Next Article in Journal
The JAK1/2 Inhibitor Baricitinib Mitigates the Spike-Induced Inflammatory Response of Immune and Endothelial Cells In Vitro
Next Article in Special Issue
How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA)
Previous Article in Journal
Retinol Binding Protein, Sunlight Hours, and the Influenza Virus-Specific Immune Response
Previous Article in Special Issue
Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study

1
Department of Orthopedics Surgery, Japan Community Healthcare Organization (JCHO) Tokyo Shinjuku Medical Center, Tokyo 162-8543, Japan
2
Department of Orthopedics Surgery, Nagoya University Graduate School of Medicine, Nagoya 464-8550, Japan
3
Department of Orthopedics Surgery, Gamagori City Hospital, Gamagori 443-8501, Japan
4
Department of Orthopedics Surgery, Miyamoto Orthopaedic Hospital, Okayama 703-8236, Japan
5
Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, Japan
6
Systematic Review Workshop Peer Support Group (SRWS-PSG), Osaka 541-0043, Japan
7
Department of Orthopedics Surgery, The Jikei University Kashiwa Hospital, Chiba 277-8567, Japan
8
Department of Orthopedics Surgery, The National Hospital Organization Nagoya Medical Center, Nagoya 460-0001, Japan
9
iSurgery Co., Ltd., Tokyo 103-0012, Japan
10
Department of Orthopaedics Surgery, Hyogo Prefectural Kakogawa Medical Center, Kakogawa 675-0003, Japan
*
Author to whom correspondence should be addressed.
Biomedicines 2022, 10(9), 2323; https://doi.org/10.3390/biomedicines10092323
Submission received: 30 August 2022 / Revised: 13 September 2022 / Accepted: 15 September 2022 / Published: 19 September 2022
(This article belongs to the Special Issue Artificial Intelligence in Biological and Biomedical Imaging 2.0)

Abstract

Although the number of patients with osteoporosis is increasing worldwide, diagnosis and treatment are presently inadequate. In this study, we developed a deep learning model to predict bone mineral density (BMD) and T-score from chest X-rays, which are one of the most common, easily accessible, and low-cost medical imaging examination methods. The dataset used in this study contained patients who underwent dual-energy X-ray absorptiometry (DXA) and chest radiography at six hospitals between 2010 and 2021. We trained the deep learning model through ensemble learning of chest X-rays, age, and sex to predict BMD using regression and T-score for multiclass classification. We assessed the following two metrics to evaluate the performance of the deep learning model: (1) correlation between the predicted and true BMDs and (2) consistency in the T-score between the predicted class and true class. The correlation coefficients for BMD prediction were hip = 0.75 and lumbar spine = 0.63. The areas under the curves for the T-score predictions of normal, osteopenia, and osteoporosis diagnoses were 0.89, 0.70, and 0.84, respectively. These results suggest that the proposed deep learning model may be suitable for screening patients with osteoporosis by predicting BMD and T-score from chest X-rays.
Keywords: osteoporosis; screening; DXA; BMD; chest X-ray; deep learning; artificial intelligence osteoporosis; screening; DXA; BMD; chest X-ray; deep learning; artificial intelligence

Share and Cite

MDPI and ACS Style

Sato, Y.; Yamamoto, N.; Inagaki, N.; Iesaki, Y.; Asamoto, T.; Suzuki, T.; Takahara, S. Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study. Biomedicines 2022, 10, 2323. https://doi.org/10.3390/biomedicines10092323

AMA Style

Sato Y, Yamamoto N, Inagaki N, Iesaki Y, Asamoto T, Suzuki T, Takahara S. Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study. Biomedicines. 2022; 10(9):2323. https://doi.org/10.3390/biomedicines10092323

Chicago/Turabian Style

Sato, Yoichi, Norio Yamamoto, Naoya Inagaki, Yusuke Iesaki, Takamune Asamoto, Tomohiro Suzuki, and Shunsuke Takahara. 2022. "Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study" Biomedicines 10, no. 9: 2323. https://doi.org/10.3390/biomedicines10092323

APA Style

Sato, Y., Yamamoto, N., Inagaki, N., Iesaki, Y., Asamoto, T., Suzuki, T., & Takahara, S. (2022). Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study. Biomedicines, 10(9), 2323. https://doi.org/10.3390/biomedicines10092323

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop