the standard method of utilizing a left-hand radiograph.

Reference study on the time frame for ossification of the distal radius and ulnar epiphyses on the hand radiograph

2009 ◽  
Vol 191 (1-3) ◽  
pp. 15-18 ◽  
Author(s):  
U. Baumann ◽  
R. Schulz ◽  
W. Reisinger ◽  
A. Heinecke ◽  
A. Schmeling ◽  
...  
Keyword(s):  
Distal Radius ◽  
Time Frame ◽  
Hand Radiograph

Can a hand radiograph indicate a special diagnosis in a child with chronic kidney disease? Questions

2017 ◽  
Vol 33 (5) ◽  
pp. 799-800
Author(s):  
Eren Soyaltın ◽  
Belde Kasap-Demir ◽  
Caner Alparslan ◽  
Seçil Arslansoyu-Çamlar ◽  
Elif Perihan Öncel ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Hand Radiograph

Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph

2019 ◽  
Vol 23 (5) ◽  
pp. 2030-2038 ◽  
Author(s):  
Xuhua Ren ◽  
Tingting Li ◽  
Xiujun Yang ◽  
Shuai Wang ◽  
Sahar Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Bone Age ◽  
Age Assessment ◽  
Bone Age Assessment ◽  
Hand Radiograph

Hand radiograph image segmentation using a coarse-to-fine strategy

2007 ◽  
Vol 40 (11) ◽  
pp. 2994-3004 ◽  
Author(s):  
Chin-Chuan Han ◽  
Chang-Hsing Lee ◽  
Wen-Li Peng
Keyword(s):  
Image Segmentation ◽  
Radiograph Image ◽  
Coarse To Fine ◽  
Hand Radiograph

scholarly journals An Automated System for Identification of Skeletal Maturity using Convolutional Neural Networks Based Mechanism

2019 ◽  
Vol 8 (11) ◽  
pp. 2221-2227
Keyword(s):  
Skeletal Maturity ◽  
Bone Age ◽  
Recognition System ◽  
Training Sample ◽  
Diagnostic Methods ◽  
Automated System ◽  
Time Saving ◽  
Left Hand ◽  
Age Prediction ◽  
Hand Radiograph

This paper puts forward a proposition of automated skeletal recognition system that takes an input of left hand-wrist-fingers radiograph and give us an output of the bone age prediction. This system is more reliable, if is successful and time-saving than those laborious, fallible and time-consuming manual diagnostic methods. Here, a Faster R-CNN takes the input of left-hand radiograph giving the detected DRU region from left-hand radiograph. This output is given as an input to a properly trained CNN model. The experiment section provides us with the details regarding the experiments conducted on 1101 radiographs of left hand and wrist datasets and accuracy of model when different optimization algorithms and training sample amounts were utilized. Finally, this proposed system achieves 92% (radius) and 90% (ulna) classification accuracy after the parameter optimization.

Sign in / Sign up

Export Citation Format

Share Document