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  1. Article: Diagnostic Accuracy of Deep Learning for the Prediction of Osteoporosis Using Plain X-rays: A Systematic Review and Meta-Analysis.

    Yen, Tzu-Yun / Ho, Chan-Shien / Chen, Yueh-Peng / Pei, Yu-Cheng

    Diagnostics (Basel, Switzerland)

    2024  Volume 14, Issue 2

    Abstract: 1) Background: This meta-analysis assessed the diagnostic accuracy of deep learning model-based osteoporosis prediction using plain X-ray images. (2) Methods: We searched PubMed, Web of Science, SCOPUS, and Google Scholar from no set beginning date to ... ...

    Abstract (1) Background: This meta-analysis assessed the diagnostic accuracy of deep learning model-based osteoporosis prediction using plain X-ray images. (2) Methods: We searched PubMed, Web of Science, SCOPUS, and Google Scholar from no set beginning date to 28 February 2023, for eligible studies that applied deep learning methods for diagnosing osteoporosis using X-ray images. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. The area under the receiver operating characteristic curve (AUROC) was used to quantify the predictive performance. Subgroup, meta-regression, and sensitivity analyses were performed to identify the potential sources of study heterogeneity. (3) Results: Six studies were included; the pooled AUROC, sensitivity, and specificity were 0.88 (95% confidence interval [CI] 0.85-0.91), 0.81 (95% CI 0.78-0.84), and 0.87 (95% CI 0.81-0.92), respectively, indicating good performance. Moderate heterogeneity was observed. Mega-regression and subgroup analyses were not performed due to the limited number of studies included. (4) Conclusion: Deep learning methods effectively extract bone density information from plain radiographs, highlighting their potential for opportunistic screening. Nevertheless, additional prospective multicenter studies involving diverse patient populations are required to confirm the applicability of this novel technique.
    Language English
    Publishing date 2024-01-18
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2662336-5
    ISSN 2075-4418
    ISSN 2075-4418
    DOI 10.3390/diagnostics14020207
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

    Yen, Tzu-Yun / Ho, Chan-Shien / Pei, Yu-Cheng / Fan, Tzuo-Yau / Chang, Szu-Yi / Kuo, Chang-Fu / Chen, Yueh-Peng

    Bone

    2024  Volume 184, Page(s) 117107

    Abstract: Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bladder ...

    Abstract Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bladder (KUB) radiographs are inexpensive and frequently ordered in clinical practice. Thus, it is a potential screening tool for osteoporosis. In this study, we explored the possibility of predicting the bone mineral density (BMD) and classifying high-risk patient groups using KUB radiographs. We proposed DeepDXA-KUB, a deep learning model that predicts the BMD values of the left hip and lumbar vertebrae from an input KUB image. The datasets were obtained from Taiwanese medical centers between 2006 and 2019, using 8913 pairs of KUB radiographs and DXA examinations performed within 6 months. The images were randomly divided into training and validation sets in a 4:1 ratio. To evaluate the model's performance, we computed a confusion matrix and evaluated the sensitivity, specificity, accuracy, precision, positive predictive value, negative predictive value, F1 score, and area under the receiver operating curve (AUROC). Moderate correlations were observed between the predicted and DXA-measured BMD values, with a correlation coefficient of 0.858 for the lumbar vertebrae and 0.87 for the left hip. The model demonstrated an osteoporosis detection accuracy, sensitivity, and specificity of 84.7 %, 81.6 %, and 86.6 % for the lumbar vertebrae and 84.2 %, 91.2 %, and 81 % for the left hip, respectively. The AUROC was 0.939 for the lumbar vertebrae and 0.947 for the left hip, indicating a satisfactory performance in osteoporosis screening. The present study is the first to develop a deep learning model based on KUB radiographs to predict lumbar spine and femoral BMD. Our model demonstrated a promising correlation between the predicted and DXA-measured BMD in both the lumbar vertebrae and hip, showing great potential for the opportunistic screening of osteoporosis.
    Language English
    Publishing date 2024-04-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 632515-4
    ISSN 1873-2763 ; 8756-3282
    ISSN (online) 1873-2763
    ISSN 8756-3282
    DOI 10.1016/j.bone.2024.117107
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Determining the Most Suitable Ultrasound-Guided Injection Technique in Treating Lumbar Facet Joint Syndrome.

    Suputtitada, Areerat / Chen, Jean-Lon / Wu, Chih-Kuan / Peng, Yu-Ning / Yen, Tzu-Yun / Chen, Carl P C

    Biomedicines

    2023  Volume 11, Issue 12

    Abstract: 1) Background: Lower back pain is often caused by lumbar facet joint syndrome. This study investigated the effectiveness of three different injection methods under ultrasound guidance in treating elderly patients with lumbar facet joint syndrome. The ... ...

    Abstract (1) Background: Lower back pain is often caused by lumbar facet joint syndrome. This study investigated the effectiveness of three different injection methods under ultrasound guidance in treating elderly patients with lumbar facet joint syndrome. The difficulty in performing these injections was also evaluated; (2) Methods: A total of 60 elderly patients with facet joint syndrome as the cause of lower back pain were recruited and divided into 3 groups. Group 1 received medial branch block (MBB). Group 2 received intra-articular facet joint injections. Group 3 received injection into the multifidus muscle portion that covers the facet joint. Five percent dextrose water (D5W) was used as the injectant. The visual analog scale (VAS) was used to measure the degree of lower back pain; (3) Results: Before the injection treatments, the VAS score averaged about 7.5. After three consecutive injection treatments (two weeks interval), the VAS score decreased significantly to an average of about 1 in all 3 groups, representing mild to no pain. Between group analyses also did not reveal significant statistical differences, suggesting that these procedures are equally effective; (4) Conclusions: Ultrasound-guided injection of the multifidus muscle may be a feasible option in treating elderly patients with lower back pain caused by facet joint syndrome as it is easier to perform as compared to MBB and intra-articular facet joint injection.
    Language English
    Publishing date 2023-12-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2720867-9
    ISSN 2227-9059
    ISSN 2227-9059
    DOI 10.3390/biomedicines11123308
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Application of deep learning neural network in predicting bone mineral density from plain X-ray radiography.

    Ho, Chan-Shien / Chen, Yueh-Peng / Fan, Tzuo-Yau / Kuo, Chang-Fu / Yen, Tzu-Yun / Liu, Yuan-Chang / Pei, Yu-Cheng

    Archives of osteoporosis

    2021  Volume 16, Issue 1, Page(s) 153

    Abstract: DeepDXA is a deep learning model designed to infer bone mineral density data from plain pelvis X-ray, and it can achieve good predicted value for clinical use.: Purpose: Osteoporosis is defined as a systemic disease of the bone characterized by a ... ...

    Abstract DeepDXA is a deep learning model designed to infer bone mineral density data from plain pelvis X-ray, and it can achieve good predicted value for clinical use.
    Purpose: Osteoporosis is defined as a systemic disease of the bone characterized by a decrease in bone strength and deterioration of bone structure at the microscopic level, leading to bone fragility and increased risk of fracture. Bone mineral density (BMD) is the preferred method for the diagnosis of osteoporosis, and dual-energy x-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis. Conventional radiography is more suited for the screening of osteoporosis rather than diagnosis, and osteoporosis can be detected on radiographs by experienced physicians only. This study explored the possibility of predicting BMD relative to DXA using patient radiographs.
    Methods: A deep learning algorithm of convolutional neural network (CNN) was used for the purpose. The method includes image segmentation, CNN learning, and a convolution-based regression model (DeepDXA) that links the isolated images of the femur bone to predict BMD value. Data were obtained in a single medical center from 2006 to 2018, with a total amount of 3472 pairs of pelvis X-ray and DXA examination within 1 year.
    Results: The proposed workflow successfully predicted BMD values of the femur bone with the correlation coefficient (R) of 0.85 (P < 0.001) and the accuracy of 0.88 for prediction osteoporosis, a finding that could be reliably ready for further clinical use.
    Conclusion: When suspicious osteoporosis is seen on plain films using the deep learning method we developed, further referral to DXA for the definite diagnosis of osteoporosis is indicated.
    MeSH term(s) Absorptiometry, Photon ; Bone Density ; Deep Learning ; Humans ; Neural Networks, Computer ; Radiography ; X-Rays
    Language English
    Publishing date 2021-10-09
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2253231-6
    ISSN 1862-3514 ; 1862-3522
    ISSN (online) 1862-3514
    ISSN 1862-3522
    DOI 10.1007/s11657-021-00985-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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