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Ruitenbeek HC, Oei EHG, Visser JJ, Kijowski R. Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade. Skeletal Radiol 2024:10.1007/s00256-024-04684-6. [PMID: 38902420 DOI: 10.1007/s00256-024-04684-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 06/22/2024]
Abstract
This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.
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Affiliation(s)
- Huibert C Ruitenbeek
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Edwin H G Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA.
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Elahmedi M, Sawhney R, Guadagno E, Botelho F, Poenaru D. The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review. J Pediatr Surg 2024; 59:774-782. [PMID: 38418276 DOI: 10.1016/j.jpedsurg.2024.01.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Accepted: 01/22/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery. METHODS Nine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2. RESULTS Authors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%. CONCLUSIONS While AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows. LEVEL OF EVIDENCE 2A.
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Affiliation(s)
- Mohamed Elahmedi
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Riya Sawhney
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Elena Guadagno
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Fabio Botelho
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada
| | - Dan Poenaru
- Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada.
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Chen J, Fan X, Chen Z, Peng Y, Liang L, Su C, Chen Y, Yao J. Enhancing YOLO5 for the Assessment of Irregular Pelvic Radiographs with Multimodal Information. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:744-755. [PMID: 38315343 PMCID: PMC11031542 DOI: 10.1007/s10278-024-00986-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/09/2023] [Accepted: 12/12/2023] [Indexed: 02/07/2024]
Abstract
Developmental dysplasia of the hip (DDH) is one of the most common orthopedic disorders in infants and young children. Accurate identification and localization of anatomical landmarks are prerequisites for the diagnosis of DDH. In recent years, various works have employed deep learning algorithms on radiography images for DDH diagnosis. However, none of these works have considered the incorporation of multimodal information. The pelvis exhibits distinct structures at different developmental stages, and there are also gender-based differences. In light of this, this study proposes a method to enhance the performance of deep learning models in diagnosing DDH by incorporating age and gender information into the channels. The study utilizes YOLO5 to construct a deep learning network for detecting hip joint landmarks. Moreover, a comprehensive dataset of 7750 pelvic X-ray images is established, covering ages from 4 months to 16 years and encompassing various conditions, such as deformities and post-operative cases, which authentically capture the temporal diversity and pathological complexities of DDH. Experimental results show that the YOLO5 model with integrated multimodal information achieves a mAP0.5-0.95 of 83.1% and a diagnostic accuracy of 86.7% in test dataset. The F1 scores for diagnosing cases of normal (NM), suspected dislocation (SD), mild dislocation (MD), and heavily dislocation (HD) are 90.9%, 79.8%, 63.5%, and 97.4%, respectively. Furthermore, experiments conducted on datasets of different sizes and networks of different sizes demonstrate the beneficial impact of multimodal information in improving the effectiveness of deep learning in diagnosing DDH.
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Affiliation(s)
- Jing Chen
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Xiaoyou Fan
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Zhen Chen
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Yichao Peng
- Department of Pediatric Orthopedics, Center for Orthopaedic Surgery, Hospital of Guangdong Province, The Third School of Clinical Medicine, Southern Medical University, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, China
- Department of Orthopedics, Academy of Orthopedics Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, Guangzhou, 510630, Guangdong, China
| | - Lichong Liang
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Chengyue Su
- School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Yun Chen
- Department of Pediatric Orthopedics, Center for Orthopaedic Surgery, Hospital of Guangdong Province, The Third School of Clinical Medicine, Southern Medical University, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, China.
- School of Nursing, Southern Medical University, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, China.
| | - Jinghui Yao
- Department of Pediatric Orthopedics, Center for Orthopaedic Surgery, Hospital of Guangdong Province, The Third School of Clinical Medicine, Southern Medical University, The Third Affiliated Hospital of Southern Medical University, Guangzhou, 510630, Guangdong, China.
- Department of Orthopedics, Academy of Orthopedics Guangdong Province, Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, Guangzhou, 510630, Guangdong, China.
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Chen YP, Fan TY, Chu CC, Lin JJ, Ji CY, Kuo CF, Kao HK. Automatic and human level Graf's type identification for detecting developmental dysplasia of the hip. Biomed J 2024; 47:100614. [PMID: 37308078 PMCID: PMC10955653 DOI: 10.1016/j.bj.2023.100614] [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: 02/14/2023] [Revised: 05/11/2023] [Accepted: 06/07/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Developmental dysplasia of the hip (DDH) is a common congenital disorder that may lead to hip dislocation and requires surgical intervention if left untreated. Ultrasonography is the preferred method for DDH screening; however, the lack of experienced operators impedes its application in universal neonatal screening. METHODS We developed a deep neural network tool to automatically register the five keypoints that mark important anatomical structures of the hip and provide a reference for measuring alpha and beta angles following Graf's guidelines, which is an ultrasound classification system for DDH in infants. Two-dimensional (2D) ultrasonography images were obtained from 986 neonates aged 0-6 months. A total of 2406 images from 921 patients were labeled with ground truth keypoints by senior orthopedists. RESULTS Our model demonstrated precise keypoint localization. The mean absolute error was approximately 1 mm, and the derived alpha angle measurement had a correlation coefficient of R = 0.89 between the model and ground truth. The model achieved an area under the receiver operating characteristic curve of 0.937 and 0.974 for classifying alpha <60° (abnormal hip) and <50° (dysplastic hip), respectively. On average, the experts agreed with 96% of the inferenced images, and the model could generalize its prediction on newly collected images with a correlation coefficient higher than 0.85. CONCLUSIONS Precise localization and highly correlated performance metrics suggest that the model can be an efficient tool for assisting DDH diagnosis in clinical settings.
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Affiliation(s)
- Yueh-Peng Chen
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Master of Science Degree Program in Innovation for Smart Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Tzuo-Yau Fan
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Artificial Intelligence Research and Development, Chang Gung Medical Technology Co., Ltd., Linkou, Taiwan
| | - Cheng-Cj Chu
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Jainn-Jim Lin
- Division of Pediatric Critical Care Medicine and Pediatric Neurocritical Care Center, Chang Gung Children's Hospital and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chin-Yi Ji
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Department of Artificial Intelligence Research and Development, Chang Gung Medical Technology Co., Ltd., Linkou, Taiwan
| | - Chang-Fu Kuo
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
| | - Hsuan-Kai Kao
- Department of Orthopedic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Bone and Joint Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.
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Jan F, Rahman A, Busaleh R, Alwarthan H, Aljaser S, Al-Towailib S, Alshammari S, Alhindi KR, Almogbil A, Bubshait DA, Ahmed MIB. Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach. J Imaging 2023; 9:242. [PMID: 37998088 PMCID: PMC10672484 DOI: 10.3390/jimaging9110242] [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: 09/30/2023] [Revised: 10/25/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023] Open
Abstract
Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray scans. However, this process can be challenging for medical personnel to achieve without proper training. To address this challenge, we propose a computational framework to detect DDH in pelvic X-ray imaging of infants that utilizes a pipelined deep learning-based technique consisting of two stages: instance segmentation and keypoint detection models to measure acetabular index angle and assess DDH affliction in the presented case. The main aim of this process is to provide an objective and unified approach to DDH diagnosis. The model achieved an average pixel error of 2.862 ± 2.392 and an error range of 2.402 ± 1.963° for the acetabular angle measurement relative to the ground truth annotation. Ultimately, the deep-learning model will be integrated into the fully developed mobile application to make it easily accessible for medical specialists to test and evaluate. This will reduce the burden on medical specialists while providing an accurate and explainable DDH diagnosis for infants, thereby increasing their chances of successful treatment and recovery.
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Affiliation(s)
- Farmanullah Jan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Atta Rahman
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Roaa Busaleh
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Haya Alwarthan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Samar Aljaser
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Sukainah Al-Towailib
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Safiyah Alshammari
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Khadeejah Rasheed Alhindi
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Asrar Almogbil
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia (K.R.A.)
| | - Dalal A. Bubshait
- Department of Orthopedics, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Mohammed Imran Basheer Ahmed
- Department of Computer Engineering, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
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Andras LM, Sanders JS, Goldstein RY, Samora JB. What's New in Pediatric Orthopaedics. J Bone Joint Surg Am 2023; 105:269-276. [PMID: 36729585 DOI: 10.2106/jbjs.22.01195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Lindsay M Andras
- Jackie and Gene Autry Orthopedic Center, Children's Hospital Los Angeles, Los Angeles, California
| | - Julia S Sanders
- Department of Orthopedic Surgery, University of Colorado, Aurora, Colorado
| | - Rachel Y Goldstein
- Jackie and Gene Autry Orthopedic Center, Children's Hospital Los Angeles, Los Angeles, California
| | - Julie Balch Samora
- Department of Orthopedic Surgery, Nationwide Children's Hospital, Columbus, Ohio
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Sha J, Huang L, Chen Y, Fan Z, Lin J, Yang Q, Li Y, Yan Y. Clinical thought-based software for diagnosing developmental dysplasia of the hip on pediatric pelvic radiographs. Front Pediatr 2023; 11:1080194. [PMID: 37063681 PMCID: PMC10098126 DOI: 10.3389/fped.2023.1080194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 03/06/2023] [Indexed: 04/18/2023] Open
Abstract
Background The common methods of radiographic diagnosis of developmental dysplasia of the hip (DDH) include measuring hip parameters and quantifying the degree of hip dislocation. However, clinical thought-based analysis of hip parameters may be a more effective way to achieve expert-like diagnoses of DDH. This study aims to develop a diagnostic strategy-based software for pediatric DDH and validate its clinical feasibility. Methods In total, 543 anteroposterior pelvic radiographs were retrospectively collected from January 2017 to December 2021. Two independent clinicians measured four diagnostic indices to compare the diagnoses made by the software and conventional manual method. The diagnostic accuracy was evaluated using the receiver operator characteristic (ROC) curves and confusion matrix, and the consistency of parametric measurements was assessed using Bland-Altman plots. Results In 543 cases (1,086 hips), the area under the curve, accuracy, sensitivity, and specificity of the software for diagnosing DDH were 0.988-0.994, 99.08%-99.72%, 98.07%-100.00%, and 99.59%, respectively. Compared with the expert panel, the Bland-Altman 95% limits of agreement for the acetabular index, as determined by the software, were -2.09°-2.91° (junior orthopedist) and -1.98°-2.72° (intermediate orthopedist). As for the lateral center-edge angle, the 95% limits were -3.68°-5.28° (junior orthopedist) and -2.94°-4.59° (intermediate orthopedist). Conclusions The software can provide expert-like analysis of pelvic radiographs and obtain the radiographic diagnosis of pediatric DDH with great consistency and efficiency. Its initial success lays the groundwork for developing a full-intelligent comprehensive diagnostic system of DDH.
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Affiliation(s)
- Jia Sha
- Department of Orthopedics, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Luyu Huang
- Department of Orthopedics, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Yaopeng Chen
- School of Telecommunications Engineering, Xidian University, Xi’an, China
- Guangzhou Institute, Xidian University, Xi’an, China
| | - Zongzhi Fan
- Department of Orthopedics, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Jincong Lin
- Department of Orthopedics, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Qinghai Yang
- School of Telecommunications Engineering, Xidian University, Xi’an, China
| | - Yi Li
- School of Telecommunications Engineering, Xidian University, Xi’an, China
- Correspondence: Yabo Yan Yi Li
| | - Yabo Yan
- Department of Orthopedics, Xijing Hospital, Air Force Military Medical University, Xi’an, China
- Correspondence: Yabo Yan Yi Li
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Jensen J, Graumann O, Overgaard S, Gerke O, Lundemann M, Haubro MH, Varnum C, Bak L, Rasmussen J, Olsen LB, Rasmussen BSB. A Deep Learning Algorithm for Radiographic Measurements of the Hip in Adults-A Reliability and Agreement Study. Diagnostics (Basel) 2022; 12:2597. [PMID: 36359441 PMCID: PMC9689405 DOI: 10.3390/diagnostics12112597] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/22/2022] [Accepted: 10/24/2022] [Indexed: 08/04/2023] Open
Abstract
Hip dysplasia (HD) is a frequent cause of hip pain in skeletally mature patients and may lead to osteoarthritis (OA). An accurate and early diagnosis may postpone, reduce or even prevent the onset of OA and ultimately hip arthroplasty at a young age. The overall aim of this study was to assess the reliability of an algorithm, designed to read pelvic anterior-posterior (AP) radiographs and to estimate the agreement between the algorithm and human readers for measuring (i) lateral center edge angle of Wiberg (LCEA) and (ii) Acetabular index angle (AIA). The algorithm was based on deep-learning models developed using a modified U-net architecture and ResNet 34. The newly developed algorithm was found to be highly reliable when identifying the anatomical landmarks used for measuring LCEA and AIA in pelvic radiographs, thus offering highly consistent measurement outputs. The study showed that manual identification of the same landmarks made by five specialist readers were subject to variance and the level of agreement between the algorithm and human readers was consequently poor with mean measured differences from 0.37 to 9.56° for right LCEA measurements. The algorithm displayed the highest agreement with the senior orthopedic surgeon. With further development, the algorithm may be a good alternative to humans when screening for HD.
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Affiliation(s)
- Janni Jensen
- Department of Radiology, Odense University Hospital, 5000 Odense, Denmark
- Research and Innovation Unit of Radiology, University of Southern Denmark, 5230 Odense, Denmark
- Open Patient Data Explorative Network, OPEN, Odense University Hospital, 5000 Odense, Denmark
| | - Ole Graumann
- Department of Radiology, Odense University Hospital, 5000 Odense, Denmark
- Research and Innovation Unit of Radiology, University of Southern Denmark, 5230 Odense, Denmark
| | - Søren Overgaard
- Department of Orthopaedic Surgery and Traumatology, Copenhagen University Hospital, Bispebjerg, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 1165 Copenhagen, Denmark
| | - Oke Gerke
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark
- Department of Nuclear Medicine, Odense University Hospital, 5000 Odense, Denmark
| | | | - Martin Haagen Haubro
- Department of Orthopedic Surgery and Traumatology, Odense University Hospital, 5000 Odense, Denmark
| | - Claus Varnum
- Department of Orthopedic Surgery and Traumatology, Odense University Hospital, 5000 Odense, Denmark
- Department of Orthopedic Surgery, Lillebaelt Hospital—Vejle, University Hospital of Southern Denmark, 7100 Vejle, Denmark
- Department of Regional Health Research, University of Southern Denmark, 5230 Odense, Denmark
| | - Lene Bak
- Department of Radiology, Odense University Hospital, 5000 Odense, Denmark
| | - Janne Rasmussen
- Department of Radiology, Odense University Hospital, 5700 Svendborg, Denmark
| | - Lone B. Olsen
- Department of Radiology, Odense University Hospital, 5000 Odense, Denmark
| | - Benjamin S. B. Rasmussen
- Department of Radiology, Odense University Hospital, 5000 Odense, Denmark
- Research and Innovation Unit of Radiology, University of Southern Denmark, 5230 Odense, Denmark
- Department of Radiology, Odense University Hospital, 5700 Svendborg, Denmark
- CAI-X (Centre for Clinical Artificial Intelligence), Odense University Hospital, University of Southern Denmark, 5230 Odense, Denmark
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