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Bharadwaj UU, Chin CT, Majumdar S. Practical Applications of Artificial Intelligence in Spine Imaging: A Review. Radiol Clin North Am 2024; 62:355-370. [PMID: 38272627 DOI: 10.1016/j.rcl.2023.10.005] [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: 01/27/2024]
Abstract
Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologies. AI-based approaches may improve imaging efficiency, diagnostic accuracy, and interpretation, which is essential for positive patient outcomes. This review explores AI algorithms, techniques, and applications in spine imaging, highlighting diagnostic impact and challenges with future directions for integrating AI into spine imaging workflow.
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Affiliation(s)
- Upasana Upadhyay Bharadwaj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA
| | - Cynthia T Chin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143, USA.
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA
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Wang X, Zhou D, Kong Y, Cheng N, Gao M, Zhang G, Ma J, Chen Y, Ge S. Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures. EJNMMI Res 2023; 13:89. [PMID: 37819414 PMCID: PMC10567613 DOI: 10.1186/s13550-023-01038-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Vertebral compression fractures (VCFs) are common clinical problems that arise from various reasons. The differential diagnosis of benign and malignant VCFs is challenging. This study was designed to develop and validate a radiomics model to predict benign and malignant VCFs with 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT). RESULTS Twenty-six features (9 PET features and 17 CT features) and eight clinical variables (age, SUVmax, SUVpeak, SULmax, SULpeak, osteolytic destruction, fracture line, and appendices/posterior vertebrae involvement) were ultimately selected. The area under the curve (AUCs) of the radiomics and clinical-radiomics models were significantly different from that of the clinical model in both the training group (0.986, 0.987 vs. 0.884, p < 0.05) and test group (0.962, 0.948 vs. 0.858, p < 0.05), while there was no significant difference between the radiomics model and clinical-radiomics model (p > 0.05). The accuracies of the radiomics and clinical-radiomics models were 94.0% and 95.0% in the training group and 93.2% and 93.2% in the test group, respectively. The three models all showed good calibration (Hosmer-Lemeshow test, p > 0.05). According to the decision curve analysis (DCA), the radiomics model and clinical-radiomics model exhibited higher overall net benefit than the clinical model. CONCLUSIONS The PET/CT-based radiomics and clinical-radiomics models showed good performance in distinguishing between malignant and benign VCFs. The radiomics method may be valuable for treatment decision-making.
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Affiliation(s)
- Xun Wang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Dandan Zhou
- Big Data and Artificial Intelligence, Jining Polytechnic, Jinyu Road, Jining, Shandong, China
| | - Yu Kong
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Nan Cheng
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Ming Gao
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Guqing Zhang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Junli Ma
- Department of Radiation Oncology, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Yueqin Chen
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China.
| | - Shuang Ge
- Department of Radiation Oncology, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China.
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Perez-Diaz M. Use of systems with deep learning and machine learning for the detection and classification of malignant vs. benign spinal fractures with MRI: can deep/machine learning help us further for detection and characterization? Eur Radiol 2023; 33:5058-5059. [PMID: 37233743 DOI: 10.1007/s00330-023-09760-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/21/2023] [Accepted: 03/27/2023] [Indexed: 05/27/2023]
Affiliation(s)
- Marlen Perez-Diaz
- Departamento de Automática, Universidad Central Marta Abreu de Las Villas, Villa Clara, Santa Clara, Cuba.
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