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Alzubaidi L, Al-Dulaimi K, Salhi A, Alammar Z, Fadhel MA, Albahri AS, Alamoodi AH, Albahri OS, Hasan AF, Bai J, Gilliland L, Peng J, Branni M, Shuker T, Cutbush K, Santamaría J, Moreira C, Ouyang C, Duan Y, Manoufali M, Jomaa M, Gupta A, Abbosh A, Gu Y. Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion. Artif Intell Med 2024; 155:102935. [PMID: 39079201 DOI: 10.1016/j.artmed.2024.102935] [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: 06/01/2023] [Revised: 03/18/2024] [Accepted: 07/22/2024] [Indexed: 08/24/2024]
Abstract
Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.
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Affiliation(s)
- Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia.
| | - Khamael Al-Dulaimi
- Computer Science Department, College of Science, Al-Nahrain University, Baghdad, Baghdad 10011, Iraq; School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Asma Salhi
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Zaenab Alammar
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Mohammed A Fadhel
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - A S Albahri
- Technical College, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
| | - A H Alamoodi
- Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, Malaysia
| | - O S Albahri
- Australian Technical and Management College, Melbourne, Australia
| | - Amjad F Hasan
- Faculty of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jinshuai Bai
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Luke Gilliland
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Jing Peng
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Marco Branni
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Tristan Shuker
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Kenneth Cutbush
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Jose Santamaría
- Department of Computer Science, University of Jaén, Jaén 23071, Spain
| | - Catarina Moreira
- Data Science Institute, University of Technology Sydney, Australia
| | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Ye Duan
- School of Computing, Clemson University, Clemson, 29631, SC, USA
| | - Mohamed Manoufali
- CSIRO, Kensington, WA 6151, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Mohammad Jomaa
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Ashish Gupta
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Amin Abbosh
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
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Kim SH, Yoo HJ, Yoon SH, Kim YT, Park SJ, Chai JW, Oh J, Chae HD. Development of a deep learning-based fully automated segmentation of rotator cuff muscles from clinical MR scans. Acta Radiol 2024; 65:1126-1132. [PMID: 39043149 DOI: 10.1177/02841851241262325] [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] [Indexed: 07/25/2024]
Abstract
BACKGROUND The fatty infiltration and atrophy in the muscle after a rotator cuff (RC) tear are important in surgical decision-making and are linked to poor clinical outcomes after rotator cuff repair. An accurate and reliable quantitative method should be developed to assess the entire RC muscles. PURPOSE To develop a fully automated approach based on a deep neural network to segment RC muscles from clinical magnetic resonance imaging (MRI) scans. MATERIAL AND METHODS In total, 94 shoulder MRI scans (mean age = 62.3 years) were utilized for the training and internal validation datasets, while an additional 20 MRI scans (mean age = 62.6 years) were collected from another institution for external validation. An orthopedic surgeon and a radiologist manually segmented muscles and bones as reference masks. Segmentation performance was evaluated using the Dice score, sensitivities, precision, and percent difference in muscle volume (%). In addition, the segmentation performance was assessed based on sex, age, and the presence of a RC tendon tear. RESULTS The average Dice score, sensitivities, precision, and percentage difference in muscle volume of the developed algorithm were 0.920, 0.933, 0.912, and 4.58%, respectively, in external validation. There was no difference in the prediction of shoulder muscles, with the exception of teres minor, where significant prediction errors were observed (0.831, 0.854, 0.835, and 10.88%, respectively). The segmentation performance of the algorithm was generally unaffected by age, sex, and the presence of RC tears. CONCLUSION We developed a fully automated deep neural network for RC muscle and bone segmentation with excellent performance from clinical MRI scans.
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Affiliation(s)
- Sae Hoon Kim
- Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hye Jin Yoo
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- MEDICALIP Co. Ltd., Seoul, Republic of Korea
| | - Yong Tae Kim
- Depatment of Orthopaedic Surgery, Hallym University Dongtan Sacred Heart Hospital, Gyeonggi, Republic of Korea
| | - Sang Joon Park
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- MEDICALIP Co. Ltd., Seoul, Republic of Korea
| | - Jee Won Chai
- Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
| | - Jiseon Oh
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hee Dong Chae
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
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Satir OB, Eghbali P, Becce F, Goetti P, Meylan A, Rothenbühler K, Diot R, Terrier A, Büchler P. Automatic quantification of scapular and glenoid morphology from CT scans using deep learning. Eur J Radiol 2024; 177:111588. [PMID: 38944907 DOI: 10.1016/j.ejrad.2024.111588] [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: 01/19/2024] [Revised: 05/28/2024] [Accepted: 06/24/2024] [Indexed: 07/02/2024]
Abstract
OBJECTIVES To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis. MATERIALS AND METHODS First, we used deep learning to segment the scapula from CT images and then to identify the location of 13 landmarks on the scapula, 9 of them to establish a coordinate system unaffected by osteoarthritis-related changes, and the remaining 4 landmarks on the glenoid cavity to determine the glenoid size and orientation in this scapular coordinate system. The glenoid version, glenoid inclination, critical shoulder angle, glenopolar angle, glenoid height, and glenoid width were subsequently measured in this coordinate system. A 5-fold cross-validation was performed to evaluate the performance of this approach on 60 normal/non-osteoarthritic and 56 pathological/osteoarthritic scapulae. RESULTS The Dice similarity coefficient between manual and automatic scapular segmentations exceeded 0.97 in both normal and pathological cases. The average error in automatic scapular and glenoid landmark positioning ranged between 1 and 2.5 mm and was comparable between the automatic method and human raters. The automatic method provided acceptable estimates of glenoid version (R2 = 0.95), glenoid inclination (R2 = 0.93), critical shoulder angle (R2 = 0.95), glenopolar angle (R2 = 0.90), glenoid height (R2 = 0.88) and width (R2 = 0.94). However, a significant difference was found for glenoid inclination between manual and automatic measurements (p < 0.001). CONCLUSIONS This open-source deep learning model enables the automatic quantification of scapular and glenoid morphology from CT scans of patients with glenohumeral osteoarthritis, with sufficient accuracy for clinical use.
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Affiliation(s)
- Osman Berk Satir
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Pezhman Eghbali
- Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Fabio Becce
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Patrick Goetti
- Department of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Arnaud Meylan
- Department of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Kilian Rothenbühler
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Robin Diot
- Department of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Alexandre Terrier
- Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Philippe Büchler
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
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Hashimoto E, Maki S, Ochiai N, Ise S, Inagaki K, Hiraoka Y, Hattori F, Ohtori S. Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning. J Shoulder Elbow Surg 2024; 33:1733-1739. [PMID: 38311106 DOI: 10.1016/j.jse.2023.12.009] [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: 01/20/2023] [Revised: 12/08/2023] [Accepted: 12/17/2023] [Indexed: 02/06/2024]
Abstract
BACKGROUND The diagnosis of rotator cuff tears (RCTs) using radiographs alone is clinically challenging; thus, the utility of deep learning algorithms based on convolutional neural networks has been remarkable in the field of medical imaging recognition. We aimed to evaluate the diagnostic performance of artificial intelligence (a deep learning algorithm; a convolutional neural network) to detect and classify RCTs using shoulder radiographs, and compare its diagnostic performance with that of orthopedic surgeons. METHODS A total of 1169 plain shoulder anteroposterior radiographs (1 image per shoulder) were included in the total dataset and divided into four groups: intact, small, medium, and large to massive tear groups. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating curve were measured for the detection of RCTs through binary classification. The average accuracy, recall, precision, and F1-score were divided into four groups by cuff tear size for multiclass classification. RESULTS The convolutional neural network demonstrated a high performance, with 92% sensitivity, 69% specificity, 86% accuracy, and an area under the receiver operating curve of 0.88 for the detection of RCTs. The average accuracy, recall, precision, and F1-score of the convolutional neural network for classification were 60%, 0.42, 0.49, and 0.45, respectively. The accuracy of the convolutional neural network for the detection and classification of RCTs was significantly better than that of orthopedic surgeons. CONCLUSION The convolutional neural network demonstrated the diagnostic ability to detect and classify RCTs using plain shoulder radiographs, and the diagnostic performance exhibited equal to superior accuracy when compared with those of shoulder experts.
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Affiliation(s)
- Eiko Hashimoto
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan.
| | - Satoshi Maki
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Nobuyasu Ochiai
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Shohei Ise
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Kenta Inagaki
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Yu Hiraoka
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Fumiya Hattori
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Seiji Ohtori
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
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Levin JM, Lorentz SG, Hurley ET, Lee J, Throckmorton TW, Garrigues GE, MacDonald P, Anakwenze O, Schoch BS, Klifto C. Artificial intelligence in shoulder and elbow surgery: overview of current and future applications. J Shoulder Elbow Surg 2024; 33:1633-1641. [PMID: 38430978 DOI: 10.1016/j.jse.2024.01.033] [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: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/14/2024] [Indexed: 03/05/2024]
Abstract
Artificial intelligence (AI) is amongst the most rapidly growing technologies in orthopedic surgery. With the exponential growth in healthcare data, computing power, and complex predictive algorithms, this technology is poised to aid providers in data processing and clinical decision support throughout the continuum of orthopedic care. Understanding the utility and limitations of this technology is vital to practicing orthopedic surgeons, as these applications will become more common place in everyday practice. AI has already demonstrated its utility in shoulder and elbow surgery for imaging-based diagnosis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation. The future integration of AI and robotic surgery represents the largest potential application of AI in shoulder and elbow surgery with the potential for significant clinical and financial impact. This editorial's purpose is to summarize common AI terms, provide a framework to understand and interpret AI model results, and discuss current applications and future directions within shoulder and elbow surgery.
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Affiliation(s)
- Jay M Levin
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA.
| | - Samuel G Lorentz
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Eoghan T Hurley
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Julia Lee
- Department of Orthopedic Surgery, Sierra Pacific Orthopedics, Fresno, CA, USA
| | - Thomas W Throckmorton
- Department of Orthopaedic Surgery, University of Tennessee-Campbell Clinic, Germantown, TN, USA
| | | | - Peter MacDonald
- Section of Orthopaedic Surgery & The Pan Am Clinic, University of Manitoba, Winnipeg, MB, Canada
| | - Oke Anakwenze
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
| | - Bradley S Schoch
- Department of Orthopedic Surgery, Mayo Clinic, Jacksonville, FL, USA
| | - Christopher Klifto
- Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA
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Cheng C, Liang X, Guo D, Xie D. Application of Artificial Intelligence in Shoulder Pathology. Diagnostics (Basel) 2024; 14:1091. [PMID: 38893618 PMCID: PMC11171621 DOI: 10.3390/diagnostics14111091] [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: 04/02/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.
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Affiliation(s)
- Cong Cheng
- Department of Orthopaedics, People’s Hospital of Longhua, Shenzhen 518000, China;
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Xinzhi Liang
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Dong Guo
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Denghui Xie
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
- Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
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Lannes X, Goetti P, Boubat M, Eghbali P, Becce F, Farron A, Terrier A. Three-dimensional evaluation of the transverse rotator cuff muscle's resultant force angle in relation to scapulohumeral subluxation and glenoid vault morphology in nonpathological shoulders. J Shoulder Elbow Surg 2024; 33:1157-1168. [PMID: 37898420 DOI: 10.1016/j.jse.2023.09.031] [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: 04/16/2023] [Revised: 09/20/2023] [Accepted: 09/24/2023] [Indexed: 10/30/2023]
Abstract
BACKGROUND Static posterior subluxation of the humeral head (SPSH) results in glenohumeral osteoarthritis. Treatment strategies for SPSH with or without resulting osteoarthritis remain challenging. There is growing interest in evaluating the rotator cuff muscle volume, fatty infiltration, or forces in osteoarthritic shoulders with SPSH, mainly due to a possible transverse force imbalance. In nonpathological shoulders, the transverse angle of the rotator cuff muscle's resultant force may be associated with scapulohumeral alignment and glenoid vault morphology, despite an assumed transverse force balance. The purpose of this study was to assess the transverse rotator cuff muscle's resultant force angle (TRFA) and its relationship with the scapulohumeral subluxation index (SHSI) and selected glenoid vault parameters using computer modeling. METHODS Computed tomography scans of 55 trauma patients (age 31 ± 13 years, 36 males) with nonpathological shoulders were analyzed and all measurements performed in 3-dimension. We placed landmarks manually to determine the humeral head center and the rotator cuff tendon footprints. The contours of the rotator cuff muscle cross-sectional areas were automatically predicted in a plane perpendicular to the scapula. Each rotator cuff muscle was divided into virtual vector fibers with homogeneous density. The resultant force vector direction for each muscle, corresponding to the rotator cuff action line, was calculated by vectorially summing the normalized fiber vectors for each muscle, weighted by the muscle trophic ratio. The resultant force vector was projected on the axial plane, and its angle with the mediolateral scapular axis was used to determine TRFA. The SHSI according to Walch, glenoid version angle (GVA), glenoid anteroposterior offset angle (GOA), glenoid depth, glenoid width, and glenoid radius were also evaluated. RESULTS The mean values for TRFA, SHSI, GVA, GOA, glenoid depth, glenoid width, and glenoid radius were 7.4 ± 4.5°, 54.3 ± 4.8%, -4.1 ± 4.4°, 5.1 ± 10.8°, 3.3 ± 0.6 mm, 20 ± 2 mm, and 33.6 ± 4.6 mm, respectively. The TRFA correlated strongly with SHSI (R = 0.731, P < .001) and GVA (R = 0.716, P < .001) and moderately with GOA (R = 0.663, P < .001). The SHSI was strongly negatively correlated with GVA (R = -0.813, P < .001) and moderately with GOA (R = -0.552, P < .001). The GVA correlated strongly with GOA (R = 0.768, P < .001). In contrast, TRFA, SHSI, GVA, and GOA did not correlate with glenoid depth, width, or radius. CONCLUSION Despite an assumed balance in the transverse volume of the rotator cuff muscles in nonpathological shoulders, variations exist regarding the transverse resultant force depending on the SHSI, GVA, and GOA. In healthy/nonosteoarthritic shoulders, an increased glenoid retroversion is associated with a decreased anterior glenoid offset.
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Affiliation(s)
- Xavier Lannes
- Service of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
| | - Patrick Goetti
- Service of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Matthieu Boubat
- Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Pezhman Eghbali
- Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Fabio Becce
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Alain Farron
- Service of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Alexandre Terrier
- Service of Orthopedics and Traumatology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Desai V. The Future of Artificial Intelligence in Sports Medicine and Return to Play. Semin Musculoskelet Radiol 2024; 28:203-212. [PMID: 38484772 DOI: 10.1055/s-0043-1778019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Artificial intelligence (AI) has shown tremendous growth over the last decade, with the more recent development of clinical applications in health care. The ability of AI to synthesize large amounts of complex data automatically allows health care providers to access previously unavailable metrics and thus enhance and personalize patient care. These innovations include AI-assisted diagnostic tools, prediction models for each treatment pathway, and various tools for workflow optimization. The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play.
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Affiliation(s)
- Vishal Desai
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania
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Alipour E, Chalian M, Pooyan A, Azhideh A, Shomal Zadeh F, Jahanian H. Automatic MRI-based rotator cuff muscle segmentation using U-Nets. Skeletal Radiol 2024; 53:537-545. [PMID: 37698626 DOI: 10.1007/s00256-023-04447-9] [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: 07/07/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/13/2023]
Abstract
BACKGROUND The rotator cuff (RC) is a crucial anatomical element within the shoulder joint, facilitating an extensive array of motions while maintaining joint stability. Comprised of the subscapularis, infraspinatus, supraspinatus, and teres minor muscles, the RC plays an integral role in shoulder functionality. RC injuries represent prevalent, incapacitating conditions that impose a substantial impact on approximately 8% of the adult population in the USA. Segmentation of these muscles provides valuable anatomical information for evaluating muscle quality and allows for better treatment planning. MATERIALS AND METHODS We developed a model based on residual deep convolutional encoder-decoder U-net to segment RC muscles on oblique sagittal T1-weighted images MRI. Our data consisted of shoulder MRIs from a cohort of 157 individuals, consisting of individuals without RC tendon tear (N=79) and patients with partial RC tendon tear (N=78). We evaluated different modeling approaches. The performance of the models was evaluated by calculating the Dice coefficient on the hold out test set. RESULTS The best-performing model's median Dice coefficient was measured to be 89% (Q1:85%, Q3:96%) for the supraspinatus, 86% (Q1:82%, Q3:88%) for the subscapularis, 86% (Q1:82%, Q3:90%) for the infraspinatus, and 78% (Q1:70%, Q3:81%) for the teres minor muscle, indicating a satisfactory level of accuracy in the model's predictions. CONCLUSION Our computational models demonstrated the capability to delineate RC muscles with a level of precision akin to that of experienced radiologists. As hypothesized, the proposed algorithm exhibited superior performance when segmenting muscles with well-defined boundaries, including the supraspinatus, subscapularis, and infraspinatus muscles.
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Affiliation(s)
- Ehsan Alipour
- Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology-Roosevelt Clinic, 4245 Roosevelt Way NE, Box, Seattle, WA, 354755, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Majid Chalian
- Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology-Roosevelt Clinic, 4245 Roosevelt Way NE, Box, Seattle, WA, 354755, USA.
| | - Atefe Pooyan
- Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology-Roosevelt Clinic, 4245 Roosevelt Way NE, Box, Seattle, WA, 354755, USA
| | - Arash Azhideh
- Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology-Roosevelt Clinic, 4245 Roosevelt Way NE, Box, Seattle, WA, 354755, USA
| | - Firoozeh Shomal Zadeh
- Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology-Roosevelt Clinic, 4245 Roosevelt Way NE, Box, Seattle, WA, 354755, USA
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Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
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Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
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11
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Rajabzadeh-Oghaz H, Kumar V, Berry DB, Singh A, Schoch BS, Aibinder WR, Gobbato B, Polakovic S, Elwell J, Roche CP. Impact of Deltoid Computer Tomography Image Data on the Accuracy of Machine Learning Predictions of Clinical Outcomes after Anatomic and Reverse Total Shoulder Arthroplasty. J Clin Med 2024; 13:1273. [PMID: 38592118 PMCID: PMC10931952 DOI: 10.3390/jcm13051273] [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: 01/19/2024] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Despite the importance of the deltoid to shoulder biomechanics, very few studies have quantified the three-dimensional shape, size, or quality of the deltoid muscle, and no studies have correlated these measurements to clinical outcomes after anatomic (aTSA) and/or reverse (rTSA) total shoulder arthroplasty in any statistically/scientifically relevant manner. Methods: Preoperative computer tomography (CT) images from 1057 patients (585 female, 469 male; 799 primary rTSA and 258 primary aTSA) of a single platform shoulder arthroplasty prosthesis (Equinoxe; Exactech, Inc., Gainesville, FL) were analyzed in this study. A machine learning (ML) framework was used to segment the deltoid muscle for 1057 patients and quantify 15 different muscle characteristics, including volumetric (size, shape, etc.) and intensity-based Hounsfield (HU) measurements. These deltoid measurements were correlated to postoperative clinical outcomes and utilized as inputs to train/test ML algorithms used to predict postoperative outcomes at multiple postoperative timepoints (1 year, 2-3 years, and 3-5 years) for aTSA and rTSA. Results: Numerous deltoid muscle measurements were demonstrated to significantly vary with age, gender, prosthesis type, and CT image kernel; notably, normalized deltoid volume and deltoid fatty infiltration were demonstrated to be relevant to preoperative and postoperative clinical outcomes after aTSA and rTSA. Incorporating deltoid image data into the ML models improved clinical outcome prediction accuracy relative to ML algorithms without image data, particularly for the prediction of abduction and forward elevation after aTSA and rTSA. Analyzing ML feature importance facilitated rank-ordering of the deltoid image measurements relevant to aTSA and rTSA clinical outcomes. Specifically, we identified that deltoid shape flatness, normalized deltoid volume, deltoid voxel skewness, and deltoid shape sphericity were the most predictive image-based features used to predict clinical outcomes after aTSA and rTSA. Many of these deltoid measurements were found to be more predictive of aTSA and rTSA postoperative outcomes than patient demographic data, comorbidity data, and diagnosis data. Conclusions: While future work is required to further refine the ML models, which include additional shoulder muscles, like the rotator cuff, our results show promise that the developed ML framework can be used to evolve traditional CT-based preoperative planning software into an evidence-based ML clinical decision support tool.
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Affiliation(s)
| | - Vikas Kumar
- Exactech, Inc., Gainesville, FL 32653, USA; (H.R.-O.); (V.K.); (S.P.); (J.E.)
| | - David B. Berry
- Department of Orthopedic Surgery, University of California San Diego, San Diego, CA 92093, USA; (D.B.B.); (A.S.)
| | - Anshu Singh
- Department of Orthopedic Surgery, University of California San Diego, San Diego, CA 92093, USA; (D.B.B.); (A.S.)
| | | | - William R. Aibinder
- Department of Orthopedic Surgery, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Bruno Gobbato
- R. José Emmendoerfer, 1449—Nova Brasília, Jaraguá do Sul 89252-278, SC, Brazil;
| | - Sandrine Polakovic
- Exactech, Inc., Gainesville, FL 32653, USA; (H.R.-O.); (V.K.); (S.P.); (J.E.)
| | - Josie Elwell
- Exactech, Inc., Gainesville, FL 32653, USA; (H.R.-O.); (V.K.); (S.P.); (J.E.)
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12
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Oeding JF, Krych AJ, Pearle AD, Kelly BT, Kunze KN. Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation. Arthroscopy 2024:S0749-8063(24)00099-9. [PMID: 38325497 DOI: 10.1016/j.arthro.2024.01.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 01/21/2024] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledge and translational gaps in the literature to provide insight into specific areas for improvement as adoption of this technology continues. METHODS A comprehensive search of the literature was performed in December 2023 for articles regarding the use of DL in sports medicine. For each study, information regarding the joint of focus, specific anatomic structure/pathology to which DL was applied, imaging modality utilized, source of images used for model training and testing, data set size, model performance, and whether the DL model was externally validated was recorded. A numerical scale was used to rate each DL model's clinical impact, with 1 corresponding to proof-of-concept studies with little to no direct clinical impact and 5 corresponding to practice-changing clinical impact and ready for clinical deployment. RESULTS Fifty-five studies were identified, all of which were published within the past 5 years, while 82% were published within the past 3 years. Of the DL models identified, 84% were developed for classification tasks, 9% for automated measurements, and 7% for segmentation. A total of 62% of studies utilized magnetic resonance imaging as the imaging modality, 25% radiographs, and 7% ultrasound, while 1 study each used computed tomography, arthroscopic images, or arthroscopic video. Sixty-five percent of studies focused on the detection of tears (anterior cruciate ligament [ACL], rotator cuff [RC], and meniscus). The diagnostic performance of ACL tears, as determined by the area under the receiver operator curve (AUROC), ranged from 0.81 to 0.99 for ACL tears (excellent to near perfect), 0.83 to 0.94 for RC tears (excellent), and from 0.75 to 0.96 for meniscus tears (acceptable to excellent). In addition, 3 studies focused on detection of cartilage lesions had AUROC ranging from 0.90 to 0.92 (excellent performance). However, only 4 (7%) studies externally validated their models, suggesting that they may not be generalizable or may not perform well when applied to populations other than that used to develop the model. Finally, the mean clinical impact score was 2 (range, 1-3) on scale of 1 to 5, corresponding to limited clinical applicability. CONCLUSIONS DL models in orthopaedic sports medicine show generally excellent performance (high internal validity) but require external validation to facilitate clinical deployment. In addition, current models have low clinical applicability and fail to advance the field due to a focus on routine tasks and a narrow conceptual framework. LEVEL OF EVIDENCE Level IV, scoping review of Level I to IV studies.
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Affiliation(s)
- Jacob F Oeding
- Mayo Clinic Alix School of Medicine, Rochester, Minnesota, U.S.A
| | - Aaron J Krych
- Department of Orthopaedic Surgery, Mayo Clinic, Rochester, Minnesota, U.S.A
| | - Andrew D Pearle
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, U.S.A
| | - Bryan T Kelly
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, U.S.A
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, U.S.A..
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13
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Zhan H, Teng F, Liu Z, Yi Z, He J, Chen Y, Geng B, Xia Y, Wu M, Jiang J. Artificial Intelligence Aids Detection of Rotator Cuff Pathology: A Systematic Review. Arthroscopy 2024; 40:567-578. [PMID: 37355191 DOI: 10.1016/j.arthro.2023.06.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 05/28/2023] [Accepted: 06/01/2023] [Indexed: 06/26/2023]
Abstract
PURPOSE To determine the model performance of artificial intelligence (AI) in detecting rotator cuff pathology using different imaging modalities and to compare capability with physicians in clinical scenarios. METHODS The review followed the PRISMA guidelines and was registered on PROSPERO. The criteria were as follows: 1) studies on the application of AI in detecting rotator cuff pathology using medical images, and 2) studies on smart devices for assisting in diagnosis were excluded. The following data were extracted and recorded: statistical characteristics, input features, AI algorithms used, sample sizes of training and testing sets, and model performance. The data extracted from the included studies were narratively reviewed. RESULTS A total of 14 articles, comprising 23,119 patients, met the inclusion and exclusion criteria. The pooled mean age of the patients was 56.7 years, and the female rate was 56.1%. The area under the curve (AUC) of the algorithmic model to detect rotator cuff pathology from ultrasound images, MRI images, and radiographic series ranged from 0.789 to 0.950, 0.844 to 0.943, and 0.820 to 0.830, respectively. Notably, 1 of the studies reported that AI models based on ultrasound images demonstrated a diagnostic performance similar to that of radiologists. Another comparative study demonstrated that AI models using MRI images exhibited greater accuracy and specificity compared to orthopedic surgeons in the diagnosis of rotator cuff pathology, albeit not in sensitivity. CONCLUSIONS The detection of rotator cuff pathology has been significantly aided by the exceptional performance of AI models. In particular, these models are equally adept as musculoskeletal radiologists in using ultrasound to diagnose rotator cuff pathology. Furthermore, AI models exhibit statistically superior levels of accuracy and specificity when using MRI to diagnose rotator cuff pathology, albeit with no marked difference in sensitivity, in comparison to orthopaedic surgeons. LEVEL OF EVIDENCE Level III, systematic review of Level III studies.
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Affiliation(s)
- Hongwei Zhan
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Fei Teng
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Zhongcheng Liu
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Zhi Yi
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Jinwen He
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Yi Chen
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Bin Geng
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Yayi Xia
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China.
| | - Meng Wu
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
| | - Jin Jiang
- Lanzhou University Second Hospital, Orthopaedic Clinical Research Center of Gansu Province, Intelligent Orthopaedic Industry Technology Center of Gansu Province, Lanzhou Gansu, China
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14
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Tang R, Li Z, Jiang L, Jiang J, Zhao B, Cui L, Zhou G, Chen X, Jiang D. Development and Clinical Application of Artificial Intelligence Assistant System for Rotator Cuff Ultrasound Scanning. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:251-257. [PMID: 38042717 DOI: 10.1016/j.ultrasmedbio.2023.10.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/19/2023] [Accepted: 10/24/2023] [Indexed: 12/04/2023]
Abstract
OBJECTIVE We developed an intelligent assistance system for shoulder ultrasound imaging, incorporating deep-learning algorithms to facilitate standard plane recognition and automatic tissue segmentation of the rotator cuff and its surrounding structures. We evaluated the system's performance using a dedicated data set of rotator cuff ultrasound images to assess its feasibility in clinical practice. METHODS To fulfill the system's primary functions, we designed a standard plane recognition module based on the ResNet50 network and an automatic tissue segmentation module using the Mask R-CNN model. The modules were trained on carefully curated data sets. The standard plane recognition module automatically identifies a specific standard plane based on the ultrasound image characteristics. The automatic tissue segmentation module effectively delineates and segments anatomical structures within the identified standard plane. RESULTS With the use of 59,265 shoulder joint ultrasound images, the standard plane recognition model achieved an impressive recognition accuracy of 94.9% in the test set, with an average precision rate of 96.4%, recall rate of 95.4% and F1 score of 95.9%. The automatic tissue segmentation model, tested on 1886 images, exhibited a commendable average intersection over union value of 96.2%, indicating robustness and accuracy. The model achieved mean intersection over union values exceeding 90.0% for all standard planes, indicating its effectiveness in precisely delineating the anatomical structures. CONCLUSION Our shoulder joint musculoskeletal intelligence system swiftly and accurately identifies standard planes and performs automatic tissue segmentation.
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Affiliation(s)
- Rui Tang
- Department of Ultrasound, Peking University Third Hospital, Beijing, China; Peking University Health Science Center Institute of Medical Technology, Beijing, China
| | - Zhiqiang Li
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Ling Jiang
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Jie Jiang
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Bo Zhao
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Ligang Cui
- Department of Ultrasound, Peking University Third Hospital, Beijing, China.
| | - Guoyi Zhou
- Sonoscape Medical Corporation, Shenzhen, China
| | - Xin Chen
- Sonoscape Medical Corporation, Shenzhen, China
| | - Daimin Jiang
- Sonoscape Medical Corporation(Wuhan), Wuhan, China
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15
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Croci E, Hess H, Warmuth F, Künzler M, Börlin S, Baumgartner D, Müller AM, Gerber K, Mündermann A. Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence. Eur Radiol 2024; 34:270-278. [PMID: 37566272 PMCID: PMC10791975 DOI: 10.1007/s00330-023-10082-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/21/2023] [Accepted: 06/28/2023] [Indexed: 08/12/2023]
Abstract
OBJECTIVE Patients with rotator cuff tears present often with glenohumeral joint instability. Assessing anatomic angles and shoulder kinematics from fluoroscopy requires labelling of specific landmarks in each image. This study aimed to develop an artificial intelligence model for automatic landmark detection from fluoroscopic images for motion tracking of the scapula and humeral head. MATERIALS AND METHODS Fluoroscopic images were acquired for both shoulders of 25 participants (N = 12 patients with unilateral rotator cuff tear, 6 men, mean (standard deviation) age: 63.7 ± 9.7 years; 13 asymptomatic subjects, 7 men, 58.2 ± 8.9 years) during a 30° arm abduction and adduction movement in the scapular plane with and without handheld weights of 2 and 4 kg. A 3D full-resolution convolutional neural network (nnU-Net) was trained to automatically locate five landmarks (glenohumeral joint centre, humeral shaft, inferior and superior edges of the glenoid and most lateral point of the acromion) and a calibration sphere. RESULTS The nnU-Net was trained with ground-truth data from 6021 fluoroscopic images of 40 shoulders and tested with 1925 fluoroscopic images of 10 shoulders. The automatic landmark detection algorithm achieved an accuracy above inter-rater variability and slightly below intra-rater variability. All landmarks and the calibration sphere were located within 1.5 mm, except the humeral landmark within 9.6 mm, but differences in abduction angles were within 1°. CONCLUSION The proposed algorithm detects the desired landmarks on fluoroscopic images with sufficient accuracy and can therefore be applied to automatically assess shoulder motion, scapular rotation or glenohumeral translation in the scapular plane. CLINICAL RELEVANCE STATEMENT This nnU-net algorithm facilitates efficient and objective identification and tracking of anatomical landmarks on fluoroscopic images necessary for measuring clinically relevant anatomical configuration (e.g. critical shoulder angle) and enables investigation of dynamic glenohumeral joint stability in pathological shoulders. KEY POINTS • Anatomical configuration and glenohumeral joint stability are often a concern after rotator cuff tears. • Artificial intelligence applied to fluoroscopic images helps to identify and track anatomical landmarks during dynamic movements. • The developed automatic landmark detection algorithm optimised the labelling procedures and is suitable for clinical application.
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Affiliation(s)
- Eleonora Croci
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland.
| | - Hanspeter Hess
- School for Biomedical and Precision Engineering, University of Bern, Bern, Switzerland
| | - Fabian Warmuth
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Marina Künzler
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Sean Börlin
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Daniel Baumgartner
- IMES Institute of Mechanical Systems, Zurich University of Applied Sciences, Winterthur, Switzerland
| | - Andreas Marc Müller
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Kate Gerber
- School for Biomedical and Precision Engineering, University of Bern, Bern, Switzerland
| | - Annegret Mündermann
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
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16
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de Marinis R, Marigi EM, Atwan Y, Yang L, Oeding JF, Gupta P, Pareek A, Sanchez-Sotelo J, Sperling JW. Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what's coming next. JSES REVIEWS, REPORTS, AND TECHNIQUES 2023; 3:447-453. [PMID: 37928999 PMCID: PMC10625013 DOI: 10.1016/j.xrrt.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Background Artificial intelligence (AI) is a continuously expanding field with the potential to transform a variety of industries-including health care-by providing automation, efficiency, precision, accuracy, and decision-making support for simple and complex tasks. Basic knowledge of the key features as well as limitations of AI is paramount to understand current developments in this field and to successfully apply them to shoulder surgery. The purpose of the present review is to provide an overview of AI within orthopedics and shoulder surgery exploring current and forthcoming AI applications. Methods PubMed and Scopus databases were searched to provide a narrative review of the most relevant literature on AI applications in shoulder surgery. Results Despite the enormous clinical and research potential of AI, orthopedic surgery has been a relatively late adopter of AI technologies. Image evaluation, surgical planning, aiding decision-making, and facilitating patient evaluations over time are some of the current areas of development with enormous opportunities to improve surgical practice, research, and education. Furthermore, the advancement of AI-driven strategies has the potential to create a more efficient medical system that may reduce the overall cost of delivering and implementing quality health care for patients with shoulder pathology. Conclusion AI is an expanding field with the potential for broad clinical and research applications in orthopedic surgery. Many challenges still need to be addressed to fully leverage the potential of AI to clinical practice and research such as privacy issues, data ownership, and external validation of the proposed models.
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Affiliation(s)
- Rodrigo de Marinis
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
- Department of Orthopedic Surgery, Pontificia Universidad Católica de Chile, Santiago, Chile
- Shoulder and Elbow Unit, Hospital Dr. Sótero del Rio, Santiago, Chile
| | - Erick M. Marigi
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Yousif Atwan
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Linjun Yang
- Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, MN, USA
| | - Jacob F. Oeding
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Puneet Gupta
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA
| | | | - John W. Sperling
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
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17
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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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18
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Rodriguez HC, Rust B, Hansen PY, Maffulli N, Gupta M, Potty AG, Gupta A. Artificial Intelligence and Machine Learning in Rotator Cuff Tears. Sports Med Arthrosc Rev 2023; 31:67-72. [PMID: 37976127 DOI: 10.1097/jsa.0000000000000371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.
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Affiliation(s)
- Hugo C Rodriguez
- Department of Orthopaedic Surgery, Larkin Community Hospital, South Miami
- Department of Orthopaedic Surgery, Hospital for Special Surgery Florida, West Palm Beach
| | - Brandon Rust
- Nova Southeastern University, Dr. Kiran Patel College of Osteopathic Medicine, Fort Lauderdale
| | - Payton Yerke Hansen
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL
| | - Nicola Maffulli
- Department of Musculoskeletal Disorders, School of Medicine and Surgery, University of Salerno, Fisciano
- San Giovanni di Dio e Ruggi D'Aragona Hospital "Clinica Ortopedica" Department, Hospital of Salerno, Salerno, Italy
- Barts and the London School of Medicine and Dentistry, Centre for Sports and Exercise Medicine, Queen Mary University of London, London
- School of Pharmacy and Bioengineering, Keele University School of Medicine, Stoke on Trent, UK
| | - Manu Gupta
- Polar Aesthetics Dental & Cosmetic Centre, Noida, Uttar Pradesh
| | - Anish G Potty
- South Texas Orthopaedic Research Institute (STORI Inc.), Laredo, TX
| | - Ashim Gupta
- Regenerative Orthopaedics, Noida, India
- South Texas Orthopaedic Research Institute (STORI Inc.), Laredo, TX
- Future Biologics
- BioIntegrate, Lawrenceville, GA
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Cho SH, Kim YS. Prediction of Retear After Arthroscopic Rotator Cuff Repair Based on Intraoperative Arthroscopic Images Using Deep Learning. Am J Sports Med 2023; 51:2824-2830. [PMID: 37565449 DOI: 10.1177/03635465231189201] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
BACKGROUND It is challenging to predict retear after arthroscopic rotator cuff repair (ARCR). The usefulness of arthroscopic intraoperative images as predictors of the ARCR prognosis has not been analyzed. PURPOSE To evaluate the usefulness of arthroscopic images for the prediction of retear after ARCR using deep learning (DL) algorithms. STUDY DESIGN Cohort study (Diagnosis); Level of evidence, 2. METHODS In total, 1394 arthroscopic intraoperative images were retrospectively obtained from 580 patients. Repaired tendon integrity was evaluated using magnetic resonance imaging performed within 2 years after surgery. Images obtained immediately after ARCR were included. We used 3 DL architectures to predict retear based on arthroscopic images. Three pretrained DL algorithms (VGG16, DenseNet, and Xception) were used for transfer learning. Training and test sets were split into 8:2. Threefold stratified validation was used to fine-tune the hyperparameters using the training data set. The validation results of each fold were evaluated. The performance of each model in the test set was evaluated in terms of accuracy, area under the receiver operating characteristic curve (AUC), F1-score, sensitivity, and specificity. RESULTS In total, 1138 and 256 arthroscopic images were obtained from 514 patients and 66 patients in the nonretear and retear groups, respectively. The mean validation accuracy of each model was 83% for VGG16, 89% for Xception, and 91% for DenseNet. The accuracy for the test set was 76% for VGG16, 87% for Xception, and 91% for DenseNet. The AUC was highest for DenseNet (0.92); it was 0.83 for VGG16 and 0.91 for Xception. For the test set, the specificity and sensitivity were 0.93 and 0.84 for DenseNet, 0.89 and 0.84 for Xception, and 0.70 and 0.80 for VGG16, respectively. CONCLUSION The application of DL algorithms to intraoperative arthroscopic images has demonstrated a high level of accuracy in predicting retear occurrences.
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Affiliation(s)
- Sung-Hyun Cho
- Department of Orthopedic Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yang-Soo Kim
- Department of Orthopedic Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
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Saavedra JP, Droppelmann G, García N, Jorquera C, Feijoo F. High-accuracy detection of supraspinatus fatty infiltration in shoulder MRI using convolutional neural network algorithms. Front Med (Lausanne) 2023; 10:1070499. [PMID: 37305126 PMCID: PMC10248442 DOI: 10.3389/fmed.2023.1070499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Background The supraspinatus muscle fatty infiltration (SMFI) is a crucial MRI shoulder finding to determine the patient's prognosis. Clinicians have used the Goutallier classification to diagnose it. Deep learning algorithms have been demonstrated to have higher accuracy than traditional methods. Aim To train convolutional neural network models to categorize the SMFI as a binary diagnosis based on Goutallier's classification using shoulder MRIs. Methods A retrospective study was performed. MRI and medical records from patients with SMFI diagnosis from January 1st, 2019, to September 20th, 2020, were selected. 900 T2-weighted, Y-view shoulder MRIs were evaluated. The supraspinatus fossa was automatically cropped using segmentation masks. A balancing technique was implemented. Five binary classification classes were developed into two as follows, A: 0, 1 v/s 3, 4; B: 0, 1 v/s 2, 3, 4; C: 0, 1 v/s 2; D: 0, 1, 2, v/s 3, 4; E: 2 v/s 3, 4. The VGG-19, ResNet-50, and Inception-v3 architectures were trained as backbone classifiers. An average of three 10-fold cross-validation processes were developed to evaluate model performance. AU-ROC, sensitivity, and specificity with 95% confidence intervals were used. Results Overall, 606 shoulders MRIs were analyzed. The Goutallier distribution was presented as follows: 0 = 403; 1 = 114; 2 = 51; 3 = 24; 4 = 14. Case A, VGG-19 model demonstrated an AU-ROC of 0.991 ± 0.003 (accuracy, 0.973 ± 0.006; sensitivity, 0.947 ± 0.039; specificity, 0.975 ± 0.006). B, VGG-19, 0.961 ± 0.013 (0.925 ± 0.010; 0.847 ± 0.041; 0.939 ± 0.011). C, VGG-19, 0.935 ± 0.022 (0.900 ± 0.015; 0.750 ± 0.078; 0.914 ± 0.014). D, VGG-19, 0.977 ± 0.007 (0.942 ± 0.012; 0.925 ± 0.056; 0.942 ± 0.013). E, VGG-19, 0.861 ± 0.050 (0.779 ± 0.054; 0.706 ± 0.088; 0.831 ± 0.061). Conclusion Convolutional neural network models demonstrated high accuracy in MRIs SMFI diagnosis.
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Affiliation(s)
- Juan Pablo Saavedra
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Guillermo Droppelmann
- Research Center on Medicine, Exercise, Sport and Health, MEDS Clinic, Santiago, Chile
- Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Murcia, Spain
- Principles and Practice of Clinical Research (PPCR), Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Nicolás García
- Research Center on Medicine, Exercise, Sport and Health, MEDS Clinic, Santiago, Chile
| | - Carlos Jorquera
- Facultad de Ciencias, Escuela de Nutrición y Dietética, Universidad Mayor, Santiago, Chile
| | - Felipe Feijoo
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
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Gupta P, Haeberle HS, Zimmer ZR, Levine WN, Williams RJ, Ramkumar PN. Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review. JSES REVIEWS, REPORTS, AND TECHNIQUES 2023; 3:189-200. [PMID: 37588443 PMCID: PMC10426484 DOI: 10.1016/j.xrrt.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature. Methods A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff). All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both). Results A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation. Conclusion Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications.
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Affiliation(s)
- Puneet Gupta
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Heather S. Haeberle
- Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Zachary R. Zimmer
- Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - William N. Levine
- Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, USA
| | - Riley J. Williams
- Institute for Cartilage Repair, Hospital for Special Surgery, New York, NY, USA
| | - Prem N. Ramkumar
- Institute for Cartilage Repair, Hospital for Special Surgery, New York, NY, USA
- Long Beach Orthopaedic Institute, Long Beach, CA, USA
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Familiari F, Galasso O, Massazza F, Mercurio M, Fox H, Srikumaran U, Gasparini G. Artificial Intelligence in the Management of Rotator Cuff Tears. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16779. [PMID: 36554660 PMCID: PMC9779744 DOI: 10.3390/ijerph192416779] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Technological innovation is a key component of orthopedic surgery. Artificial intelligence (AI), which describes the ability of computers to process massive data and "learn" from it to produce outputs that mirror human cognition and problem solving, may become an important tool for orthopedic surgeons in the future. AI may be able to improve decision making, both clinically and surgically, via integrating additional data-driven problem solving into practice. The aim of this article will be to review the current applications of AI in the management of rotator cuff tears. The article will discuss various stages of the clinical course: predictive models and prognosis, diagnosis, intraoperative applications, and postoperative care and rehabilitation. Throughout the article, which is a review in terms of study design, we will introduce the concept of AI in rotator cuff tears and provide examples of how these tools can impact clinical practice and patient care. Though many advancements in AI have been made regarding evaluating rotator cuff tears-particularly in the realm of diagnostic imaging-further advancements are required before they become a regular facet of daily clinical practice.
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Affiliation(s)
- Filippo Familiari
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Olimpio Galasso
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Federica Massazza
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Michele Mercurio
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
| | - Henry Fox
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Uma Srikumaran
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Giorgio Gasparini
- Department of Orthopaedic and Trauma Surgery, “Mater Domini” University Hospital, “Magna Græcia” University, 88100 Catanzaro, Italy
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23
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Measuring the critical shoulder angle on radiographs: an accurate and repeatable deep learning model. Skeletal Radiol 2022; 51:1873-1878. [PMID: 35347406 DOI: 10.1007/s00256-022-04041-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE Since the critical shoulder angle (CSA) is considered a risk factor for shoulder pathology and the intra- and inter-rater variabilities in its calculation are not negligible, we developed a deep learning model that calculates it automatically and accurately. METHODS We used a dataset of 8467 anteroposterior x-ray images of the shoulder annotated with 3 landmarks of interest. A Convolutional Neural Network model coupled with a spatial to numerical transform (DSNT) layer was used to predict the landmark coordinates from which the CSA was calculated. The performances were evaluated by calculating the Euclidean distance between the ground truth coordinates and the predicted ones normalized with respect to the distance between the first and the second points, and the error between the CSA angle measured by a human observer and the predicted one. RESULTS Regarding the normalized point distances, we obtained a median error of 2.9%, 2.5%, and 2% for the three points among the entire set. Considering CSA calculations, the median errors were 1° (standard deviation 1.2°), 0.88° (standard deviation 0.87°), and 0.99° (standard deviation 1°) for angles below 30°, between 30° and 35°, and above 35°, respectively. DISCUSSION These results demonstrate that the model has the potential to be used in clinical settings where the replicability is important. The reported standard error of the CSA measurement is greater than 2° that is above the median error of our model, indicating a potential accuracy sufficient to be used in a clinical setting.
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Diagnostic Performance for Detecting Bone Marrow Edema of the Hip on Dual-Energy CT: Deep Learning Model vs. Musculoskeletal Physicians and Radiologists. Eur J Radiol 2022; 152:110337. [DOI: 10.1016/j.ejrad.2022.110337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/30/2022] [Accepted: 04/21/2022] [Indexed: 02/03/2023]
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Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12020681] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.
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Deep learning-based quantification of temporalis muscle has prognostic value in patients with glioblastoma. Br J Cancer 2021; 126:196-203. [PMID: 34848854 PMCID: PMC8770629 DOI: 10.1038/s41416-021-01590-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/25/2021] [Accepted: 10/06/2021] [Indexed: 01/19/2023] Open
Abstract
Background Glioblastoma is the commonest malignant brain tumour. Sarcopenia is associated with worse cancer survival, but manually quantifying muscle on imaging is time-consuming. We present a deep learning-based system for quantification of temporalis muscle, a surrogate for skeletal muscle mass, and assess its prognostic value in glioblastoma. Methods A neural network for temporalis segmentation was trained with 366 MRI head images from 132 patients from 4 different glioblastoma data sets and used to quantify muscle cross-sectional area (CSA). Association between temporalis CSA and survival was determined in 96 glioblastoma patients from internal and external data sets. Results The model achieved high segmentation accuracy (Dice coefficient 0.893). Median age was 55 and 58 years and 75.6 and 64.7% were males in the in-house and TCGA-GBM data sets, respectively. CSA was an independently significant predictor for survival in both the in-house and TCGA-GBM data sets (HR 0.464, 95% CI 0.218–0.988, p = 0.046; HR 0.466, 95% CI 0.235–0.925, p = 0.029, respectively). Conclusions Temporalis CSA is a prognostic marker in patients with glioblastoma, rapidly and accurately assessable with deep learning. We are the first to show that a head/neck muscle-derived sarcopenia metric generated using deep learning is associated with oncological outcomes and one of the first to show deep learning-based muscle quantification has prognostic value in cancer.
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Ro K, Kim JY, Park H, Cho BH, Kim IY, Shim SB, Choi IY, Yoo JC. Deep-learning framework and computer assisted fatty infiltration analysis for the supraspinatus muscle in MRI. Sci Rep 2021; 11:15065. [PMID: 34301978 PMCID: PMC8302634 DOI: 10.1038/s41598-021-93026-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 06/02/2021] [Indexed: 02/07/2023] Open
Abstract
Occupation ratio and fatty infiltration are important parameters for evaluating patients with rotator cuff tears. We analyzed the occupation ratio using a deep-learning framework and studied the fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. To calculate the amount of fatty infiltration of the supraspinatus muscle using an automated region-based Otsu thresholding technique. The mean Dice similarity coefficient, accuracy, sensitivity, specificity, and relative area difference for the segmented lesion, measuring the similarity of clinician assessment and that of a deep neural network, were 0.97, 99.84, 96.89, 99.92, and 0.07, respectively, for the supraspinatus fossa and 0.94, 99.89, 93.34, 99.95, and 2.03, respectively, for the supraspinatus muscle. The fatty infiltration measure using the Otsu thresholding method significantly differed among the Goutallier grades (Grade 0; 0.06, Grade 1; 4.68, Grade 2; 20.10, Grade 3; 42.86, Grade 4; 55.79, p < 0.0001). The occupation ratio and fatty infiltration using Otsu thresholding demonstrated a moderate negative correlation (ρ = - 0.75, p < 0.0001). This study included 240 randomly selected patients who underwent shoulder magnetic resonance imaging (MRI) from January 2015 to December 2016. We used a fully convolutional deep-learning algorithm to quantitatively detect the fossa and muscle regions by measuring the occupation ratio of the supraspinatus muscle. Fatty infiltration was objectively evaluated using the Otsu thresholding method. The proposed convolutional neural network exhibited fast and accurate segmentation of the supraspinatus muscle and fossa from shoulder MRI, allowing automatic calculation of the occupation ratio. Quantitative evaluation using a modified Otsu thresholding method can be used to calculate the proportion of fatty infiltration in the supraspinatus muscle. We expect that this will improve the efficiency and objectivity of diagnoses by quantifying the index used for shoulder MRI.
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Affiliation(s)
- Kyunghan Ro
- Gangnambon Research Institute, Gangnambon Orthopaedic Cinic, Seoul, Republic of Korea
| | - Joo Young Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Heeseol Park
- Department of Orthopaedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Baek Hwan Cho
- Medical AI Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Medical Device Management and Research, SAIHST, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - In Young Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Seung Bo Shim
- Department of Orthopaedic Surgery, Yonsei Thebaro Hospital, Seoul, Republic of Korea
| | - In Young Choi
- Department of Radiology, Korea University Ansan Hospital, Korea University, Ansan-si, Gyeonggi-do, Republic of Korea
| | - Jae Chul Yoo
- Department of Orthopaedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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