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Rassmann S, Keller A, Skaf K, Hustinx A, Gausche R, Ibarra-Arrelano MA, Hsieh TC, Madajieu YED, Nöthen MM, Pfäffle R, Attenberger UI, Born M, Mohnike K, Krawitz PM, Javanmardi B. Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias. Pediatr Radiol 2024; 54:82-95. [PMID: 37953411 PMCID: PMC10776485 DOI: 10.1007/s00247-023-05789-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Skeletal dysplasias collectively affect a large number of patients worldwide. Most of these disorders cause growth anomalies. Hence, evaluating skeletal maturity via the determination of bone age (BA) is a useful tool. Moreover, consecutive BA measurements are crucial for monitoring the growth of patients with such disorders, especially for timing hormonal treatment or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra- and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automate BA assessment have been proposed, few are validated for BA assessment on children with skeletal dysplasias. OBJECTIVE We present Deeplasia, an open-source prior-free deep-learning approach designed for BA assessment specifically validated on patients with skeletal dysplasias. MATERIALS AND METHODS We trained multiple convolutional neural network models under various conditions and selected three to build a precise model ensemble. We utilized the public BA dataset from the Radiological Society of North America (RSNA) consisting of training, validation, and test subsets containing 12,611, 1,425, and 200 hand and wrist radiographs, respectively. For testing the performance of our model ensemble on dysplastic hands, we retrospectively collected 568 radiographs from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders including achondroplasia and hypochondroplasia. A subset of the dysplastic cohort (149 images) was used to estimate the test-retest precision of our model ensemble on longitudinal data. RESULTS The mean absolute difference of Deeplasia for the RSNA test set (based on the average of six different reference ratings) and dysplastic set (based on the average of two different reference ratings) were 3.87 and 5.84 months, respectively. The test-retest precision of Deeplasia on longitudinal data (2.74 months) is estimated to be similar to a human expert. CONCLUSION We demonstrated that Deeplasia is competent in assessing the age and monitoring the development of both normal and dysplastic bones.
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Affiliation(s)
- Sebastian Rassmann
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Venusberg-Campus 1 Building 11, 2nd Floor, 53127, Bonn, Germany
| | | | - Kyra Skaf
- Medical Faculty, Otto-Von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Alexander Hustinx
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Venusberg-Campus 1 Building 11, 2nd Floor, 53127, Bonn, Germany
| | - Ruth Gausche
- CrescNet - Wachstumsnetzwerk, Medical Faculty, University Hospital Leipzig, Leipzig, Germany
| | - Miguel A Ibarra-Arrelano
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Venusberg-Campus 1 Building 11, 2nd Floor, 53127, Bonn, Germany
| | - Tzung-Chien Hsieh
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Venusberg-Campus 1 Building 11, 2nd Floor, 53127, Bonn, Germany
| | | | - Markus M Nöthen
- Institute of Human Genetics, University Hospital Bonn, Bonn, Germany
| | - Roland Pfäffle
- Department for Pediatrics, University Hospital Leipzig, Leipzig, Germany
| | - Ulrike I Attenberger
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Mark Born
- Division of Paediatric Radiology, Department of Radiology, University Hospital Bonn, Bonn, Germany
| | - Klaus Mohnike
- Medical Faculty, Otto-Von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Peter M Krawitz
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Venusberg-Campus 1 Building 11, 2nd Floor, 53127, Bonn, Germany
| | - Behnam Javanmardi
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Venusberg-Campus 1 Building 11, 2nd Floor, 53127, Bonn, Germany.
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Eninanç İ, Büyükbayraktar ZÇ. Assessment of correlation between hand-wrist maturation and cervical vertebral maturation: a fractal analysis study. BMC Oral Health 2023; 23:798. [PMID: 37884998 PMCID: PMC10601178 DOI: 10.1186/s12903-023-03483-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/03/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND To investigate whether fractal dimension (FD) measurements from hand-wrist radiographs and lateral cephalometric radiographs are correlated with each other and with skeletal maturation stages. METHODS In this retrospective study conducted on hand-wrist and lateral cephalometric radiographs obtained from patients between 2017 and 2023, hand-wrist maturation stages (HWMS) and cervical vertebral maturation stages (CVMS) of 144 subjects (6 to 17 years of age) were assessed radiographically. The participants were divided into nine groups (n = 16 each) based on HWMS. Fractal analysis was performed on the radiographs of the radius, the middle finger phalanges (proximal, medial and distal), and the cervical vertebral bodies (C2, C3, C4). Mean and standard deviation values, Spearman's and Pearson correlation analyses, one-way ANOVA, Kruskal-Wallis H tests and Mann-Whitney-U test were used to evaluate the data. RESULTS Positive correlations were found between the FD values of the radius and HWMS or CVMS (r = .559, P = .001, r = .528 P = .001 respectively). The FD values of the radius were positively correlated with those of all cervical vertebrae (C2, C3, C4), proximal and medial phalanges as well as age. FD values measured from the proximal phalanx, medial phalanx and radius showed significant differences among both HWMS and CVMS (P < .05). HWMS was strongly correlated with CVMS (r = .929, P = .001). Age was strongly correlated with HWMS (r = .795, P = .001) and CVMS (r = .756, P = .001). There was a significant difference in terms of age distribution among HWMS and CVMS (P < .05). CONCLUSIONS FD measurements on hand-wrist radiographs can provide useful information for the assessment of skeletal maturation stage. Especially, FD measurements from the radius are important and more reliable to predict skeletal maturation stage.
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Affiliation(s)
- İlknur Eninanç
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sivas Cumhuriyet University, Sivas, Turkey.
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Martín Pérez SE, Martín Pérez IM, Vega González JM, Molina Suárez R, León Hernández C, Rodríguez Hernández F, Herrera Perez M. Precision and Accuracy of Radiological Bone Age Assessment in Children among Different Ethnic Groups: A Systematic Review. Diagnostics (Basel) 2023; 13:3124. [PMID: 37835867 PMCID: PMC10572703 DOI: 10.3390/diagnostics13193124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/24/2023] [Accepted: 09/30/2023] [Indexed: 10/15/2023] Open
Abstract
AIM The aim was to identify, evaluate, and summarize the findings of relevant individual studies on the precision and accuracy of radiological BA assessment procedures among children from different ethnic groups. MATERIALS AND METHODS A qualitative systematic review was carried out following the MOOSE statement and previously registered in PROSPERO (CRD42023449512). A search was performed in MEDLINE (PubMed) (n = 561), the Cochrane Library (n = 261), CINAHL (n = 103), Web of Science (WOS) (n = 181), and institutional repositories (n = 37) using MeSH and free terms combined with the Booleans "AND" and "OR". NOS and ROBINS-E were used to assess the methodological quality and the risk of bias of the included studies, respectively. RESULTS A total of 51 articles (n = 20,100) on radiological BA assessment procedures were precise in terms of intra-observer and inter-observer reliability for all ethnic groups. In Caucasian and Hispanic children, the Greulich-Pyle Atlas (GPA) was accurate at all ages, but in youths, Tanner-Whitehouse radius-ulna-short bones 3 (TW3-RUS) could be an alternative. In Asian and Arab subjects, GPA and Tanner-Whitehouse 3 (TW3) overestimated the BA in adolescents near adulthood. In African youths, GPA overestimated the BA while TW3 was more accurate. CONCLUSION GPA and TW3 radiological BA assessment procedures are both precise but their accuracy in estimating CA among children of different ethnic groups can be altered by racial bias.
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Affiliation(s)
- Sebastián Eustaquio Martín Pérez
- Departamento de Farmacología y Medicina Física, Área de Radiología y Medicina Física, Sección de Enfermería y Fisioterapia, Facultad de Ciencias de la Salud, Universidad de La Laguna, 38200 Santa Cruz de Tenerife, Spain; (I.M.M.P.); (F.R.H.)
- Escuela de Doctorado y Estudios de Posgrado, Universidad de La Laguna, San Cristóbal de La Laguna, 38203 Santa Cruz de Tenerife, Spain
- Musculoskeletal Pain and Motor Control Research Group, Faculty of Health Sciences, Universidad Europea de Canarias, 38300 Santa Cruz de Tenerife, Spain
- Musculoskeletal Pain and Motor Control Research Group, Faculty of Sport Sciences, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain
| | - Isidro Miguel Martín Pérez
- Departamento de Farmacología y Medicina Física, Área de Radiología y Medicina Física, Sección de Enfermería y Fisioterapia, Facultad de Ciencias de la Salud, Universidad de La Laguna, 38200 Santa Cruz de Tenerife, Spain; (I.M.M.P.); (F.R.H.)
- Escuela de Doctorado y Estudios de Posgrado, Universidad de La Laguna, San Cristóbal de La Laguna, 38203 Santa Cruz de Tenerife, Spain
| | - Jesús María Vega González
- Institute of Legal Medicine and Forensic Sciences of Santa Cruz de Tenerife, 38230 San Cristóbal de La Laguna, Spain;
| | - Ruth Molina Suárez
- Pediatric Endocrinology Unit, Pediatric Department, Hospital Universitario de Canarias, San Cristóbal de La Laguna, 38320 Santa Cruz de Tenerife, Spain;
| | - Coromoto León Hernández
- Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Apdo. 456, San Cristóbal de La Laguna, 38200 Santa Cruz de Tenerife, España;
| | - Fidel Rodríguez Hernández
- Departamento de Farmacología y Medicina Física, Área de Radiología y Medicina Física, Sección de Enfermería y Fisioterapia, Facultad de Ciencias de la Salud, Universidad de La Laguna, 38200 Santa Cruz de Tenerife, Spain; (I.M.M.P.); (F.R.H.)
| | - Mario Herrera Perez
- School of Medicine (Health Sciences), Universidad de La Laguna, 38200 Santa Cruz de Tenerife, Spain;
- Foot and Ankle Unit, Orthopedic Surgery and Traumatology Department, San Cristóbal de La Laguna, 38320 Santa Cruz de Tenerife, Spain
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Liu R, Zhu H, Wang L, Han B, Du J, Jia Y. Coarse-to-fine segmentation and ensemble convolutional neural networks for automated pediatric bone age assessment. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Meza BC, LaValva SM, Aoyama JT, DeFrancesco CJ, Striano BM, Carey JL, Nguyen JC, Ganley TJ. A Novel Shorthand Approach to Knee Bone Age Using MRI: A Validation and Reliability Study. Orthop J Sports Med 2021; 9:23259671211021582. [PMID: 34395683 PMCID: PMC8361531 DOI: 10.1177/23259671211021582] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 02/23/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Bone-age determination remains a difficult process. An atlas for bone age has been created from knee-ossification patterns on magnetic resonance imaging (MRI), thereby avoiding the need for radiographs and associated costs, radiation exposure, and clinical inefficiency. Shorthand methods for bone age can be less time-consuming and require less extensive training as compared with conventional methods. Purpose: To create and validate a novel shorthand algorithm for bone age based on knee MRIs that could correlate with conventional hand bone age and demonstrate reliability across medical trainees. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Included in this study were adolescent patients who underwent both knee MRI and hand bone age radiographs within 90 days between 2009 and 2018. A stepwise algorithm for predicting bone age using knee MRI was developed separately for male and female patients, and 7 raters at varying levels of training used the algorithm to determine the bone age for each MRI. The shorthand algorithm was validated using Spearman rho (rS) to correlate each rater’s predicted MRI bone age with the recorded Greulich and Pyle (G&P) hand bone age. Interrater and intrarater reliability were also calculated using intraclass correlation coefficients (ICCs). Results: A total of 38 patients (44.7% female) underwent imaging at a mean age of 12.8 years (range, 9.3-15.7 years). Shorthand knee MRI bone age scores were strongly correlated with G&P hand bone age (rS = 0.83; P < .001). The shorthand algorithm was a valid predictor of G&P hand bone age regardless of level of training, as medical students (rS = 0.75), residents (rS = 0.81), and attending physicians (rS = 0.84) performed similarly. The interrater reliability of our shorthand algorithm was 0.81 (95% CI, 0.73-0.88), indicating good to excellent interobserver agreement. Respondents also demonstrated consistency, with 6 of 7 raters demonstrating excellent intrarater reliability (median ICC, 0.86 [range, 0.68-0.96]). Conclusion: This shorthand algorithm is a consistent, reliable, and valid way to determine skeletal maturity using knee MRI in patients aged 9 to 16 years and can be utilized across different levels of orthopaedic and radiographic expertise. This method is readily applicable in a clinical setting and may reduce the need for routine hand bone age radiographs.
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Affiliation(s)
- Blake C Meza
- Hospital for Special Surgery, New York, New York, USA
| | | | - Julien T Aoyama
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | | | - Brendan M Striano
- Harvard Combined Orthopaedic Residency Program, Boston, Massachusetts, USA
| | - James L Carey
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jie C Nguyen
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Theodore J Ganley
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
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Koc U, Taydaş O, Bolu S, Elhan AH, Karakas SP. The Greulich-Pyle and Gilsanz-Ratib atlas method versus automated estimation tool for bone age: a multi-observer agreement study. Jpn J Radiol 2020; 39:267-272. [PMID: 33067733 DOI: 10.1007/s11604-020-01055-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 10/07/2020] [Indexed: 12/01/2022]
Abstract
PURPOSE To evaluate the agreement between observers using Greulich-Pyle (GP) and Gilsanz-Ratib (GR) methods, between four specialities (radiology, pediatrics, pediatric endocrinology and pediatric radiology) and between observers and automated tool in the bone age estimation. MATERIALS AND METHODS A total of 99 observers participated in this questionnaire-based study. BoneXpert was used for the automated tool. Experienced, senior, and junior observers were defined by their experience, and the bone age determined by experienced observers was regarded as the ground truth. Agreement between observers was evaluated using the coefficient of variance (CV) and intraclass correlation coefficient (ICC), and they were reevaluated after adding BoneXpert to the observers. Agreement of BoneXpert, the senior, and the junior observers was also evaluated using the root-mean-square-error (RMSE) values and Blant Altman method by comparing with the ground truth. RESULTS The CV ranged from 4.98% to 22.08%. The ICC were 0.980 for GP, 0.980 for GP and BoneXpert, 0.973 for GR, and 0.976 for GR and BoneXpert, and the ICC between four specialities ranged form 0.963 to 0.990. BoneXpert tool had the lowest RMSE values (0.504 years for GP atlas). CONCLUSION Automated bone age estimation showed comparable results with GP and GR methods and its utilization may decrease inter-observer variability.
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Affiliation(s)
- Ural Koc
- Radiology Section, Ankara Sehit Ahmet Ozsoy State Hospital, Ankara, Turkey.
| | - Onur Taydaş
- School of Medicine, Department of Radiology, Sakarya University, Sakarya, Turkey
- Department of Radiology, Erzincan Mengucek Gazi Training and Research Hospital, Erzincan, Turkey
| | - Semih Bolu
- Department of Pediatric Endocrinology, Adiyaman Training Research Hospital, Adiyaman, Turkey
| | - Atilla Halil Elhan
- Faculty of Medicine, Department of Biostatistics, Ankara University, Ankara, Turkey
| | - S Pınar Karakas
- Clinic of Pediatric Radiology, UCSF Benioff Children's Hospital, Oakland, CA, USA
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Wang F, Gu X, Chen S, Liu Y, Shen Q, Pan H, Shi L, Jin Z. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development. PeerJ 2020; 8:e8854. [PMID: 32274267 PMCID: PMC7127473 DOI: 10.7717/peerj.8854] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 03/05/2020] [Indexed: 11/20/2022] Open
Abstract
Objective Bone age (BA) is a crucial indicator for revealing the growth and development of children. This study tested the performance of a fully automated artificial intelligence (AI) system for BA assessment of Chinese children with abnormal growth and development. Materials and Methods A fully automated AI system based on the Greulich and Pyle (GP) method was developed for Chinese children by using 8,000 BA radiographs from five medical centers nationwide in China. Then, a total of 745 cases (360 boys and 385 girls) with abnormal growth and development from another tertiary medical center of north China were consecutively collected between January and October 2018 to test the system. The reference standard was defined as the result interpreted by two experienced reviewers (a radiologist with 10 years and an endocrinologist with 15 years of experience in BA reading) through consensus using the GP atlas. BA accuracy within 1 year, root mean square error (RMSE), mean absolute difference (MAD), and 95% limits of agreement according to the Bland-Altman plot were statistically calculated. Results For Chinese pediatric patients with abnormal growth and development, the accuracy of this new automated AI system within 1 year was 84.60% as compared to the reference standard, with the highest percentage of 89.45% in the 12- to 18-year group. The RMSE, MAD, and 95% limits of agreement of the AI system were 0.76 years, 0.58 years, and -1.547 to 1.428, respectively, according to the Bland-Altman plot. The largest difference between the AI and experts' BA result was noted for patients of short stature with bone deformities, severe osteomalacia, or different rates of maturation of the carpals and phalanges. Conclusions The developed automated AI system could achieve comparable BA results to experienced reviewers for Chinese children with abnormal growth and development.
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Affiliation(s)
- Fengdan Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiao Gu
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Shi Chen
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yongliang Liu
- Hangzhou YITU Healthcare Technology Co., Ltd., Hangzhou, China
| | - Qing Shen
- Hangzhou YITU Healthcare Technology Co., Ltd., Hangzhou, China
| | - Hui Pan
- Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Lei Shi
- Hangzhou YITU Healthcare Technology Co., Ltd., Hangzhou, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Makaremi M, Lacaule C, Mohammad-Djafari A. Deep Learning and Artificial Intelligence for the Determination of the Cervical Vertebra Maturation Degree from Lateral Radiography. ENTROPY 2019. [PMCID: PMC7514567 DOI: 10.3390/e21121222] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Deep Learning (DL) and Artificial Intelligence (AI) tools have shown great success in different areas of medical diagnostics. In this paper, we show another success in orthodontics. In orthodontics, the right treatment timing of many actions and operations is crucial because many environmental and genetic conditions may modify jaw growth. The stage of growth is related to the Cervical Vertebra Maturation (CVM) degree. Thus, determining the CVM to determine the suitable timing of the treatment is important. In orthodontics, lateral X-ray radiography is used to determine it. Many classical methods need knowledge and time to look and identify some features. Nowadays, ML and AI tools are used for many medical and biological diagnostic imaging. This paper reports on the development of a Deep Learning (DL) Convolutional Neural Network (CNN) method to determine (directly from images) the degree of maturation of CVM classified in six degrees. The results show the performances of the proposed method in different contexts with different number of images for training, evaluation and testing and different pre-processing of these images. The proposed model and method are validated by cross validation. The implemented software is almost ready for use by orthodontists.
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Affiliation(s)
- Masrour Makaremi
- Department of Orthodontics, University of Bordeaux, 33000 Bordeaux, France; (M.M.); (C.L.)
| | - Camille Lacaule
- Department of Orthodontics, University of Bordeaux, 33000 Bordeaux, France; (M.M.); (C.L.)
| | - Ali Mohammad-Djafari
- International Science Consulting and Training (ISCT), 91440 Bures-sur-Yvette, France
- Correspondence: ; Tel.: +33-6-2295-4233
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Mihail RP, Liang G, Jacobs N. Automatic Hand Skeletal Shape Estimation From Radiographs. IEEE Trans Nanobioscience 2019; 18:296-305. [DOI: 10.1109/tnb.2019.2911026] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Abstract
The ability to correct limb deformities is one of the core elements of pediatric orthopedics. The term, orthopedics, is derived from the Greek language and means straightening (ortho) children (paidos). New advances in the evaluation and management of children with limb alignment or limb length issues are constantly appearing. This review highlights some of the recent technologies that have been developed to improve the care of these children.
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Affiliation(s)
- Christopher A Iobst
- Department of Orthopedic Surgery, Center for Limb Lengthening and Reconstruction, The Ohio State University, College of Medicine, Nationwide Children's Hospital, 700 Children's Drive, Suite T2E-A2700, Columbus, OH 43205, USA.
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Choi JA, Kim YC, Min SJ, Khil EK. A simple method for bone age assessment: the capitohamate planimetry. Eur Radiol 2018; 28:2299-2307. [PMID: 29383523 PMCID: PMC5938295 DOI: 10.1007/s00330-017-5255-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 12/04/2017] [Accepted: 12/18/2017] [Indexed: 11/26/2022]
Abstract
Objectives To determine if the capitohamate (CH) planimetry could be a reliable indicator of bone age, and to compare it with Greulich-Pyle (GP) method. Methods This retrospective study included 391 children (age, 1–180 months). Two reviewers manually measured the areas of the capitate and hamate on plain radiographs. CH planimetry was defined as the measurement of the sum of areas of the capitate and hamate. Two reviewers independently applied the CH planimetry and GP methods in 109 children whose heights were at the 50th percentile of the growth chart. Results There was a strong positive correlation between chronological age and CH planimetry measurement (right, r = 0.9702; left, r = 0.9709). There was no significant difference in accuracy between CH planimetry (84.39–84.46 %) and the GP method (85.15–87.66 %) (p ≥ 0.0867). The interobserver reproducibility of CH planimetry (precision, 4.42 %; 95 % limits of agreement [LOA], −10.5 to 13.4 months) was greater than that of the GP method (precision, 8.45 %; LOA, −29.5 to 21.1 months). Conclusions CH planimetry may be a reliable method for bone age assessment. Key Points • Bone age assessment is important in the work-up of paediatric endocrine disorders. • Radiography of the left hand is widely used to estimate bone age. • Capitatohamate planimetry is a reliable and reproducible method for assessing bone age.
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Affiliation(s)
- Jung-Ah Choi
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, 7 Keunjaebong-gil, Hwaseong, 18450, Gyeonggi-do, Republic of Korea
| | - Young Chul Kim
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, 7 Keunjaebong-gil, Hwaseong, 18450, Gyeonggi-do, Republic of Korea.
| | - Seon Jeong Min
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, 7 Keunjaebong-gil, Hwaseong, 18450, Gyeonggi-do, Republic of Korea
| | - Eun Kyung Khil
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, 7 Keunjaebong-gil, Hwaseong, 18450, Gyeonggi-do, Republic of Korea
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