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Choi G, Ham S, Je BK, Rhie YJ, Ahn KS, Shim E, Lee MJ. Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model. Eur Radiol 2024:10.1007/s00330-024-10748-x. [PMID: 38676732 DOI: 10.1007/s00330-024-10748-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/28/2024] [Accepted: 03/21/2024] [Indexed: 04/29/2024]
Abstract
OBJECTIVES To improve pubertal bone age (BA) evaluation by developing a precise and practical elbow BA classification using the olecranon, and a deep-learning AI model. MATERIALS AND METHODS Lateral elbow radiographs taken for BA evaluation in children under 18 years were collected from January 2020 to June 2022, retrospectively. A novel classification and the olecranon BA were established based on the morphological changes in the olecranon ossification process during puberty. The olecranon BA was compared with other elbow and hand BA methods, using intraclass correlation coefficients (ICCs), and a deep-learning AI model was developed. RESULTS A total of 3508 lateral elbow radiographs (mean age 9.8 ± 1.8 years) were collected. The olecranon BA showed the highest applicability (100%) and interobserver agreement (ICC 0.993) among elbow BA methods. It showed excellent reliability with Sauvegrain (0.967 in girls, 0.969 in boys) and Dimeglio (0.978 in girls, 0.978 in boys) elbow BA methods, as well as Korean standard (KS) hand BA in boys (0.917), and good reliability with KS in girls (0.896) and Greulich-Pyle (GP)/Tanner-Whitehouse (TW)3 (0.835 in girls, 0.895 in boys) hand BA methods. The AI model for olecranon BA showed an accuracy of 0.96 and a specificity of 0.98 with EfficientDet-b4. External validation showed an accuracy of 0.86 and a specificity of 0.91. CONCLUSION The olecranon BA evaluation for puberty, requiring only a lateral elbow radiograph, showed the highest applicability and interobserver agreement, and excellent reliability with other BA evaluation methods, along with a high performance of the AI model. CLINICAL RELEVANCE STATEMENT This AI model uses a single lateral elbow radiograph to determine bone age for puberty from the olecranon ossification center and can improve pubertal bone age assessment with the highest applicability and excellent reliability compared to previous methods. KEY POINTS Elbow bone age is valuable for pubertal bone age assessment, but conventional methods have limitations. Olecranon bone age and its AI model showed high performances for pubertal bone age assessment. Olecranon bone age system is practical and accurate while requiring only a single lateral elbow radiograph.
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Affiliation(s)
- Gayoung Choi
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Korea
| | - Sungwon Ham
- Healthcare Readiness Institute for Unified Korea, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Korea
| | - Bo-Kyung Je
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Korea.
| | - Young-Jun Rhie
- Department of Pediatrics, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Korea
| | - Kyung-Sik Ahn
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Euddeum Shim
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Seoul, Korea
| | - Mi-Jung Lee
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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Lee KC, Cho Y, Ahn KS, Park HJ, Kang YS, Lee S, Kim D, Kang CH. Deep-Learning-Based Automated Rotator Cuff Tear Screening in Three Planes of Shoulder MRI. Diagnostics (Basel) 2023; 13:3254. [PMID: 37892075 PMCID: PMC10606560 DOI: 10.3390/diagnostics13203254] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/06/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
This study aimed to develop a screening model for rotator cuff tear detection in all three planes of routine shoulder MRI using a deep neural network. A total of 794 shoulder MRI scans (374 men and 420 women; aged 59 ± 11 years) were utilized. Three musculoskeletal radiologists labeled the rotator cuff tear. The YOLO v8 rotator cuff tear detection model was then trained; training was performed with all imaging planes simultaneously and with axial, coronal, and sagittal images separately. The performances of the models were evaluated and compared using receiver operating curves and the area under the curve (AUC). The AUC was the highest when using all imaging planes (0.94; p < 0.05). Among a single imaging plane, the axial plane showed the best performance (AUC: 0.71), followed by the sagittal (AUC: 0.70) and coronal (AUC: 0.68) imaging planes. The sensitivity and accuracy were also the highest in the model with all-plane training (0.98 and 0.96, respectively). Thus, deep-learning-based automatic rotator cuff tear detection can be useful for detecting torn areas in various regions of the rotator cuff in all three imaging planes.
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Affiliation(s)
- Kyu-Chong Lee
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea (C.H.K.)
| | - Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea (C.H.K.)
- Advanced Medical Imaging Institute, Korea University College of Medicine, Seoul 02841, Republic of Korea
- AI Center, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - Kyung-Sik Ahn
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea (C.H.K.)
- Advanced Medical Imaging Institute, Korea University College of Medicine, Seoul 02841, Republic of Korea
- AI Center, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - Hyun-Joon Park
- Institute for Healthcare Service Innovation, College of Medicine, Korea University, Seoul 02841, Republic of Korea; (H.-J.P.); (Y.-S.K.)
| | - Young-Shin Kang
- Institute for Healthcare Service Innovation, College of Medicine, Korea University, Seoul 02841, Republic of Korea; (H.-J.P.); (Y.-S.K.)
| | - Sungshin Lee
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea (C.H.K.)
| | | | - Chang Ho Kang
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Republic of Korea (C.H.K.)
- Advanced Medical Imaging Institute, Korea University College of Medicine, Seoul 02841, Republic of Korea
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Deng Y, Chen Y, He Q, Wang X, Liao Y, Liu J, Liu Z, Huang J, Song T. Bone age assessment from articular surface and epiphysis using deep neural networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:13133-13148. [PMID: 37501481 DOI: 10.3934/mbe.2023585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Bone age assessment is of great significance to genetic diagnosis and endocrine diseases. Traditional bone age diagnosis mainly relies on experienced radiologists to examine the regions of interest in hand radiography, but it is time-consuming and may even lead to a vast error between the diagnosis result and the reference. The existing computer-aided methods predict bone age based on general regions of interest but do not explore specific regions of interest in hand radiography. This paper aims to solve such problems by performing bone age prediction on the articular surface and epiphysis from hand radiography using deep convolutional neural networks. The articular surface and epiphysis datasets are established from the Radiological Society of North America (RSNA) pediatric bone age challenge, where the specific feature regions of the articular surface and epiphysis are manually segmented from hand radiography. Five convolutional neural networks, i.e., ResNet50, SENet, DenseNet-121, EfficientNet-b4, and CSPNet, are employed to improve the accuracy and efficiency of bone age diagnosis in clinical applications. Experiments show that the best-performing model can yield a mean absolute error (MAE) of 7.34 months on the proposed articular surface and epiphysis datasets, which is more accurate and fast than the radiologists. The project is available at https://github.com/YameiDeng/BAANet/, and the annotated dataset is also published at https://doi.org/10.5281/zenodo.7947923.
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Affiliation(s)
- Yamei Deng
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
| | - Yonglu Chen
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
| | - Qian He
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
| | - Xu Wang
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China
| | - Yong Liao
- School of physics, electronics and electrical engineering, Xiangnan University, Chenzhou 423000, China
| | - Jue Liu
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
| | - Zhaoran Liu
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
| | - Jianwei Huang
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
| | - Ting Song
- Department of Radiology, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
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Cho D, Choi YS, Oh H, Ahn YM, Seo JY. Accuracy of predicted adult height using the Greulich-Pyle method and artificial intelligence medical device. Clin Exp Pediatr 2023; 66:145-147. [PMID: 36701990 PMCID: PMC9989724 DOI: 10.3345/cep.2022.01116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/28/2022] [Indexed: 01/26/2023] Open
Affiliation(s)
- Dongho Cho
- Department of Pediatrics, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Korea
| | - Yun Sun Choi
- Department of Radiology, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Korea
| | - Hayun Oh
- Department of Radiology, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Korea
| | | | - Ji-Young Seo
- Department of Pediatrics, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Korea
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Razzaq M, Clément F, Yvinec R. An overview of deep learning applications in precocious puberty and thyroid dysfunction. Front Endocrinol (Lausanne) 2022; 13:959546. [PMID: 36339395 PMCID: PMC9632447 DOI: 10.3389/fendo.2022.959546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 09/16/2022] [Indexed: 11/24/2022] Open
Abstract
In the last decade, deep learning methods have garnered a great deal of attention in endocrinology research. In this article, we provide a summary of current deep learning applications in endocrine disorders caused by either precocious onset of adult hormone or abnormal amount of hormone production. To give access to the broader audience, we start with a gentle introduction to deep learning and its most commonly used architectures, and then we focus on the research trends of deep learning applications in thyroid dysfunction classification and precocious puberty diagnosis. We highlight the strengths and weaknesses of various approaches and discuss potential solutions to different challenges. We also go through the practical considerations useful for choosing (and building) the deep learning model, as well as for understanding the thought process behind different decisions made by these models. Finally, we give concluding remarks and future directions.
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Affiliation(s)
- Misbah Razzaq
- PRC, INRAE, CNRS, Université de Tours, Nouzilly, France
- *Correspondence: Misbah Razzaq,
| | - Frédérique Clément
- Université Paris-Saclay, Inria, Centre Inria de Saclay, Palaiseau, France
| | - Romain Yvinec
- PRC, INRAE, CNRS, Université de Tours, Nouzilly, France
- Université Paris-Saclay, Inria, Centre Inria de Saclay, Palaiseau, France
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