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Chen YC, Li YT, Kuo PC, Cheng SJ, Chung YH, Kuo DP, Chen CY. Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography. Eur Radiol 2023; 33:5097-5106. [PMID: 36719495 DOI: 10.1007/s00330-023-09421-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 12/24/2022] [Accepted: 01/01/2023] [Indexed: 02/01/2023]
Abstract
OBJECTIVE This study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT). METHODS A total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient's bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation. RESULTS The model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05. CONCLUSION The combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening. KEY POINTS • This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.
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Affiliation(s)
- Yung-Chieh Chen
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Tien Li
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan
| | - Po-Chih Kuo
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Sho-Jen Cheng
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Hsiang Chung
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
| | - Duen-Pang Kuo
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan.
| | - Cheng-Yu Chen
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Radiology, National Defense Medical Center, Taipei, Taiwan
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Artificial intelligence used to diagnose osteoporosis from risk factors in clinical data and proposing sports protocols. Sci Rep 2022; 12:18330. [PMID: 36316387 PMCID: PMC9622877 DOI: 10.1038/s41598-022-23184-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 10/26/2022] [Indexed: 11/13/2022] Open
Abstract
Osteoporosis (OP) is characterized by diminished bone mass and deteriorating bone structure that increases the chance of fractures in the spine, hips, and wrists. In this paper, a novel data processing method of artificial intelligence (AI) is used for evaluating, predicting, and classifying OP risk factors in clinical data of men and women separately. Additionally, artificial intelligence was used to suggest the most appropriate sports programs for treatment. Data was obtained from dual-energy x-ray absorption scanning center of Ayatollah Kashani, Milad, and Khatam al-Anbia hospitals in Tehran, Iran. The subjects included 1224 men and women. Models were developed using decision tree, random forest (RF), k-nearest neighbor, support vector machine, gradient boosting (GB), Extra trees, Ada Boost (AB), and artificial neural network multilayer perceptron analysis to predict osteoporosis and to recommend sports programs. Data was divided into training (80%) and test dataset (20%). The results were obtained on a 20% test dataset. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. To predict healthy individuals, osteopenia and osteoporosis, the FR algorithm with AUROC 0.91 performed best in men and the GB algorithm with AUROC 0.95 performed best in women compared to other classification algorithms. Prediction of RF algorithm in women and men with AUROC 0.96 and 0.99, respectively, showed the highest performance in diagnosing the type of exercise for healthy individuals and those with osteopenia and OP. Eight AI algorithms were developed and compared to accurately predict osteoporosis risk factors and classify individuals into three categories: healthy, osteopenia, and OP. In addition, the AI algorithms were developed to recommend the most appropriate sports programs as part of treatment. Applying the AI algorithms in a clinical setting could help primary care providers classify patients with osteoporosis and improve treatment by recommending appropriate exercise programs.
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Albuquerque G, Cruz A, Carvalho D, Mayrink N, Pinheiro B, Campos A, Lima JG, Henriques J, Valentim R. A method based on non-ionizing microwave radiation for ancillary diagnosis of osteoporosis: a pilot study. Biomed Eng Online 2022; 21:70. [PMID: 36138480 PMCID: PMC9494783 DOI: 10.1186/s12938-022-01038-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 09/07/2022] [Indexed: 11/25/2022] Open
Abstract
Background Osteoporosis is a condition characterized by low bone mineral density, which typically leads to fractures and reduced quality of life. Currently, diagnostic devices used to assess this condition (e.g., dual-energy X-ray absorptiometry) are very costly, making it infeasible to meet the demand for testing in most countries. Therefore, we proposed a preclinical validation of a prototype called Osseus in an attempt to enhance osteoporosis screening tests and alleviate their costs. Osseus is a device developed to assist bone mineral density classification. It integrates a microcontroller into other peripheral devices to measure the attenuation at the middle phalanx of the middle finger, with two antennas operating at the 2.45 GHz frequency. Results We conducted tests with plaster, poultry, and porcine bones. A comparison of the measurements of the original and mechanically altered samples demonstrated that the device can handle the complexity of the tissues within the bone structure and characterize its microarchitecture. Conclusions Osseus is a device that has been preliminarily validated. Ionising radiation needed for DXA tests is replaced by non-ionising microwave electromagnetic radiation. Osseus enables early detection of osteoporosis, reduces costs, and optimizes high-complexity testing referrals. There is a lack of validation studies with the reference/gold standard that are currently under development.
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Affiliation(s)
- Gabriela Albuquerque
- Advanced Technological Innovation Nucleus-NAVI, Federal Institute of Rio Grande Do Norte, Natal, RN, Brazil.
| | - Agnaldo Cruz
- Advanced Technological Innovation Nucleus-NAVI, Federal Institute of Rio Grande Do Norte, Natal, RN, Brazil
| | - Dionísio Carvalho
- Advanced Technological Innovation Nucleus-NAVI, Federal Institute of Rio Grande Do Norte, Natal, RN, Brazil
| | - Nadja Mayrink
- Advanced Technological Innovation Nucleus-NAVI, Federal Institute of Rio Grande Do Norte, Natal, RN, Brazil
| | - Bruno Pinheiro
- Advanced Technological Innovation Nucleus-NAVI, Federal Institute of Rio Grande Do Norte, Natal, RN, Brazil
| | - Antonio Campos
- Advanced Technological Innovation Nucleus-NAVI, Federal Institute of Rio Grande Do Norte, Natal, RN, Brazil
| | | | - Jorge Henriques
- Department of Informatics Engineering, University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Coimbra, Portugal
| | - Ricardo Valentim
- Advanced Technological Innovation Nucleus-NAVI, Federal Institute of Rio Grande Do Norte, Natal, RN, Brazil
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4
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Xie Q, Chen Y, Hu Y, Zeng F, Wang P, Xu L, Wu J, Li J, Zhu J, Xiang M, Zeng F. Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography. BMC Med Imaging 2022; 22:140. [PMID: 35941568 PMCID: PMC9358842 DOI: 10.1186/s12880-022-00868-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/26/2022] [Indexed: 12/01/2022] Open
Abstract
Background To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia.
Methods A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC–AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. Results The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. Conclusions The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00868-5.
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Affiliation(s)
- Qianrong Xie
- Department of Clinical Research Center, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, 635000, Sichuan, China.,Department of Laboratory Medicine, The Third People's Hospital of Chengdu, Chengdu, 610000, China
| | - Yue Chen
- Department of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Jinniu District, Chengdu, 610000, Sichuan, China
| | - Yimei Hu
- Department of Orthopedics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610000, China
| | - Fanwei Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, 635000, Sichuan, China
| | - Pingxi Wang
- Department of Bone Disease, Dazhou Central Hospital, Dazhou, 635000, China
| | - Lin Xu
- Department of Medical Imaging, Dazhou Central Hospital, Dazhou, 635000, China
| | - Jianhong Wu
- Department of Bone Disease, Dazhou Central Hospital, Dazhou, 635000, China
| | - Jie Li
- Department of Clinical Research Center, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, 635000, Sichuan, China
| | - Jing Zhu
- Department of Rheumatology and Immunology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, No.32 First Ring Road West, Jinniu District, Chengdu, 610000, Sichuan, China.
| | - Ming Xiang
- Department of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Jinniu District, Chengdu, 610000, Sichuan, China. .,Department of Orthopedics, Sichuan Provincial Orthopedic Hospital, Chengdu, 610000, China.
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, No.56 Nanyuemiao Street, Tongchuan District, Dazhou, 635000, Sichuan, China. .,Department of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, No. 37 Shi-er-qiao Road, Jinniu District, Chengdu, 610000, Sichuan, China.
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5
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Adams JW, Zhang Z, Noetscher GM, Nazarian A, Makarov SN. Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screening. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2021; 9:4900907. [PMID: 34522471 PMCID: PMC8428761 DOI: 10.1109/jtehm.2021.3108575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/08/2021] [Accepted: 07/27/2021] [Indexed: 01/13/2023]
Abstract
Objective: There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation. Methods: The present study reports the application of a neural network classifier to the processing of previously collected data on very-low-power radiofrequency propagation through the wrist to detect osteoporotic/osteopenic conditions. Our approach categorizes the data obtained for two dichotomic groups. Group 1 included 27 osteoporotic/osteopenic subjects with low Bone Mineral Density (BMD), characterized by a Dual X-Ray Absorptiometry (DXA) T-score below – 1, measured within one year. Group 2 included 40 healthy and mostly young subjects without major clinical risk factors such as a (family) history of bone fracture. We process the complex radiofrequency spectrum from 30 kHz to 2 GHz. Instead of averaging data for both wrists, we process them independently along with the wrist circumference and then combine the results, which greatly increases the sensitivity. Measurements along with data processing require less than 1 min. Results: For the two dichotomic groups identified above, the neural network classifier of the radiofrequency spectrum reports a sensitivity of 83% and a specificity of 94%. Significance: These results are obtained without including any additional clinical risk factors. They justify that the radio transmission data are usable on their own as a predictor of bone density. This approach has the potential for screening patients at risk for fragility fractures in the office, given the ease of implementation, small device size, and low costs associated with both the technique and the equipment.
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Affiliation(s)
- Johnathan W Adams
- Department of Electrical and Computer EngineeringWorcester Polytechnic Institute Worcester MA 01609 USA
| | - Ziming Zhang
- Department of Electrical and Computer EngineeringWorcester Polytechnic Institute Worcester MA 01609 USA
| | - Gregory M Noetscher
- Department of Electrical and Computer EngineeringWorcester Polytechnic Institute Worcester MA 01609 USA.,Neva Electromagnetics LLC Yarmouth Port MA 02675 USA
| | - Ara Nazarian
- Musculoskeletal Translational Innovation InitiativeCarl J. Shapiro Department of Orthopaedic SurgeryBeth Israel Deaconess Medical Center, Harvard Medical School Boston MA 02215 USA.,Department of Orthopedic SurgeryYerevan State Medical University 0025 Yerevan Armenia
| | - Sergey N Makarov
- Department of Electrical and Computer EngineeringWorcester Polytechnic Institute Worcester MA 01609 USA.,Neva Electromagnetics LLC Yarmouth Port MA 02675 USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalHarvard Medical School Boston MA 02114 USA
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Smets J, Shevroja E, Hügle T, Leslie WD, Hans D. Machine Learning Solutions for Osteoporosis-A Review. J Bone Miner Res 2021; 36:833-851. [PMID: 33751686 DOI: 10.1002/jbmr.4292] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/04/2021] [Accepted: 03/16/2021] [Indexed: 12/11/2022]
Abstract
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high-dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12-point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML-based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model-based decision-making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Julien Smets
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Enisa Shevroja
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Didier Hans
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
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Irisin Has a Protective Role against Osteoporosis in Ovariectomized Rats. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5570229. [PMID: 33997010 PMCID: PMC8096550 DOI: 10.1155/2021/5570229] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 04/11/2021] [Accepted: 04/17/2021] [Indexed: 10/27/2022]
Abstract
The reduction in estrogen levels results in a decrease in bone density at menopause. Irisin is a myokine that modulates the benefits of exercise, which may include bone health. This study was planned to examine irisin's impact in preventing osteoporosis after ovariectomy. 4 groups of female albino rats (10 rats/group): control, sham-operated, ovariectomized (OVX-control), and OVX-irisin-treated. Serum levels of bone markers [osteocalcin (OC), bone alkaline phosphatase (BALP), tartrate-resistant acid phosphatase (TRAP), calcium (Ca++), phosphorus (P)], glucose, and insulin were being measured. Body mass index, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), dry and ash femur weight, and bone contents of Ca++ and P were investigated. The femur was examined histopathologically. The OVX-control group showed an increase in serum levels of OC, BALP, TRAP, calcium, phosphorus, BMI, glucose, insulin, and HOMA-IR (P < 0.05) and a reduction in dry and ash weight of the femur, the concentration of calcium and phosphorus content in bone ash (P < 0.05). The OVX-irisin-treated group exhibited a decrease in serum levels of OC, BALP and TRAP, calcium, phosphorus, BMI, glucose, insulin, HOMA-IR (P < 0.05), and a rise in dry and ash weight of the femur, the concentration of calcium and phosphorus in bone ash (P < 0.05). Histological examination of the distal femur diaphysis of the OVX-irisin-treated group exhibited proper bone architecture and density compared with that of the OVX-control group. It is concluded that irisin treatment in the OVX rats safeguarded the regular bone architecture and normal levels of serum bone biomarkers. Irisin may be a possible novel target in the prohibition of postmenopausal osteoporosis.
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Ao C, Jin S, Ding H, Zou Q, Yu L. Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis. Curr Pharm Des 2021; 26:3069-3075. [PMID: 32228416 DOI: 10.2174/1381612826666200331091156] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/17/2020] [Indexed: 12/12/2022]
Abstract
With the continuous development of artificial intelligence (AI) technology, big data-supported AI technology with considerable computer and learning capacity has been applied in diagnosing different types of diseases. This study reviews the application of expert systems, neural networks, and deep learning used by AI technology in disease diagnosis. This paper also gives a glimpse of the intelligent diagnosis and treatment of digestive system diseases, respiratory system diseases, and osteoporosis by AI technology.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Shunshan Jin
- Department of Neurology, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China
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9
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Wani IM, Arora S. Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey. Med Biol Eng Comput 2020; 58:1873-1917. [PMID: 32583141 DOI: 10.1007/s11517-020-02171-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 03/26/2020] [Indexed: 12/18/2022]
Abstract
Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.
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Affiliation(s)
- Insha Majeed Wani
- School of Computer Science and Engineering, SMVDU, Katra, J&K, India
| | - Sakshi Arora
- School of Computer Science and Engineering, SMVDU, Katra, J&K, India.
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Fusco T, Bi Y, Wang H, Browne F. Data mining and machine learning approaches for prediction modelling of schistosomiasis disease vectors: Epidemic disease prediction modelling. INT J MACH LEARN CYB 2019; 11:1159-1178. [PMID: 33727985 PMCID: PMC7224118 DOI: 10.1007/s13042-019-01029-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Accepted: 10/29/2019] [Indexed: 11/30/2022]
Abstract
This research presents viable solutions for prediction modelling of schistosomiasis disease based on vector density. Novel training models proposed in this work aim to address various aspects of interest in the artificial intelligence applications domain. Topics discussed include data imputation, semi-supervised labelling and synthetic instance simulation when using sparse training data. Innovative semi-supervised ensemble learning paradigms are proposed focusing on labelling threshold selection and stringency of classification confidence levels. A regression-correlation combination (RCC) data imputation method is also introduced for handling of partially complete training data. Results presented in this work show data imputation precision improvement over benchmark value replacement using proposed RCC on 70% of test cases. Proposed novel incremental transductive models such as ITSVM have provided interesting findings based on threshold constraints outperforming standard SVM application on 21% of test cases and can be applied with alternative environment-based epidemic disease domains. The proposed incremental transductive ensemble approach model enables the combination of complimentary algorithms to provide labelling for unlabelled vector density instances. Liberal (LTA) and strict training approaches provided varied results with LTA outperforming Stacking ensemble on 29.1% of test cases. Proposed novel synthetic minority over-sampling technique (SMOTE) equilibrium approach has yielded subtle classification performance increases which can be further interrogated to assess classification performance and efficiency relationships with synthetic instance generation.
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Affiliation(s)
- Terence Fusco
- Faculty of Computing and Engineering, University of Ulster, Newtownabbey, UK
| | - Yaxin Bi
- Faculty of Computing and Engineering, University of Ulster, Newtownabbey, UK
| | - Haiying Wang
- Faculty of Computing and Engineering, University of Ulster, Newtownabbey, UK
| | - Fiona Browne
- Faculty of Computing and Engineering, University of Ulster, Newtownabbey, UK
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11
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Cai J, He WG, Wang L, Zhou K, Wu TX. Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion. Sci Rep 2019; 9:10971. [PMID: 31358772 PMCID: PMC6662810 DOI: 10.1038/s41598-019-47281-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 07/15/2019] [Indexed: 11/09/2022] Open
Abstract
Considering the poor medical conditions in some regions of China, this paper attempts to develop a simple and easy way to extract and process the bone features of blurry medical images and improve the diagnosis accuracy of osteoporosis as much as possible. After reviewing the previous studies on osteoporosis, especially those focusing on texture analysis, a convexity optimization model was proposed based on intra-class dispersion, which combines texture features and shape features. Experimental results show that the proposed model boasts a larger application scope than Lasso, a popular feature selection method that only supports generalized linear models. The research findings ensure the accuracy of osteoporosis diagnosis and enjoy good potentials for clinical application.
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Affiliation(s)
- Jie Cai
- School of Information Engineering, Guangdong Medical University, Zhanjiang, 524023, China
| | - Wen-Guang He
- School of Information Engineering, Guangdong Medical University, Zhanjiang, 524023, China
| | - Long Wang
- School of Information Engineering, Guangdong Medical University, Zhanjiang, 524023, China
| | - Ke Zhou
- School of Information Engineering, Guangdong Medical University, Zhanjiang, 524023, China
| | - Tian-Xiu Wu
- School of Basic Medical Science, Guangdong Medical University, Zhanjiang, 524023, China.
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12
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Idri A, Benhar H, Fernández-Alemán JL, Kadi I. A systematic map of medical data preprocessing in knowledge discovery. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:69-85. [PMID: 29903496 DOI: 10.1016/j.cmpb.2018.05.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 04/25/2018] [Accepted: 05/03/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Datamining (DM) has, over the last decade, received increased attention in the medical domain and has been widely used to analyze medical datasets in order to extract useful knowledge and previously unknown patterns. However, historical medical data can often comprise inconsistent, noisy, imbalanced, missing and high dimensional data. These challenges lead to a serious bias in predictive modeling and reduce the performance of DM techniques. Data preprocessing is, therefore, an essential step in knowledge discovery as regards improving the quality of data and making it appropriate and suitable for DM techniques. The objective of this paper is to review the use of preprocessing techniques in clinical datasets. METHODS We performed a systematic map of studies regarding the application of data preprocessing to healthcare and published between January 2000 and December 2017. A search string was determined on the basis of the mapping questions and the PICO categories. The search string was then applied in digital databases covering the fields of computer science and medical informatics in order to identify relevant studies. The studies were initially selected by reading their titles, abstracts and keywords. Those that were selected at that stage were then reviewed using a set of inclusion and exclusion criteria in order to eliminate any that were not relevant. This process resulted in 126 primary studies. RESULTS Selected studies were analyzed and classified according to their publication years and channels, research type, empirical type and contribution type. The findings of this mapping study revealed that researchers have paid a considerable amount of attention to preprocessing in medical DM in last decade. A significant number of the selected studies used data reduction and cleaning preprocessing tasks. Moreover, the disciplines in which preprocessing have received most attention are: cardiology, endocrinology and oncology. CONCLUSIONS Researchers should develop and implement standards for an effective integration of multiple medical data types. Moreover, we identified the need to perform literature reviews.
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Affiliation(s)
- A Idri
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - H Benhar
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - J L Fernández-Alemán
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain.
| | - I Kadi
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
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Cruz AS, Lins HC, Medeiros RVA, Filho JMF, da Silva SG. Artificial intelligence on the identification of risk groups for osteoporosis, a general review. Biomed Eng Online 2018; 17:12. [PMID: 29378578 PMCID: PMC5789692 DOI: 10.1186/s12938-018-0436-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 01/10/2018] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. METHODS A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms "Neural Network", "Osteoporosis Machine Learning" and "Osteoporosis Neural Network". Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000-2017 were selected; however, articles prior to this period with great relevance were included in this study. DISCUSSION Based on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented. CONCLUSIONS It is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.
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Affiliation(s)
- Agnaldo S. Cruz
- Centro de Tecnologia, Universidade Federal do Rio Grande do Norte UFRN, Av. Salgado Filho, Natal, Brazil
| | - Hertz C. Lins
- Laboratory of Technological Innovation in Healthcare, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Ricardo V. A. Medeiros
- Laboratory of Technological Innovation in Healthcare, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - José M. F. Filho
- Laboratory of Technological Innovation in Healthcare, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Sandro G. da Silva
- Centro de Tecnologia, Universidade Federal do Rio Grande do Norte UFRN, Av. Salgado Filho, Natal, Brazil
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Faghihi F, Moustafa AA. Sparse and burst spiking in artificial neural networks inspired by synaptic retrograde signaling. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2017.08.073] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Kavitha MS, Ganesh Kumar P, Park SY, Huh KH, Heo MS, Kurita T, Asano A, An SY, Chien SI. Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches. Dentomaxillofac Radiol 2016; 45:20160076. [PMID: 27186991 DOI: 10.1259/dmfr.20160076] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis. METHODS The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers. RESULTS Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942-0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD. CONCLUSIONS The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage.
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Affiliation(s)
- Muthu Subash Kavitha
- 1 School of Electronics Engineering, Kyungpook National University, Daegu, Korea
| | | | - Soon-Yong Park
- 3 School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea
| | - Kyung-Hoe Huh
- 4 Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, Seoul, Korea
| | - Min-Suk Heo
- 4 Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, Seoul, Korea
| | - Takio Kurita
- 5 Graduate School of Engineering, Hiroshima University, Hiroshima, Japan
| | - Akira Asano
- 6 Faculty of Informatics, Kansai University, Osaka, Japan
| | - Seo-Yong An
- 7 Department of Oral and Maxillofacial Radiology, School of Dentistry, Kyungpook National University, Daegu, Korea
| | - Sung-Il Chien
- 1 School of Electronics Engineering, Kyungpook National University, Daegu, Korea
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