Endocrinology and Metabolism

Jae-Won Lee (Lee JW)

2 Articles

Calcium & Bone Metabolism
Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm: Sung Hye Kong, Jae-Won Lee, Byeong Uk Bae, Jin Kyeong Sung, Kyu Hwan Jung, Jung Hee Kim, Chan Soo Shin; Endocrinol Metab. 2022;37(4):674-683. Published online August 5, 2022; DOI: https://doi.org/10.3803/EnM.2022.1461

3,888 View
211 Download
12 Web of Science
15 Crossref

Abstract PDF Supplementary Material PubReader ePub

Background
Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data.
Methods
This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models.
Results
Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women.
Conclusion
DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.

Citations

Citations to this article as recorded by

Automated detection of vertebral fractures from X-ray images: A novel machine learning model and survey of the field
Li-Wei Cheng, Hsin-Hung Chou, Yu-Xuan Cai, Kuo-Yuan Huang, Chin-Chiang Hsieh, Po-Lun Chu, I-Szu Cheng, Sun-Yuan Hsieh
Neurocomputing.2024; 566: 126946. CrossRef
Application of radiomics model based on lumbar computed tomography in diagnosis of elderly osteoporosis
Baisen Chen, Jiaming Cui, Chaochen Li, Pengjun Xu, Guanhua Xu, Jiawei Jiang, Pengfei Xue, Yuyu Sun, Zhiming Cui
Journal of Orthopaedic Research.2024;[Epub] CrossRef
Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis
Satoshi Maki, Takeo Furuya, Masahiro Inoue, Yasuhiro Shiga, Kazuhide Inage, Yawara Eguchi, Sumihisa Orita, Seiji Ohtori
Journal of Clinical Medicine.2024; 13(3): 705. CrossRef
A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture
Yisak Kim, Young-Gon Kim, Jung-Wee Park, Byung Woo Kim, Youmin Shin, Sung Hye Kong, Jung Hee Kim, Young-Kyun Lee, Sang Wan Kim, Chan Soo Shin
Radiology.2024;[Epub] CrossRef
A Novel QCT-Based Deep Transfer Learning Approach for Predicting Stiffness Tensor of Trabecular Bone Cubes
Pengwei Xiao, Tinghe Zhang, Yufei Huang, Xiaodu Wang
IRBM.2024; 45(2): 100831. CrossRef
Deep learning in the radiologic diagnosis of osteoporosis: a literature review
Yu He, Jiaxi Lin, Shiqi Zhu, Jinzhou Zhu, Zhonghua Xu
Journal of International Medical Research.2024;[Epub] CrossRef
Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer
Min Wook Joo, Taehoon Ko, Min Seob Kim, Yong-Suk Lee, Seung Han Shin, Yang-Guk Chung, Hong Kwon Lee
Clinical Orthopaedics & Related Research.2023; 481(11): 2247. CrossRef
Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT
Sophia S. Goller, Jon F. Rischewski, Thomas Liebig, Jens Ricke, Sebastian Siller, Vanessa F. Schmidt, Robert Stahl, Julian Kulozik, Thomas Baum, Jan S. Kirschke, Sarah C. Foreman, Alexandra S. Gersing
Diagnostics.2023; 13(12): 2119. CrossRef
Machine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study
Kainat A. Ullah, Faisal Rehman, Muhammad Anwar, Muhammad Faheem, Naveed Riaz
Health Science Reports.2023;[Epub] CrossRef
Skeletal Fracture Detection with Deep Learning: A Comprehensive Review
Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin, Masri Ayob, Gauthamen Punganan
Diagnostics.2023; 13(20): 3245. CrossRef
Deep learning system for automated detection of posterior ligamentous complex injury in patients with thoracolumbar fracture on MRI
Sang Won Jo, Eun Kyung Khil, Kyoung Yeon Lee, Il Choi, Yu Sung Yoon, Jang Gyu Cha, Jae Hyeok Lee, Hyunggi Kim, Sun Yeop Lee
Scientific Reports.2023;[Epub] CrossRef
Vertebra Segmentation Based Vertebral Compression Fracture Determination from Reconstructed Spine X-Ray Images
Srinivasa Rao Gadu, Chandra Sekhar Potala
International Journal of Electrical and Electronics Research.2023; 11(4): 1225. CrossRef
Computer Vision in Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review
Anthony K. Allam, Adrish Anand, Alex R. Flores, Alexander E. Ropper
Neurospine.2023; 20(4): 1112. CrossRef
A Meaningful Journey to Predict Fractures with Deep Learning
Jeonghoon Ha
Endocrinology and Metabolism.2022; 37(4): 617. CrossRef
New Horizons: Artificial Intelligence Tools for Managing Osteoporosis
Hans Peter Dimai
The Journal of Clinical Endocrinology & Metabolism.2022;[Epub] CrossRef

Bone Metabolism
Efficacy of a Once-Monthly Pill Containing Ibandronate and Cholecalciferol on the Levels of 25-Hydroxyvitamin D and Bone Markers in Postmenopausal Women with Osteoporosis: In-Jin Cho, Ho-Yeon Chung, Sung-Woon Kim, Jae-Won Lee, Tae-Won Lee, Hye-Soon Kim, Sin-Gon Kim, Han Seok Choi, Sung-Hee Choi, Chan Soo Shin, Ki-Won Oh, Yong-Ki Min, Jung-Min Koh, Yumie Rhee, Dong-Won Byun, Yoon-Sok Chung, Jeong Hyun Park, Dong Jin Chung, Minho Shong, Eun-Gyoung Hong, Chang Beom Lee, Ki Hyun Baek, Moo-Il Kang; Endocrinol Metab. 2015;30(3):272-279. Published online December 9, 2014; DOI: https://doi.org/10.3803/EnM.2015.30.3.272

4,503 View
47 Download
4 Web of Science
5 Crossref

Abstract PDF PubReader

Background

The present study evaluated the efficacy of a combination of ibandronate and cholecalciferol on the restoration of the levels of 25-hydroxyvitamin D (25[OH]D) and various bone markers in postmenopausal women with osteoporosis.

Methods

This was a randomized, double-blind, active-controlled, prospective 16-week clinical trial conducted in 20 different hospitals. A total of 201 postmenopausal women with osteoporosis were assigned randomly to one of two groups: the IBN group, which received a once-monthly pill containing 150 mg ibandronate (n=99), or the IBN+ group, which received a once-monthly pill containing 150 mg ibandronate and 24,000 IU cholecalciferol (n=102). Serum levels of 25(OH)D, parathyroid hormone (PTH), and various bone markers were assessed at baseline and at the end of a 16-week treatment period.

Results

After 16 weeks of treatment, the mean serum levels of 25(OH)D significantly increased from 21.0 to 25.3 ng/mL in the IBN+ group but significantly decreased from 20.6 to 17.4 ng/mL in the IBN group. Additionally, both groups exhibited significant increases in mean serum levels of PTH but significant decreases in serum levels of bone-specific alkaline phosphatase and C-telopeptide of type 1 collagen (CTX) at 16 weeks; no significant differences were observed between the groups. However, in subjects with a vitamin D deficiency, IBN+ treatment resulted in a significant decrease in serum CTX levels compared with IBN treatment.

Conclusion

The present findings demonstrate that a once-monthly pill containing ibandronate and cholecalciferol may be useful for the amelioration of vitamin D deficiency in patients with postmenopausal osteoporosis. Moreover, this treatment combination effectively decreased serum levels of resorption markers, especially in subjects with a vitamin D deficiency, over the 16-week treatment period.

Citations

Citations to this article as recorded by

Effect of vitamin D supplementation or fortification on bone turnover markers in women: a systematic review and meta-analysis
Nasrin Nasimi, Sanaz Jamshidi, Aida Askari, Nazanin Zolfaghari, Erfan Sadeghi, Mehran Nouri, Nick Bellissimo, Shiva Faghih
British Journal of Nutrition.2024; 131(9): 1473. CrossRef
Quality of life and patient satisfaction with raloxifene/cholecalciferol combination therapy in postmenopausal women
Dong-Yun Lee, Yoon-Sok Chung
Scientific Reports.2022;[Epub] CrossRef
Efficacy of risedronate with cholecalciferol on bone mineral density in Korean patients with osteoporosis
So Young Park, Moo-Il Kang, Hyung Moo Park, Yumie Rhee, Seong Hwan Moon, Hyun Koo Yoon, Jung-Min Koh, Jae Suk Chang, In Joo Kim, Ye Yeon Won, Ye Soo Park, Hoon Choi, Chan Soo Shin, Taek Rim Yoon, Sung-Cheol Yun, Ho-Yeon Chung
Archives of Osteoporosis.2020;[Epub] CrossRef
Efficacy and safety of vitamin D3 B.O.N intramuscular injection in Korean adults with vitamin D deficiency
Han Seok Choi, Yoon-Sok Chung, Yong Jun Choi, Da Hea Seo, Sung-Kil Lim
Osteoporosis and Sarcopenia.2016; 2(4): 228. CrossRef
Pharmacologic treatment of osteoporosis
Yong-Ki Min
Journal of the Korean Medical Association.2016; 59(11): 847. CrossRef

Author index