A deep-learning model for identifying fresh vertebral compression fractures on digital radiography

Chen, Weijuan; Liu, Xi; Li, Kunhua; Luo, Yin; Bai, Shanwei; Wu, Jiangfen; Chen, Weidao; Dong, Mengxing; Guo, Dajing

doi:10.1007/s00330-021-08247-4

Cited by 39 publications

(32 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Improving the recognition of compression fractures is also the direction of the current solution. Now, deep learning can identify vertebral compression fractures on radiography and can help distinguish between fresh and old compression fractures, solving the challenge of identifying fresh compression fractures on radiography ( 39 ). Similarly, on MRI images, deep learning has also reached the level of specialists in identifying fresh compressed bones ( 40 ).…”

Section: Discussionmentioning

confidence: 99%

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Feng

Zhou

Wang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

BackgroundAs a research hotspot, deep learning has been continuously combined with various research fields in medicine. Recently, there is a growing amount of deep learning-based researches in orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends.MethodsWe screened global publication on deep learning applications in orthopedics by accessing the Web of Science Core Collection. The articles and reviews were collected without language and time restrictions. Citespace was applied to conduct the bibliometric analysis of the publications.ResultsA total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopedics has great prospects for development based on the annual publications. The most prolific country is the USA, followed by China. University of California San Francisco, and Skeletal Radiology are the most prolific institution and journal, respectively. LeCun Y is the most frequently cited author, and Nature has the highest impact factor in the cited journals. The current hot keywords are convolutional neural network, classification, segmentation, diagnosis, image, fracture, and osteoarthritis. The burst keywords are risk factor, identification, localization, and surgery. The timeline viewer showed two recent research directions for bone tumors and osteoporosis.ConclusionPublications on deep learning applications in orthopedics have increased in recent years, with the USA being the most prolific. The current research mainly focused on classifying, diagnosing and risk predicting in osteoarthritis and fractures from medical images. Future research directions may put emphasis on reducing intraoperative risk, predicting the occurrence of postoperative complications, screening for osteoporosis, and identification and classification of bone tumors from conventional imaging.

show abstract

Section: Discussionmentioning

confidence: 99%

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Feng

Zhou

Wang

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Murata et al ( 51 ) reported AI enabled detection of VFs on plain spinal radiograph. With MRI as the reference standard, Chen et al ( 52 ) reported identifying fresh CVFs from spine radiograph. Chen et al ( 53 ) reported the application of a deep learning algorithm to detect and visualize VFs on plain frontal abdominal radiographs.…”

Section: Discussionmentioning

confidence: 99%

“…In recent a few years, a number of authors reported AI (artificial Intelligence) enabled analysis and detection of VF (vertebral fracture) of spine medical images which included spine radiograph (46)(47)(48)(49)(50)(51)(52)(53) appears to be that they did not apply different thresholds (or different criteria) for male and female patients. They reported that, of the patients with VFs, 43.7% were male; while this may represent an overestimation of VFs in male patients (67).…”

Section: Discussionmentioning

confidence: 99%

A software program for automated compressive vertebral fracture detection on elderly women’s lateral chest radiograph: Ofeye 1.0

Xiao¹,

Zhu²,

Du³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background Because osteoporotic vertebral fracture (OVF) on chest radiographs is commonly missed in radiological reports, we aimed to develop a software program which offers automated detection of compressive vertebral fracture (CVF) on lateral chest radiographs, and which emphasizes CVF detection specificity with a low false positivity rate. Methods For model training, we retrieved 3,991 spine radiograph cases and 1,979 chest radiograph cases from 16 sources, with among them in total 1,404 cases had OVF. For model testing, we retrieved 542 chest radiograph cases and 162 spine radiograph cases from four independent clinics, with among them 215 cases had OVF. All cases were female subjects, and except for 31 training data cases which were spine trauma cases, all the remaining cases were post-menopausal women. Image data included DICOM (Digital Imaging and Communications in Medicine) format, hard film scanned PNG (Portable Network Graphics) format, DICOM exported PNG format, and PACS (Picture Archiving and Communication System) downloaded resolution reduced DICOM format. OVF classification included: minimal and mild grades with <20% or ≥20–25% vertebral height loss respectively, moderate grade with ≥25–40% vertebral height loss, severe grade with ≥40%–2/3 vertebral height loss, and collapsed grade with ≥2/3 vertebral height loss. The CVF detection base model was mainly composed of convolution layers that include convolution kernels of different sizes, pooling layers, up-sampling layers, feature merging layers, and residual modules. When the model loss function could not be further decreased with additional training, the model was considered to be optimal and termed ‘base-model 1.0’. A user-friendly interface was also developed, with the synthesized software termed ‘Ofeye 1.0’. Results Counting cases and with minimal and mild OVFs included, base-model 1.0 demonstrated a specificity of 97.1%, a sensitivity of 86%, and an accuracy of 93.9% for the 704 testing cases. In total, 33 OVFs in 30 cases had a false negative reading, which constituted a false negative rate of 14.0% (30/215) by counting all OVF cases. Eighteen OVFs in 15 cases had OVFs of ≥ moderate grades missed, which constituted a false negative rate of 7.0% (15/215, i.e., sensitivity 93%) if only counting cases with ≥ moderate grade OVFs missed. False positive reading was recorded in 13 vertebrae in 13 cases (one vertebra in each case), which constituted a false positivity rate of 2.7% (13/489). These vertebrae with false positivity labeling could be readily differentiated from a true OVF by a human reader. The software Ofeye 1.0 allows ‘batch processing’, for example, 100 radiographs can be processed in a single operation. This software can be integrated into hospital PACS, or installed in a standalone personal computer. Conclusions A user-friendly software program was developed for CVF detection on elderly women’s lateral chest radiographs. It has an o...

show abstract

“…The deep learning algorithm is one of the most effective methods to identify subtle differences. Chen developed a deep‐learning model for identifying fresh vertebral compression fractures from digital radiography, and found that the ensemble model reached an AUC of .80, an accuracy of 74% and a sensitivity of 80% 14 . Bae used CNN with plain X‐ray to detect of femoral neck fracture, and found that the values were .999 AUC, .986 accuracy, .960 Youden index, and .966 sensitivity, and .993 specificity 38 .…”

Section: Discussionmentioning

confidence: 99%

Prediction of subsequent osteoporotic vertebral compression fracture on CT radiography via deep learning

Zhu

Qian

et al. 2022

VIEW

View full text Add to dashboard Cite

Combination of computed tomography (CT) radiography and deep learning to predict subsequent osteoporotic vertebral compression fracture (OVCF) has not been reported. To do so, we analyzed retrospectively CT images from 103 patients who experienced twice OVCF in Tongji Hospital from 2011 to 2022. Meanwhile, CT images from 70 age-matched osteoporotic patients without vertebral fracture were used as the negative control. Convolutional neural network was used for classification and the Adam optimizer combining the momentum and exponentially weighted moving average gradients methods were used to update the weights of the networks. In the prediction model, we split 80% data of each type of the patient as the training group, while the other 20% was held as the independent testing group. We found that the number of subsequent fracture in women is higher than that in men (81 vs. 22). Additionally, the incidence rate of adjacent vertebral fracture is higher than that of remote vertebral fracture (64.1 vs. 35.9%), while the onset time of the former was 11.9 ± 12.8 months, significantly less than 22.3 ± 18.2 months of the latter (p < .001). For the prediction of subsequent fracture, our model attained .839 of accuracy and .883 of receiver operating characteristic-area under curve on the whole testing dataset. Furthermore, our model gained .867 and .719 of accuracy on the single-class testing dataset separated from the former, .817 of accuracy on the independent test. In conclusion, Xiao Hu and Yanjing Zhu contributed equally to this work.

show abstract

A deep-learning model for identifying fresh vertebral compression fractures on digital radiography

Cited by 39 publications

References 32 publications

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study

A software program for automated compressive vertebral fracture detection on elderly women’s lateral chest radiograph: Ofeye 1.0

Prediction of subsequent osteoporotic vertebral compression fracture on CT radiography via deep learning

Contact Info

Product

Resources

About