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Dreizin D, Staziaki PV, Khatri GD, Beckmann NM, Feng Z, Liang Y, Delproposto ZS, Klug M, Spann JS, Sarkar N, Fu Y. Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emerg Radiol 2023; 30:251-265. [PMID: 36917287 PMCID: PMC10640925 DOI: 10.1007/s10140-023-02120-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty. PURPOSE To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness. METHODS Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends. RESULTS A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network-based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from < 100 to > 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human-computer interaction. The USA and China had the highest research output and frequency of research funding. CONCLUSIONS Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Pedro V Staziaki
- Cardiothoracic Imaging, Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Garvit D Khatri
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Nicholas M Beckmann
- Memorial Hermann Orthopedic & Spine Hospital, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Zhaoyong Feng
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yuanyuan Liang
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Zachary S Delproposto
- Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | - J Stephen Spann
- Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
| | - Nathan Sarkar
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yunting Fu
- Health Sciences and Human Services Library, University of Maryland, Baltimore, Baltimore, MD, USA
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Ehsaeyan E. An efficient image segmentation method based on expectation maximization and Salp swarm algorithm. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-31. [PMID: 37362643 PMCID: PMC10061417 DOI: 10.1007/s11042-023-15149-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 11/14/2022] [Accepted: 03/13/2023] [Indexed: 06/28/2023]
Abstract
Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and Salp Swarm Algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods.
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Affiliation(s)
- Ehsan Ehsaeyan
- Electrical Engineering Department, Sirjan University of Technology, Sirjan, Iran
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Ryu SM, Shin K, Shin SW, Lee SH, Seo SM, Cheon SU, Ryu SA, Kim MJ, Kim H, Doh CH, Choi YR, Kim N. Automated diagnosis of flatfoot using cascaded convolutional neural network for angle measurements in weight-bearing lateral radiographs. Eur Radiol 2023:10.1007/s00330-023-09442-1. [PMID: 36856842 DOI: 10.1007/s00330-023-09442-1] [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: 10/21/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 03/02/2023]
Abstract
OBJECTIVES Diagnosis of flatfoot using a radiograph is subject to intra- and inter-observer variabilities. Here, we developed a cascade convolutional neural network (CNN)-based deep learning model (DLM) for an automated angle measurement for flatfoot diagnosis using landmark detection. METHODS We used 1200 weight-bearing lateral foot radiographs from young adult Korean males for the model development. An experienced orthopedic surgeon identified 22 radiographic landmarks and measured three angles for flatfoot diagnosis that served as the ground truth (GT). Another orthopedic surgeon (OS) and a general physician (GP) independently identified the landmarks of the test dataset and measured the angles using the same method. External validation was performed using 100 and 17 radiographs acquired from a tertiary referral center and a public database, respectively. RESULTS The DLM showed smaller absolute average errors from the GT for the three angle measurements for flatfoot diagnosis compared with both human observers. Under the guidance of the DLM, the average errors of observers OS and GP decreased from 2.35° ± 3.01° to 1.55° ± 2.09° and from 1.99° ± 2.76° to 1.56° ± 2.19°, respectively (both p < 0.001). The total measurement time decreased from 195 to 135 min in observer OS and from 205 to 155 min in observer GP. The absolute average errors of the DLM in the external validation sets were similar or superior to those of human observers in the original test dataset. CONCLUSIONS Our CNN model had significantly better accuracy and reliability than human observers in diagnosing flatfoot, and notably improved the accuracy and reliability of human observers. KEY POINTS • Development of deep learning model (DLM) that allows automated angle measurements for landmark detection based on 1200 weight-bearing lateral radiographs for diagnosing flatfoot. • Our DLM showed smaller absolute average errors for flatfoot diagnosis compared with two human observers. • Under the guidance of the model, the average errors of two human observers decreased and total measurement time also decreased from 195 to 135 min and from 205 to 155 min.
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Affiliation(s)
- Seung Min Ryu
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 26, Olympic-ro 43-gil, Songpa-gu, Seoul, 05506, Republic of Korea.,Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Keewon Shin
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 26, Olympic-ro 43-gil, Songpa-gu, Seoul, 05506, Republic of Korea
| | - Soo Wung Shin
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05506, Republic of Korea.,Department of Computer Science and Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Sun Ho Lee
- Department of Orthopedic Surgery, Chonnam National University Hospital, 42, Jebong-ro, Dong-gu, Gwangju, 61469, Republic of Korea
| | - Su Min Seo
- Department of Anesthesiology and Pain Medicine, Seoul Medical Center, 156, Sinnae-ro, Jungnang-gu, Seoul, 02053, Republic of Korea
| | - Seung-Uk Cheon
- Department of Anesthesiology and Pain Medicine, Seoul Medical Center, 156, Sinnae-ro, Jungnang-gu, Seoul, 02053, Republic of Korea
| | - Seung-Ah Ryu
- Department of Anesthesiology and Pain Medicine, Seoul Medical Center, 156, Sinnae-ro, Jungnang-gu, Seoul, 02053, Republic of Korea
| | - Min-Ju Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Hyunjung Kim
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 26, Olympic-ro 43-gil, Songpa-gu, Seoul, 05506, Republic of Korea
| | - Chang Hyun Doh
- Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Young Rak Choi
- Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Namkug Kim
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05506, Republic of Korea. .,Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 26, Olympic-ro 43-gil, Songpa-gu, Seoul, 05506, Republic of Korea.
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Fan X, Zhu Q, Tu P, Joskowicz L, Chen X. A review of advances in image-guided orthopedic surgery. Phys Med Biol 2023; 68. [PMID: 36595258 DOI: 10.1088/1361-6560/acaae9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
Orthopedic surgery remains technically demanding due to the complex anatomical structures and cumbersome surgical procedures. The introduction of image-guided orthopedic surgery (IGOS) has significantly decreased the surgical risk and improved the operation results. This review focuses on the application of recent advances in artificial intelligence (AI), deep learning (DL), augmented reality (AR) and robotics in image-guided spine surgery, joint arthroplasty, fracture reduction and bone tumor resection. For the pre-operative stage, key technologies of AI and DL based medical image segmentation, 3D visualization and surgical planning procedures are systematically reviewed. For the intra-operative stage, the development of novel image registration, surgical tool calibration and real-time navigation are reviewed. Furthermore, the combination of the surgical navigation system with AR and robotic technology is also discussed. Finally, the current issues and prospects of the IGOS system are discussed, with the goal of establishing a reference and providing guidance for surgeons, engineers, and researchers involved in the research and development of this area.
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Affiliation(s)
- Xingqi Fan
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Qiyang Zhu
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Puxun Tu
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.,Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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Ryu SM, Shin K, Shin SW, Lee SH, Seo SM, Cheon SU, Ryu SA, Kim JS, Ji S, Kim N. Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot. Comput Biol Med 2022; 148:105914. [DOI: 10.1016/j.compbiomed.2022.105914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/08/2022] [Accepted: 07/23/2022] [Indexed: 11/15/2022]
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Feng C, Zhou X, Wang H, He Y, Li Z, Tu C. Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study. Front Public Health 2022; 10:949366. [PMID: 35928480 PMCID: PMC9343683 DOI: 10.3389/fpubh.2022.949366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
Background As a research hotspot, deep learning has been continuously combined with various research fields in medicine. Recently, there is a growing amount of deep learning-based researches in orthopedics. This bibliometric analysis aimed to identify the hotspots of deep learning applications in orthopedics in recent years and infer future research trends. Methods We screened global publication on deep learning applications in orthopedics by accessing the Web of Science Core Collection. The articles and reviews were collected without language and time restrictions. Citespace was applied to conduct the bibliometric analysis of the publications. Results A total of 822 articles and reviews were finally retrieved. The analysis showed that the application of deep learning in orthopedics has great prospects for development based on the annual publications. The most prolific country is the USA, followed by China. University of California San Francisco, and Skeletal Radiology are the most prolific institution and journal, respectively. LeCun Y is the most frequently cited author, and Nature has the highest impact factor in the cited journals. The current hot keywords are convolutional neural network, classification, segmentation, diagnosis, image, fracture, and osteoarthritis. The burst keywords are risk factor, identification, localization, and surgery. The timeline viewer showed two recent research directions for bone tumors and osteoporosis. Conclusion Publications on deep learning applications in orthopedics have increased in recent years, with the USA being the most prolific. The current research mainly focused on classifying, diagnosing and risk predicting in osteoarthritis and fractures from medical images. Future research directions may put emphasis on reducing intraoperative risk, predicting the occurrence of postoperative complications, screening for osteoporosis, and identification and classification of bone tumors from conventional imaging.
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Affiliation(s)
- Chengyao Feng
- The Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xiaowen Zhou
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Hua Wang
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Yu He
- The Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Zhihong Li
- The Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Chao Tu
- The Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, China
- Hunan Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital of Central South University, Changsha, China
- *Correspondence: Chao Tu
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Xiao J, Xin Z, Fu X, Huang J, Zhang B, Yu H. Treatment of Fracture of the Calcaneus via Bone Axial X-Ray Image-Based Minimally Invasive Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3012589. [PMID: 35813425 PMCID: PMC9270132 DOI: 10.1155/2022/3012589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/09/2022] [Accepted: 06/13/2022] [Indexed: 11/17/2022]
Abstract
To discuss the values of two bone axial X-ray image-based minimally invasive approach surgeries in the diagnosis and treatment of fracture of the calcaneus, 80 patients diagnosed with fracture of the calcaneus by bone axial X-ray examination were selected and divided equally into the minimally invasive longitudinal approach (MILA) group (40 cases) and the sinus tarsal approach (STA) group (40 cases). Besides, the duration of operation, the incidence of complications, the time-to-start weight training, and the American Orthopaedic Foot and Ankle Society (AOFAS) foot function scoring system between the patients in the two groups were compared. The results showed that the duration of operation and incidence of complications among the patients in the MILA group (42.87 ± 5.12 minutes, 20%) were both superior to those among the patients in the STA group (60.43 ± 7.31 minutes, 32.5%). The time-to-start weight training in the MILA group was 5.2 weeks, which was obviously shorter than that in the STA group (5.7 weeks). The difference in AOFAS scores between the two groups was not significant. The walking pavement score in the MILA group (4.2 ± 0.37 points) was slightly higher than that in the STA group (3.3 ± 0.45 points), and the differences demonstrated statistical meaning (P < 0.05). To sum up, the bone axial X-ray image is an essential examination method of diagnosing fracture of the calcaneus. The two minimally invasive methods both showed good clinical therapeutic effects. The operation of MILA was relatively shorter with fewer complications and is worthy of being promoted as an effective treatment method of fracture of the calcaneus.
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Affiliation(s)
- Jie Xiao
- Department of Orthopedics, The Second Affiliated Hospital (Jiande Branch), Zhejiang University School of Medicine, Jiande, Hangzhou, 311600 Zhejiang, China
| | - Zengfeng Xin
- Department of Orthopedics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 311000 Zhejiang, China
| | - Xiaojun Fu
- Department of Orthopedics, The Second Affiliated Hospital (Jiande Branch), Zhejiang University School of Medicine, Jiande, Hangzhou, 311600 Zhejiang, China
| | - Jiaqi Huang
- Department of Orthopedics, The Second Affiliated Hospital (Jiande Branch), Zhejiang University School of Medicine, Jiande, Hangzhou, 311600 Zhejiang, China
| | - Bi Zhang
- Department of Orthopedics, The Second Affiliated Hospital (Jiande Branch), Zhejiang University School of Medicine, Jiande, Hangzhou, 311600 Zhejiang, China
| | - Haiping Yu
- Department of Orthopedics, The Second Affiliated Hospital (Jiande Branch), Zhejiang University School of Medicine, Jiande, Hangzhou, 311600 Zhejiang, China
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