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Sadat-Ali M, Alzahrani BA, Alqahtani TS, Alotaibi MA, Alhalafi AM, Alsousi AA, Alasiri AM. Accuracy of artificial intelligence in prediction of osteoporotic fractures in comparison with dual-energy X-ray absorptiometry and the Fracture Risk Assessment Tool: A systematic review. World J Orthop 2025; 16:103572. [PMID: 40290609 PMCID: PMC12019139 DOI: 10.5312/wjo.v16.i4.103572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Revised: 01/10/2025] [Accepted: 02/27/2025] [Indexed: 04/17/2025] Open
Abstract
BACKGROUND Osteoporotic fractures, whether due to postmenopausal or senile causes, impose a significant financial burden on developing countries and diminish quality of life. Recent advancements in artificial intelligence (AI) algorithms have demonstrated immense potential in predicting osteoporotic fractures. AIM To assess and compare the efficacy of AI models against dual-energy X-ray absorptiometry (DXA) and the Fracture Risk Assessment Tool (FRAX) in predicting fragility fractures. METHODS We conducted a literature search in English using electronic databases, including PubMed, Web of Science, and Scopus, for studies published until May 2024. The keywords employed were fragility fractures, osteoporosis, AI, deep learning, machine learning, and convolutional neural network. The inclusion criteria for selecting publications were based on studies involving patients with proximal femur and vertebral column fractures due to osteoporosis, utilizing AI algorithms, and analyzing the site of fracture and accuracy for predicting fracture risk using SPSS version 29 (Chicago, IL, United States). RESULTS We identified 156 publications for analysis. After applying our inclusion criteria, 24489 patients were analyzed from 13 studies. The mean area under the receiver operating characteristic curve was 0.925 ± 0.69. The mean sensitivity was 68.3% ± 15.3%, specificity was 85.5% ± 13.4%, and positive predictive value was 86.5% ± 6.3%. DXA showed a sensitivity of 37.0% and 74.0%, while FRAX demonstrated a sensitivity of 45.7% and 84.7%. The P value for sensitivity between DXA and AI was < 0.0001, while for FRAX it was < 0.0001 and 0.2. CONCLUSION This review found that AI is a valuable tool to analyze and identify patients who will suffer from fragility fractures before they occur, demonstrating superiority over DXA and FRAX. Further studies are necessary to be conducted across various centers with diverse population groups, larger datasets, and a longer duration of follow-up to enhance the predictive performance of the AI models before their universal application.
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Affiliation(s)
- Mir Sadat-Ali
- Department of Orthopedic Surgery, Haifa Medical Complex, Al Khobar 32424, Saudi Arabia
| | - Bandar A Alzahrani
- Department of Orthopedics, College of Medicine, Imam Abdulrahman bin Faisal University, Dammam 34212, Saudi Arabia
| | - Turki S Alqahtani
- Department of Orthopaedic Surgery, King Fahd Military Medical Complex, Dhahran, Saudi Arabia
| | - Musaad A Alotaibi
- Department of Orthopedics, College of Medicine, Imam Abdulrahman bin Faisal University, Dammam 34212, Saudi Arabia
| | - Abdallah M Alhalafi
- Department of Orthopedics, College of Medicine, Imam Abdulrahman bin Faisal University, Dammam 34212, Saudi Arabia
| | - Ahmed A Alsousi
- Department of Orthopedics, College of Medicine, Imam Abdulrahman bin Faisal University, Dammam 34212, Saudi Arabia
| | - Abdullah M Alasiri
- Department of Orthopaedic Surgery, Security Forces Hospital, Dammam, Saudi Arabia
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Castro-Collado C, Llorente-Cantarero FJ, Gil-Campos M, Jurado-Castro JM. Basketball's Improvement in Bone Mineral Density Compared to Other Sports or Free Exercise Practice in Children and Adolescents: A Systematic Review and Meta-Analysis. CHILDREN (BASEL, SWITZERLAND) 2025; 12:271. [PMID: 40150554 PMCID: PMC11941393 DOI: 10.3390/children12030271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2025] [Revised: 02/18/2025] [Accepted: 02/19/2025] [Indexed: 03/29/2025]
Abstract
BACKGROUND Bone mineral density (BMD) is crucial for bone health, contributing up to 50% of total bone mineral content during childhood and pre-adolescence, with the accumulation of bone mass in youth significantly impacting adult bone health. Physical activity, especially impact exercise, plays a fundamental role in strengthening bones. OBJECTIVES The aim of this meta-analysis was to study the effects of basketball practice on BMD compared to other sports and free activity practice in children and adolescents. METHODS Observational studies were selected up to January 2024. A total of 492 articles were identified, of which 9 met the criteria for inclusion in the meta-analysis. RESULTS The BMD increase favored the group of basketball players in the total body (MD 0.07; CI 0.04 to 0.09; p < 0.001; I2 = 93%), upper limbs (MD 0.10; CI 0.008 to 0.12; p < 0.001; I2 = 96%), and lower limbs (MD 0.05; CI 0.03 to 0.07; p < 0.001; I2 = 80%). CONCLUSIONS Basketball practice in children and adolescents appears to be one of the most effective sports for enhancing BMD (total body and upper and lower limbs) compared to football, swimming, combat sports, other team sports, such as baseball and volleyball, as well as athletics and gymnastics. The high heterogeneity among studies, largely due to differences in sports, may limit the interpretation of the findings.
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Affiliation(s)
- Cristina Castro-Collado
- Metabolism and Investigation Unit, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 14004 Cordoba, Spain; (C.C.-C.); (J.M.J.-C.)
| | - Francisco Jesus Llorente-Cantarero
- Metabolism and Investigation Unit, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 14004 Cordoba, Spain; (C.C.-C.); (J.M.J.-C.)
- Department of Specific Didactics, Faculty of Education and Psychology, University of Cordoba, 14004 Cordoba, Spain
- CIBEROBN (Physiopathology of Obesity and Nutrition), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Mercedes Gil-Campos
- Metabolism and Investigation Unit, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 14004 Cordoba, Spain; (C.C.-C.); (J.M.J.-C.)
- CIBEROBN (Physiopathology of Obesity and Nutrition), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Jose Manuel Jurado-Castro
- Metabolism and Investigation Unit, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 14004 Cordoba, Spain; (C.C.-C.); (J.M.J.-C.)
- CIBEROBN (Physiopathology of Obesity and Nutrition), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- Ciencias De La Actividad Física y El Deporte, Escuela Universitaria de Osuna (Centro Adscrito a la Universidad de Sevilla), 41640 Osuna, Spain
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Gatineau G, Shevroja E, Vendrami C, Gonzalez-Rodriguez E, Leslie WD, Lamy O, Hans D. Development and reporting of artificial intelligence in osteoporosis management. J Bone Miner Res 2024; 39:1553-1573. [PMID: 39163489 PMCID: PMC11523092 DOI: 10.1093/jbmr/zjae131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 07/17/2024] [Accepted: 08/01/2024] [Indexed: 08/22/2024]
Abstract
An abundance of medical data and enhanced computational power have led to a surge in artificial intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17, 2020 to February 1, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the minimum information about clinical artificial intelligence modeling checklist. The systematic search yielded 97 articles that fell into 5 areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles), and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7-11) for bone properties assessment, 7.8 (range: 5-11) for osteoporosis classification, 8.4 (range: 7-11) for fracture detection, 7.6 (range: 4-11) for risk prediction, and 9.0 (range: 6-11) for bone segmentation. A sixth area, AI-driven clinical decision support, identified the studies from the 5 preceding areas that aimed to improve clinician efficiency, diagnostic accuracy, and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision-making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.
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Affiliation(s)
- Guillaume Gatineau
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
| | - Enisa Shevroja
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
| | - Colin Vendrami
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
| | - Elena Gonzalez-Rodriguez
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
| | - William D Leslie
- Department of Medicine, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Olivier Lamy
- Internal Medicine Unit, Internal Medicine Department, Lausanne University Hospital and University of Lausanne, 1005 Lausanne, Switzerland
| | - Didier Hans
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland
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Lehmann O, Mineeva O, Veshchezerova D, Häuselmann H, Guyer L, Reichenbach S, Lehmann T, Demler O, Everts-Graber J. Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK Biobank. J Bone Miner Res 2024; 39:1103-1112. [PMID: 38836468 DOI: 10.1093/jbmr/zjae089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 05/01/2024] [Accepted: 06/04/2024] [Indexed: 06/06/2024]
Abstract
Fracture prediction is essential in managing patients with osteoporosis and is an integral component of many fracture prevention guidelines. We aimed to identify the most relevant clinical fracture risk factors in contemporary populations by training and validating short- and long-term fracture risk prediction models in 2 cohorts. We used traditional and machine learning survival models to predict risks of vertebral, hip, and any fractures on the basis of clinical risk factors, T-scores, and treatment history among participants in a nationwide Swiss Osteoporosis Registry (N = 5944 postmenopausal women, median follow-up of 4.1 yr between January 2015 and October 2022; a total of 1190 fractures during follow-up). The independent validation cohort comprised 5474 postmenopausal women from the UK Biobank with 290 incident fractures during follow-up. Uno's C-index and the time-dependent area under the receiver operating characteristics curve were calculated to evaluate the performance of different machine learning models (Random survival forest and eXtreme Gradient Boosting). In the independent validation set, the C-index was 0.74 [0.58, 0.86] for vertebral fractures, 0.83 [0.7, 0.94] for hip fractures, and 0.63 [0.58, 0.69] for any fractures at year 2, and these values further increased for longer estimations of up to 7 yr. In comparison, the 10-yr fracture probability calculated with FRAX Switzerland was 0.60 [0.55, 0.64] for major osteoporotic fractures and 0.62 [0.49, 0.74] for hip fractures. The most important variables identified with Shapley additive explanations values were age, T-scores, and prior fractures, while number of falls was an important predictor of hip fractures. Performances of both traditional and machine learning models showed similar C-indices. We conclude that fracture risk can be improved by including the lumbar spine T-score, trabecular bone score, numbers of falls and recent fractures, and treatment information has a significant impact on fracture prediction.
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Affiliation(s)
- Oliver Lehmann
- Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland
| | - Olga Mineeva
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
| | | | - HansJörg Häuselmann
- Zentrum für Rheuma- und Knochenerkrankungen, Klinik Im Park, Hirslanden, Zürich, Switzerland
| | - Laura Guyer
- Faculty of Medicine, University of Bern, Bern, Switzerland
| | - Stephan Reichenbach
- Institute for Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Department of Rheumatology and Immunology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | | | - Olga Demler
- Department of Computer Science, ETH Zürich, Zürich, Switzerland
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Judith Everts-Graber
- Department of Rheumatology and Immunology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- OsteoRheuma Bern, Bahnhofplatz 1, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland
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Zhang J, Xia L, Zhang X, Liu J, Tang J, Xia J, Liu Y, Zhang W, Liang Z, Tang G, Zhang L. Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2024; 33:3242-3260. [PMID: 38955868 DOI: 10.1007/s00586-024-08235-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/22/2024] [Accepted: 03/17/2024] [Indexed: 07/04/2024]
Abstract
OBJECTIVE This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images. METHODS A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan-Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA). RESULTS BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction. CONCLUSION This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Clinical Medical College of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China
| | - Liang Xia
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China.
| | - Xueli Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China
| | - Jiayi Liu
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China
| | - Jun Tang
- Department of Radiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, 366 Taihu Road, Taizhou, 225300, Jiangsu, People's Republic of China
| | - Jianguo Xia
- Department of Radiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, 366 Taihu Road, Taizhou, 225300, Jiangsu, People's Republic of China.
| | - Yongkang Liu
- Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong Road, Nanjing, 210004, Jiangsu, People's Republic of China
| | - Weixiao Zhang
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China
| | - Zhipeng Liang
- Department of Radiology, Sir Run Run Hospital, Nanjing Medical University, 109 Longmian Road, Nanjing, 211002, Jiangsu, People's Republic of China
| | - Guangyu Tang
- Department of Radiology, Shanghai Tenth People's Hospital, Clinical Medical College of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China.
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China.
| | - Lin Zhang
- Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, People's Republic of China.
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Zabihiyeganeh M, Mirzaei A, Tabrizian P, Rezaee A, Sheikhtaheri A, Kadijani AA, Kadijani BA, Sharifi Kia A. Prediction of subsequent fragility fractures: application of machine learning. BMC Musculoskelet Disord 2024; 25:438. [PMID: 38834975 DOI: 10.1186/s12891-024-07559-y] [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: 11/29/2023] [Accepted: 05/29/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients. METHODS We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation. RESULTS The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score. CONCLUSION ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies.
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Affiliation(s)
- Mozhdeh Zabihiyeganeh
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
| | - Alireza Mirzaei
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
- Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Pouria Tabrizian
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
| | - Aryan Rezaee
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
- Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Abbas Sheikhtaheri
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Azade Amini Kadijani
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
| | - Bahare Amini Kadijani
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Ali Sharifi Kia
- Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran.
- Department of Computer Science, Faculty of Science, Western University, London, ON, Canada.
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Li Z, Zhao W, Lin X, Li F. AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review. J Orthop Surg Res 2023; 18:956. [PMID: 38087332 PMCID: PMC10714483 DOI: 10.1186/s13018-023-04446-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/05/2023] [Indexed: 12/18/2023] Open
Abstract
Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.
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Affiliation(s)
- Zeting Li
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Wen Zhao
- The Reproductive Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiahong Lin
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
| | - Fangping Li
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
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Wu Y, Chao J, Bao M, Zhang N. Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis. BMJ Open 2023; 13:e071430. [PMID: 38070927 PMCID: PMC10728980 DOI: 10.1136/bmjopen-2022-071430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 11/06/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis. METHODS Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites. RESULTS Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively. CONCLUSIONS ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models. PROSPERO REGISTRATION NUMBER CRD42022346896.
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Affiliation(s)
- Yanqian Wu
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Jianqian Chao
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Min Bao
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Na Zhang
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
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Khanna VV, Chadaga K, Sampathila N, Chadaga R, Prabhu S, K S S, Jagdale AS, Bhat D. A decision support system for osteoporosis risk prediction using machine learning and explainable artificial intelligence. Heliyon 2023; 9:e22456. [PMID: 38144333 PMCID: PMC10746430 DOI: 10.1016/j.heliyon.2023.e22456] [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: 08/02/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 12/26/2023] Open
Abstract
Osteoporosis is a metabolic bone condition that occurs when bone mineral density and mass decrease. This makes the bones weak and brittle. The disorder is often undiagnosed and untreated due to its asymptomatic nature until the manifestation of a fracture. Machine Learning (ML) is extensively used in diverse healthcare domains to analyze precise outcomes, provide timely risk scores, and allocate resources. Hence, we have designed multiple heterogeneous machine-learning frameworks to predict the risk of Osteoporosis. An open-source dataset of 1493 patients containing bone density, blood, and physical tests is utilized. Thirteen distinct feature selection techniques were leveraged to extract the most salient parameters. The best-performing pipeline consisted of a Forward Feature Selection algorithm followed by a custom multi-level ensemble learning-based stack, which achieved an accuracy of 89 %. Deploying a layer of explainable artificial intelligence using tools such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, and feature importance provided interpretability and rationale behind classifier prediction. With this study, we aim to provide the holistic risk prediction of Osteoporosis and concurrently present a system for automated screening to assist physicians in making diagnostic decisions.
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Affiliation(s)
- Varada Vivek Khanna
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India
| | - Krishnaraj Chadaga
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India
| | - Niranjana Sampathila
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India
| | - Rajagopala Chadaga
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India
| | - Srikanth Prabhu
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India
| | - Swathi K S
- Department of Social And Health Innovation, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Aditya S. Jagdale
- Mahatma Gandhi Institute of Medical Sciences, Sevagram, Maharashtra, India
| | - Devadas Bhat
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, India
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10
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Whittier DE, Samelson EJ, Hannan MT, Burt LA, Hanley DA, Biver E, Szulc P, Sornay-Rendu E, Merle B, Chapurlat R, Lespessailles E, Wong AKO, Goltzman D, Khosla S, Ferrari S, Bouxsein ML, Kiel DP, Boyd SK. A Fracture Risk Assessment Tool for High Resolution Peripheral Quantitative Computed Tomography. J Bone Miner Res 2023; 38:1234-1244. [PMID: 37132542 PMCID: PMC10523935 DOI: 10.1002/jbmr.4808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 02/10/2023] [Accepted: 03/21/2023] [Indexed: 05/04/2023]
Abstract
Most fracture risk assessment tools use clinical risk factors combined with bone mineral density (BMD) to improve assessment of osteoporosis; however, stratifying fracture risk remains challenging. This study developed a fracture risk assessment tool that uses information about volumetric bone density and three-dimensional structure, obtained using high-resolution peripheral quantitative compute tomography (HR-pQCT), to provide an alternative approach for patient-specific assessment of fracture risk. Using an international prospective cohort of older adults (n = 6802) we developed a tool to predict osteoporotic fracture risk, called μFRAC. The model was constructed using random survival forests, and input predictors included HR-pQCT parameters summarizing BMD and microarchitecture alongside clinical risk factors (sex, age, height, weight, and prior adulthood fracture) and femoral neck areal BMD (FN aBMD). The performance of μFRAC was compared to the Fracture Risk Assessment Tool (FRAX) and a reference model built using FN aBMD and clinical covariates. μFRAC was predictive of osteoporotic fracture (c-index = 0.673, p < 0.001), modestly outperforming FRAX and FN aBMD models (c-index = 0.617 and 0.636, respectively). Removal of FN aBMD and all clinical risk factors, except age, from μFRAC did not significantly impact its performance when estimating 5-year and 10-year fracture risk. The performance of μFRAC improved when only major osteoporotic fractures were considered (c-index = 0.733, p < 0.001). We developed a personalized fracture risk assessment tool based on HR-pQCT that may provide an alternative approach to current clinical methods by leveraging direct measures of bone density and structure. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Danielle E Whittier
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elizabeth J Samelson
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Marian T Hannan
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Lauren A Burt
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - David A Hanley
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Emmanuel Biver
- Division of Bone Diseases, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pawel Szulc
- INSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France
| | | | - Blandine Merle
- INSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France
| | - Roland Chapurlat
- INSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France
| | - Eric Lespessailles
- Regional Hospital of Orleans, PRIMMO and EA 4708-I3MTO, University of Orleans, Orleans, France
| | - Andy Kin On Wong
- Joint Department of Medical Imaging, University Health Network, Dalla Lana School of Public Health, University of Toronto, Toronto, CA, USA
- Department of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, CA, USA
| | - David Goltzman
- Department of Medicine, McGill University and McGill University Health Centre, Montreal, QC, Canada
| | - Sundeep Khosla
- Kogod Center on Aging and Division of Endocrinology, Mayo Clinic, Rochester, MN, USA
| | - Serge Ferrari
- Division of Bone Diseases, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Mary L Bouxsein
- Center for Advanced Orthopedic Studies, BIDMC, Harvard Medical School, Boston, MA, USA
- Endocrine Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Orthopedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Douglas P Kiel
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Steven K Boyd
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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11
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Cha Y, Seo SH, Kim JT, Kim JW, Lee SY, Yoo JI. Osteoporosis Feature Selection and Risk Prediction Model by Machine Learning Using a Cross-Sectional Database. J Bone Metab 2023; 30:263-273. [PMID: 37718904 PMCID: PMC10509024 DOI: 10.11005/jbm.2023.30.3.263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/03/2023] [Accepted: 07/19/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND The purpose of this study was to verify the accuracy and validity of using machine learning (ML) to select risk factors, to discriminate differences in feature selection by ML between men and women, and to develop predictive models for patients with osteoporosis in a big database. METHODS The data on 968 observed features from a total of 3,484 the Korea National Health and Nutrition Examination Survey participants were collected. To find preliminary features that were well-related to osteoporosis, logistic regression, random forest, gradient boosting, adaptive boosting, and support vector machine were used. RESULTS In osteoporosis feature selection by 5 ML models in this study, the most selected variables as risk factors in men and women were body mass index, monthly alcohol consumption, and dietary surveys. However, differences between men and women in osteoporosis feature selection by ML models were age, smoking, and blood glucose level. The receiver operating characteristic (ROC) analysis revealed that the area under the ROC curve for each ML model was not significantly different for either gender. CONCLUSIONS ML performed a feature selection of osteoporosis, considering hidden differences between men and women. The present study considers the preprocessing of input data and the feature selection process as well as the ML technique to be important factors for the accuracy of the osteoporosis prediction model.
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Affiliation(s)
- Yonghan Cha
- Department of Orthopaedic Surgery, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon,
Korea
| | - Sung Hyo Seo
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju,
Korea
| | - Jung-Taek Kim
- Department of Orthopedic Surgery, Ajou Medical Center, Ajou University School of Medicine, Suwon,
Korea
| | - Jin-Woo Kim
- Department of Orthopaedic Surgery, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul,
Korea
| | - Sang-Yeob Lee
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju,
Korea
| | - Jun-Il Yoo
- Department of Orthopaedic Surgery, Inha University Hospital, Inha University School of Medicine, Incheon,
Korea
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12
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Cha Y, Kim JT, Kim JW, Seo SH, Lee SY, Yoo JI. Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review. J Bone Metab 2023; 30:245-252. [PMID: 37718902 PMCID: PMC10509025 DOI: 10.11005/jbm.2023.30.3.245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/12/2023] [Accepted: 05/29/2023] [Indexed: 09/19/2023] Open
Abstract
BACKGROUND Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology. METHODS The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including "hip fractures" AND "artificial intelligence". RESULTS A total of 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%. CONCLUSIONS We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.
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Affiliation(s)
- Yonghan Cha
- Department of Orthopedic Surgery, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon,
Korea
| | - Jung-Taek Kim
- Department of Orthopedic Surgery, Ajou Medical Center, Ajou University School of Medicine, Suwon,
Korea
| | - Jin-Woo Kim
- Department of Orthopedic Surgery, Nowon Eulji Medical Center, Eulji University, Seoul,
Korea
| | - Sung Hyo Seo
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju,
Korea
| | - Sang-Yeob Lee
- Department of Biomedical Research Institute, Gyeongsang National University Hospital, Jinju,
Korea
| | - Jun-Il Yoo
- Department of Orthopaedic Surgery, Inha University Hospital, Inha University School of Medicine, Incheon,
Korea
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13
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Rahim F, Zaki Zadeh A, Javanmardi P, Emmanuel Komolafe T, Khalafi M, Arjomandi A, Ghofrani HA, Shirbandi K. Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study. Biomed Eng Online 2023; 22:68. [PMID: 37430259 PMCID: PMC10331995 DOI: 10.1186/s12938-023-01132-9] [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: 12/10/2022] [Accepted: 06/26/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Osteoporosis is a significant health problem in the skeletal system, associated with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has been accompanied by improvements in recent years and has been in the spotlight. This study is designed to investigate the Diagnostic Test Accuracy (DTA) of ML to detect osteoporosis through the hip dual-energy X-ray absorptiometry (DXA) images. METHODS The ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched until June 2023 for studies that tested the diagnostic precision of ML model-assisted for predicting an osteoporosis diagnosis. RESULTS The pooled sensitivity of univariate analysis of seven studies was 0.844 (95% CI 0.791 to 0.885, I2 = 94% for 7 studies). The pooled specificity of univariate analysis was 0.781 (95% CI 0.732 to 0.824, I2 = 98% for 7 studies). The pooled diagnostic odds ratio (DOR) was 18.91 (95% CI 14.22 to 25.14, I2 = 93% for 7 studies). The pooled mean positive likelihood ratio (LR+) and the negative likelihood ratio (LR-) were 3.7 and 0.22, respectively. Also, the summary receiver operating characteristics (sROC) of the bivariate model has an AUC of 0.878. CONCLUSION Osteoporosis can be diagnosed by ML with acceptable accuracy, and hip fracture prediction was improved via training in an Architecture Learning Network (ALN).
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Affiliation(s)
- Fakher Rahim
- Department of Anesthesia, Cihan University - Sulaimaniya, Sulaymaniyah, Kurdistan Region, Iraq
| | - Amin Zaki Zadeh
- Medical Doctor (MD), School of Medicine, Ahvaz Jondishapour University of Medical Sciences, Ahvaz, Iran
| | - Pouya Javanmardi
- Department of Radiologic Technology, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | | | - Mohammad Khalafi
- School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Ali Arjomandi
- Department of Radiologic Technology, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Haniye Alsadat Ghofrani
- Department of Radiologic Technology, Faculty of Paramedicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Kiarash Shirbandi
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran.
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14
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Minami T, Sato M, Toyoda H, Yasuda S, Yamada T, Nakatsuka T, Enooku K, Nakagawa H, Fujinaga H, Izumiya M, Tanaka Y, Otsuka M, Ohki T, Arai M, Asaoka Y, Tanaka A, Yasuda K, Miura H, Ogata I, Kamoshida T, Inoue K, Nakagomi R, Akamatsu M, Mitsui H, Fujie H, Ogura K, Uchino K, Yoshida H, Hanajiri K, Wada T, Kurai K, Maekawa H, Kondo Y, Obi S, Teratani T, Masaki N, Nagashima K, Ishikawa T, Kato N, Yotsuyanagi H, Moriya K, Kumada T, Fujishiro M, Koike K, Tateishi R. Machine learning for individualized prediction of hepatocellular carcinoma development after the eradication of hepatitis C virus with antivirals. J Hepatol 2023; 79:S0168-8278(23)00424-5. [PMID: 37716372 DOI: 10.1016/j.jhep.2023.05.042] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 04/03/2023] [Accepted: 05/23/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND AND AIMS Accurate risk stratification for hepatocellular carcinoma (HCC) after achieving a sustained viral response (SVR) is necessary for optimal surveillance. We aimed to develop and validate a machine learning (ML) model to predict the risk of HCC after achieving an SVR in individual patients. METHODS In this multicenter cohort study, 1742 patients with chronic hepatitis C who achieved an SVR were enrolled. Five ML models were developed including DeepSurv, gradient boosting survival analysis, random survival forest (RSF), survival support vector machine, and a conventional Cox proportional hazard model. Model performance was evaluated using Harrel' c-index and was externally validated in an independent cohort (977 patients). RESULTS During the mean observation period of 5.4 years, 122 patients developed HCC (83 in the derivation cohort and 39 in the external validation cohort). The RSF model showed the best discrimination ability using seven parameters at the achievement of an SVR with a c-index of 0.839 in the external validation cohort and a high discriminative ability when the patients were categorized into three risk groups (P <0.001). Furthermore, this RSF model enabled the generation of an individualized predictive curve for HCC occurrence for each patient with an app available online. CONCLUSIONS We developed and externally validated an RSF model with good predictive performance for the risk of HCC after an SVR. The application of this novel model is available on the website. This model could provide the data to consider an effective surveillance method. Further studies are needed to make recommendations for surveillance policies tailored to the medical situation in each country. IMPACT AND IMPLICATIONS A novel prediction model for HCC occurrence in patients after hepatitis C virus eradication was developed using machine learning algorithms. This model, using seven commonly measured parameters, has been shown to have a good predictive ability for HCC development and could provide a personalized surveillance system.
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Affiliation(s)
- Tatsuya Minami
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Masaya Sato
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Hidenori Toyoda
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital
| | - Satoshi Yasuda
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital
| | - Tomoharu Yamada
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Takuma Nakatsuka
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Kenichiro Enooku
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Hayato Nakagawa
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Hidetaka Fujinaga
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Masashi Izumiya
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Yasuo Tanaka
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Motoyuki Otsuka
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Takamasa Ohki
- Department of Gastroenterology, Mitsui Memorial Hospital
| | - Masahiro Arai
- Department of Gastroenterology, Toshiba General Hospital
| | | | - Atsushi Tanaka
- Department of Medicine, Teikyo University School of Medicine
| | | | - Hideaki Miura
- Department of Gastroenterology, Tokyo Yamate Medical Center
| | - Itsuro Ogata
- Department of Gastroenterology, Kawakita General Hospital
| | | | - Kazuaki Inoue
- Department of Gastroenterology, Showa University Fujigaoka Hospital
| | - Ryo Nakagomi
- Department of Gastroenterology, Kanto Central Hospital of the Mutual Aid Association of Public School Teacher
| | | | | | - Hajime Fujie
- Department of Gastroenterology, Tokyo Shinjuku Medical Center
| | - Keiji Ogura
- Department of Gastroenterology, Tokyo Metropolitan Police Hospital
| | - Koji Uchino
- Department of Gastroenterology, Japanese Red Cross Medical Center
| | - Hideo Yoshida
- Department of Gastroenterology, Japanese Red Cross Medical Center
| | | | | | | | - Hisato Maekawa
- Department of Gastroenterology and Hepatology, Tokyo Takanawa Hospital
| | - Yuji Kondo
- Department of Gastroenterology and Hepatology, Kyoundo Hospital
| | - Shuntaro Obi
- Department of Gastroenterology and Hepatology, Kyoundo Hospital
| | - Takuma Teratani
- Department of Hepato-Bililary-Pancreatic Medicine, NTT Medical Center Tokyo
| | - Naohiko Masaki
- Clinical Laboratory Department, Center Hospital of the National Center for Global Health and Medicine
| | - Kayo Nagashima
- Department of Gastroenterology, National Disaster Medical Center
| | | | - Naoya Kato
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Hiroshi Yotsuyanagi
- Division of Infectious Disease and Applied Immunology, The University of Tokyo the Institute of Medical Science Research Hospital
| | - Kyoji Moriya
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Takashi Kumada
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Kazuhiko Koike
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
| | - Ryosuke Tateishi
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo.
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15
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Goller SS, Rischewski JF, Liebig T, Ricke J, Siller S, Schmidt VF, Stahl R, Kulozik J, Baum T, Kirschke JS, Foreman SC, Gersing AS. Automated Opportunistic Trabecular Volumetric Bone Mineral Density Extraction Outperforms Manual Measurements for the Prediction of Vertebral Fractures in Routine CT. Diagnostics (Basel) 2023; 13:2119. [PMID: 37371014 DOI: 10.3390/diagnostics13122119] [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: 05/09/2023] [Revised: 06/16/2023] [Accepted: 06/18/2023] [Indexed: 06/29/2023] Open
Abstract
Opportunistic osteoporosis screening using multidetector CT-scans (MDCT) and convolutional neural network (CNN)-derived segmentations of the spine to generate volumetric bone mineral density (vBMD) bears the potential to improve incidental osteoporotic vertebral fracture (VF) prediction. However, the performance compared to the established manual opportunistic vBMD measures remains unclear. Hence, we investigated patients with a routine MDCT of the spine who had developed a new osteoporotic incidental VF and frequency matched to patients without incidental VFs as assessed on follow-up MDCT images after 1.5 years. Automated vBMD was generated using CNN-generated segmentation masks and asynchronous calibration. Additionally, manual vBMD was sampled by two radiologists. Automated vBMD measurements in patients with incidental VFs at 1.5-years follow-up (n = 53) were significantly lower compared to patients without incidental VFs (n = 104) (83.6 ± 29.4 mg/cm3 vs. 102.1 ± 27.7 mg/cm3, p < 0.001). This comparison was not significant for manually assessed vBMD (99.2 ± 37.6 mg/cm3 vs. 107.9 ± 33.9 mg/cm3, p = 0.30). When adjusting for age and sex, both automated and manual vBMD measurements were significantly associated with incidental VFs at 1.5-year follow-up, however, the associations were stronger for automated measurements (β = -0.32; 95% confidence interval (CI): -20.10, 4.35; p < 0.001) compared to manual measurements (β = -0.15; 95% CI: -11.16, 5.16; p < 0.03). In conclusion, automated opportunistic measurements are feasible and can be useful for bone mineral density assessment in clinical routine.
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Affiliation(s)
- Sophia S Goller
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Jon F Rischewski
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Thomas Liebig
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Sebastian Siller
- Department of Neurosurgery, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Vanessa F Schmidt
- Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Robert Stahl
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
| | - Julian Kulozik
- Institute of Micro Technology and Medical Device Technology (MIMED), Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany
| | - Alexandra S Gersing
- Institute for Diagnostic and Interventional Neuroradiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany
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16
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Qian X, Keerman M, Zhang X, Guo H, He J, Maimaitijiang R, Wang X, Ma J, Li Y, Ma R, Guo S. Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis. BMC Public Health 2023; 23:1041. [PMID: 37264356 PMCID: PMC10234013 DOI: 10.1186/s12889-023-15630-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 04/07/2023] [Indexed: 06/03/2023] Open
Abstract
PURPOSE With the increase in aging and cardiovascular risk factors, the morbidity and mortality of atherosclerotic cardiovascular disease (ASCVD), represented by ischemic heart disease and stroke, continue to rise in China. For better prevention and intervention, relevant guidelines recommend using predictive models for early detection of ASCVD high-risk groups. Therefore, this study aims to establish a population ASCVD prediction model in rural areas of Xinjiang using survival analysis. METHODS Baseline cohort data were collected from September to December 2016 and followed up till June 2022. A total of 7975 residents (4054 males and 3920 females) aged 30-74 years were included in the analysis. The data set was divided according to different genders, and the training and test sets ratio was 7:3 for different genders. A Cox regression, Lasso-Cox regression, and random survival forest (RSF) model were established in the training set. The model parameters were determined by cross-validation and parameter tuning and then verified in the training set. Traditional ASCVD prediction models (Framingham and China-PAR models) were constructed in the test set. Different models' discrimination and calibration degrees were compared to find the optimal prediction model for this population according to different genders and further analyze the risk factors of ASCVD. RESULTS After 5.79 years of follow-up, 873 ASCVD events with a cumulative incidence of 10.19% were found (7.57% in men and 14.44% in women). By comparing the discrimination and calibration degrees of each model, the RSF showed the best prediction performance in males and females (male: Area Under Curve (AUC) 0.791 (95%CI 0.767,0.813), C statistic 0.780 (95%CI 0.730,0.829), Brier Score (BS):0.060, female: AUC 0.759 (95%CI 0.734,0.783) C statistic was 0.737 (95%CI 0.702,0.771), BS:0.110). Age, systolic blood pressure (SBP), apolipoprotein B (APOB), Visceral Adiposity Index (VAI), hip circumference (HC), and plasma arteriosclerosis index (AIP) are important predictors of ASCVD in the rural population of Xinjiang. CONCLUSION The performance of the ASCVD prediction model based on the RSF algorithm is better than that based on Cox regression, Lasso-Cox, and the traditional ASCVD prediction model in the rural population of Xinjiang.
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Affiliation(s)
- Xin Qian
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China
| | - Mulatibieke Keerman
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China
| | - Xianghui Zhang
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China
| | - Heng Guo
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China
| | - Jia He
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China
| | - Remina Maimaitijiang
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China
| | - Xinping Wang
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China
| | - Jiaolong Ma
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China
| | - Yu Li
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China
| | - Rulin Ma
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China.
- Department of Public Health, The Key Laboratory of Preventive Medicine, Shihezi University School of Medicine, Suite 816Building No. 1, Beier Road, Shihezi, 832000, Xinjiang, China.
| | - Shuxia Guo
- Department of Public Health, Shihezi University School of Medicine, Suite 721, The Key Laboratory of Preventive Medicine, Building No. 1, Beier Road, ShiheziShihezi, 832000, Xinjiang, China.
- Department of NHC Key Laboratory of Prevention and Treatment of Central, Asia High Incidence Diseases, The First Affiliated Hospital of Shihezi University Medical College, Shihezi, Xinjiang, China.
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Kukafka R, Eysenbach G, Kim H, Lee S, Kong S, Kim JW, Choi J. Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation. J Med Internet Res 2023; 25:e40179. [PMID: 36482780 PMCID: PMC9883743 DOI: 10.2196/40179] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/16/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Osteoporosis is one of the diseases that requires early screening and detection for its management. Common clinical tools and machine-learning (ML) models for screening osteoporosis have been developed, but they show limitations such as low accuracy. Moreover, these methods are confined to limited risk factors and lack individualized explanation. OBJECTIVE The aim of this study was to develop an interpretable deep-learning (DL) model for osteoporosis risk screening with clinical features. Clinical interpretation with individual explanations of feature contributions is provided using an explainable artificial intelligence (XAI) technique. METHODS We used two separate data sets: the National Health and Nutrition Examination Survey data sets from the United States (NHANES) and South Korea (KNHANES) with 8274 and 8680 respondents, respectively. The study population was classified according to the T-score of bone mineral density at the femoral neck or total femur. A DL model for osteoporosis diagnosis was trained on the data sets and significant risk factors were investigated with local interpretable model-agnostic explanations (LIME). The performance of the DL model was compared with that of ML models and conventional clinical tools. Additionally, contribution ranking of risk factors and individualized explanation of feature contribution were examined. RESULTS Our DL model showed area under the curve (AUC) values of 0.851 (95% CI 0.844-0.858) and 0.922 (95% CI 0.916-0.928) for the femoral neck and total femur bone mineral density, respectively, using the NHANES data set. The corresponding AUC values for the KNHANES data set were 0.827 (95% CI 0.821-0.833) and 0.912 (95% CI 0.898-0.927), respectively. Through the LIME method, significant features were induced, and each feature's integrated contribution and interpretation for individual risk were determined. CONCLUSIONS The developed DL model significantly outperforms conventional ML models and clinical tools. Our XAI model produces high-ranked features along with the integrated contributions of each feature, which facilitates the interpretation of individual risk. In summary, our interpretable model for osteoporosis risk screening outperformed state-of-the-art methods.
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Affiliation(s)
| | | | - Hyeyeon Kim
- Department of Family Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Sanghwa Lee
- Department of Family Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Sunghye Kong
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jin-Woo Kim
- Department of Oral and Maxillofacial Surgery, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Jongeun Choi
- School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea
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18
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Reinold J, Braitmaier M, Riedel O, Haug U. Potential of Health Insurance Claims Data to Predict Fractures in Older Adults: A Prospective Cohort Study. Clin Epidemiol 2022; 14:1111-1122. [PMID: 36237823 PMCID: PMC9552670 DOI: 10.2147/clep.s379002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/16/2022] [Indexed: 11/28/2022] Open
Abstract
Purpose In older adults, fractures are associated with mortality, disability, loss of independence and high costs. Knowledge on their predictors can help to identify persons at high risk who may benefit from measures to prevent fractures. We aimed to assess the potential of German claims data to predict fractures in older adults. Patients and Methods Using the German Pharmacoepidemiological Research Database (short GePaRD; claims data from ~20% of the German population), we included persons aged ≥65 years with at least one year of continuous insurance coverage and no fractures prior to January 1, 2017 (baseline). We randomly divided the study population into a training (80%) and a test sample (20%) and used logistic regression and random forest models to predict the risk of fractures within one year after baseline based on different combinations of potential predictors. Results Among 2,997,872 persons (56% female), the incidence per 10,000 person years of any fracture in women increased from 133 in age group 65–74 years (men: 71) to 583 in age group 85+ (men: 332). The maximum predictive performance as measured by the area under the curve (AUC) across models was 0.63 in men and 0.60 in women and was achieved by combining information on drugs and morbidities. AUCs were lowest in age group 85+. Conclusion Our study showed that the performance of models using German claims data to predict the risk of fractures in older adults is moderate. Given that the models used data readily available to health insurance providers in Germany, it may still be worthwhile to explore the cost–benefit ratio of interventions aiming to reduce the risk of fractures based on such prediction models in certain risk groups.
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Affiliation(s)
- Jonas Reinold
- Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, 28359, Germany,Correspondence: Jonas Reinold, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Achterstraße 30, Bremen, 28359, Germany, Tel +49 421 218-56868, Fax +49 421 218-56821, Email
| | - Malte Braitmaier
- Department of Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, 28359, Germany
| | - Oliver Riedel
- Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, 28359, Germany
| | - Ulrike Haug
- Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, 28359, Germany,Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany
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Miranda D, Olivares R, Munoz R, Minonzio JG. Improvement of Patient Classification Using Feature Selection Applied to Bidirectional Axial Transmission. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:2663-2671. [PMID: 35914050 DOI: 10.1109/tuffc.2022.3195477] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Osteoporosis is still a worldwide problem, particularly due to associated fragility fractures. Patients at risk of fracture are currently detected using the X-Ray gold standard dual-energy X-ray absorptiometry (DXA), based on a calibrated 2-D image. Different alternatives, such as 3-D X-rays, magnetic resonance imaging (MRI) or ultrasound, have been proposed, the latter having advantages of being portable and sensitive to mechanical and geometrical properties. Bidirectional axial transmission (BDAT) has been used to classify between patients with or without nontraumatic fractures using "classical" ultrasonic parameters, such as velocities, as well as cortical thickness and porosity, obtained from an inverse problems. Recently, complementary parameters acquired with structural and textural analysis of guided wave spectrum images (GWSIs) have been introduced. These parameters are not limited by solution ambiguities, as for inverse problem. The aim of the study is to improve the patient classification using a feature selection strategy for all available ultrasound features completed by clinical parameters. To this end, three classical feature ranking methods were considered: analysis of variance (ANOVA), recursive feature elimination (RFE), and extreme gradient boosting importance feature (XGBI). In order to evaluate the performance of the feature selection techniques, three classical classification methods were used: logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB). The database was obtained from a previous clinical study [Minonzio et al., 2019]. Results indicate that the best accuracy of 71 [66-76]% was achieved by using RFE and SVM with 22 (out of 43) ultrasonic and clinical features. This value outperformed the accuracy of 68 [64-73]% reached with 2 (out of 6) DXA and clinical features. These values open promising perspectives toward improved and generalizable classification of patients at risk of fracture.
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20
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Kong SH, Lee JW, Bae BU, Sung JK, Jung KH, Kim JH, Shin CS. Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm. Endocrinol Metab (Seoul) 2022; 37:674-683. [PMID: 35927066 PMCID: PMC9449110 DOI: 10.3803/enm.2022.1461] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/20/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGRUOUND Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data. METHODS This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models. RESULTS Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women. CONCLUSION DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.
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Affiliation(s)
- Sung Hye Kong
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | | | | | | | | | - Jung Hee Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Corresponding author: Jung Hee Kim. Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea Tel: +82-2-2072-4839, Fax: +82-2-2072-7246, E-mail:
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
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21
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Sun X, Chen Y, Gao Y, Zhang Z, Qin L, Song J, Wang H, Wu IXY. Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal. Aging Dis 2022; 13:1215-1238. [PMID: 35855348 PMCID: PMC9286920 DOI: 10.14336/ad.2021.1206] [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: 10/04/2021] [Accepted: 12/06/2021] [Indexed: 11/01/2022] Open
Abstract
Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction models for OF have been developed, but their performance and methodological quality are unclear. We conducted this systematic review to summarize and critically appraise the OF risk prediction models. Three databases were searched until April 2021. Studies developing or validating multivariable models for OF risk prediction were considered eligible. Used the prediction model risk of bias assessment tool to appraise the risk of bias and applicability of included models. All results were narratively summarized and described. A total of 68 studies describing 70 newly developed prediction models and 138 external validations were included. Most models were explicitly developed (n=31, 44%) and validated (n=76, 55%) only for female. Only 22 developed models (31%) were externally validated. The most validated tool was Fracture Risk Assessment Tool. Overall, only a few models showed outstanding (n=3, 1%) or excellent (n=32, 15%) prediction discrimination. Calibration of developed models (n=25, 36%) or external validation models (n=33, 24%) were rarely assessed. No model was rated as low risk of bias, mostly because of an insufficient number of cases and inappropriate assessment of calibration. There are a certain number of OF risk prediction models. However, few models have been thoroughly internally validated or externally validated (with calibration being unassessed for most of the models), and all models showed methodological shortcomings. Instead of developing completely new models, future research is suggested to validate, improve, and analyze the impact of existing models.
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Affiliation(s)
- Xuemei Sun
- Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha 410000, Hunan, China.
| | - Yancong Chen
- Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha 410000, Hunan, China.
| | - Yinyan Gao
- Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha 410000, Hunan, China.
| | - Zixuan Zhang
- Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha 410000, Hunan, China.
| | - Lang Qin
- Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha 410000, Hunan, China.
| | - Jinlu Song
- Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha 410000, Hunan, China.
| | - Huan Wang
- Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha 410000, Hunan, China.
| | - Irene XY Wu
- Department of Epidemiology and Biostatistics, Xiangya School of Public Health, Central South University, Changsha 410000, Hunan, China.
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha 410000, China
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22
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Ren J, Liu D, Li G, Duan J, Dong J, Liu Z. Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients. Front Cardiovasc Med 2022; 9:923549. [PMID: 35811691 PMCID: PMC9263287 DOI: 10.3389/fcvm.2022.923549] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717–0.817), AUC of 0.780(95%CI: 0.721–0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.
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Affiliation(s)
- Jingjing Ren
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- Clinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dongwei Liu
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
| | - Guangpu Li
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- Clinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jiayu Duan
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- Clinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Jiayu Duan
| | - Jiancheng Dong
- Clinical Research Center of Big-data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Jiancheng Dong
| | - Zhangsuo Liu
- Department of Integrated Traditional and Western Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Research Institute of Nephrology, Zhengzhou University, Zhengzhou, China
- Henan Province Research Center for Kidney Disease, Zhengzhou, China
- Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, China
- *Correspondence: Zhangsuo Liu
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23
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Arai J, Aoki T, Sato M, Niikura R, Suzuki N, Ishibashi R, Tsuji Y, Yamada A, Hirata Y, Ushiku T, Hayakawa Y, Fujishiro M. Machine learning-based personalized prediction of gastric cancer incidence using the endoscopic and histologic findings at the initial endoscopy. Gastrointest Endosc 2022; 95:864-872. [PMID: 34998795 DOI: 10.1016/j.gie.2021.12.033] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/29/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Accurate risk stratification for gastric cancer is required for optimal endoscopic surveillance in patients with chronic gastritis. We aimed to develop a machine learning (ML) model that incorporates endoscopic and histologic findings for an individualized prediction of gastric cancer incidence. METHODS We retrospectively evaluated 1099 patients with chronic gastritis who underwent EGD and biopsy sampling of the gastric mucosa. Patients were randomly divided into training and test sets (4:1). We constructed a conventional Cox proportional hazard model and 3 ML models. Baseline characteristics, endoscopic atrophy, and Operative Link on Gastritis-Intestinal Metaplasia Assessment (OLGIM)/Operative Link on Gastritis Assessment (OLGA) stage at initial EGD were comprehensively assessed. Model performance was evaluated using Harrel's c-index. RESULTS During a mean follow-up of 5.63 years, 94 patients (8.55%) developed gastric cancer. The gradient-boosting decision tree (GBDT) model achieved the best performance (c-index from the test set, .84) and showed high discriminative ability in stratifying the test set into 3 risk categories (P < .001). Age, OLGIM/OLGA stage, endoscopic atrophy, and history of malignant tumors other than gastric cancer were important predictors of gastric cancer incidence in the GBDT model. Furthermore, the proposed GBDT model enabled the generation of a personalized cumulative incidence prediction curve for each patient. CONCLUSIONS We developed a novel ML model that incorporates endoscopic and histologic findings at initial EGD for personalized risk prediction of gastric cancer. This model may lead to the development of effective and personalized follow-up strategies after initial EGD.
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Affiliation(s)
- Junya Arai
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Tomonori Aoki
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Masaya Sato
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Ryota Niikura
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Nobumi Suzuki
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Rei Ishibashi
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Yosuke Tsuji
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Atsuo Yamada
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Yoshihiro Hirata
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Tetsuo Ushiku
- Department of Pathology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Yoku Hayakawa
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
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An Evolution Gaining Momentum—The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases. Diagnostics (Basel) 2022; 12:diagnostics12040836. [PMID: 35453884 PMCID: PMC9025301 DOI: 10.3390/diagnostics12040836] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/23/2022] [Accepted: 03/28/2022] [Indexed: 11/17/2022] Open
Abstract
In recent years, applications using artificial intelligence have been gaining importance in the diagnosis and treatment of spinal diseases. In our review, we describe the basic features of artificial intelligence which are currently applied in the field of spine diagnosis and treatment, and we provide an orientation of the recent technical developments and their applications. Furthermore, we point out the possible limitations and challenges in dealing with such technological advances. Despite the momentary limitations in practical application, artificial intelligence is gaining ground in the field of spine treatment. As an applying physician, it is therefore necessary to engage with it in order to benefit from those advances in the interest of the patient and to prevent these applications being misused by non-medical partners.
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Saravi B, Hassel F, Ülkümen S, Zink A, Shavlokhova V, Couillard-Despres S, Boeker M, Obid P, Lang GM. Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models. J Pers Med 2022; 12:jpm12040509. [PMID: 35455625 PMCID: PMC9029065 DOI: 10.3390/jpm12040509] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/18/2022] [Accepted: 03/19/2022] [Indexed: 12/22/2022] Open
Abstract
Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Correspondence:
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Veronika Shavlokhova
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany;
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria
| | - Martin Boeker
- Intelligence and Informatics in Medicine, Medical Center Rechts der Isar, School of Medicine, Technical University of Munich, 81675 Munich, Germany;
| | - Peter Obid
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
| | - Gernot Michael Lang
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
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Sato M, Tateishi R, Moriyama M, Fukumoto T, Yamada T, Nakagomi R, Kinoshita MN, Nakatsuka T, Minami T, Uchino K, Enooku K, Nakagawa H, Shiina S, Ninomiya K, Kodera S, Yatomi Y, Koike K. Machine Learning-Based Personalized Prediction of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation. GASTRO HEP ADVANCES 2022; 1:29-37. [PMID: 39129938 PMCID: PMC11308827 DOI: 10.1016/j.gastha.2021.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/13/2021] [Indexed: 08/13/2024]
Abstract
Background and Aims Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. Methods We included a total of 1778 patients with treatment-naïve HCC who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model and 6 ML models-including the deep learning-based DeepSurv model. Model performance was evaluated using Harrel's c-index and was validated externally using the split-sample method. Results The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into 2, 3, and 4 different risk groups (P < .001 among all risk groups). The c-index of DeepSurv was 0.64. In order of significance, the tumor number, serum albumin level, and des-gamma-carboxyprothrombin level were the most important variables for the prediction of HCC recurrence in the GBDT model. Also, the current GBDT model enabled the output of a personalized cumulative recurrence prediction curve for each patient. Conclusion We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.
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Affiliation(s)
- Masaya Sato
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryosuke Tateishi
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Makoto Moriyama
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Fukumoto
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomoharu Yamada
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryo Nakagomi
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | - Takuma Nakatsuka
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tatsuya Minami
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Koji Uchino
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kenichiro Enooku
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hayato Nakagawa
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shuichiro Shiina
- Department of Gastroenterology, Juntendo University, Tokyo, Japan
| | - Kota Ninomiya
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Satoshi Kodera
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yutaka Yatomi
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazuhiko Koike
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Yang H, Yan S, Li J, Zheng X, Yao Q, Duan S, Zhu J, Li C, Qin J. Prediction of acute versus chronic osteoporotic vertebral fracture using radiomics-clinical model on CT. Eur J Radiol 2022; 149:110197. [DOI: 10.1016/j.ejrad.2022.110197] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/26/2021] [Accepted: 01/31/2022] [Indexed: 11/28/2022]
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Does Machine Learning Offer Added Value Vis-à-Vis Traditional Statistics? An Exploratory Study on Retirement Decisions Using Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). MATHEMATICS 2022. [DOI: 10.3390/math10010152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Do machine learning algorithms perform better than statistical survival analysis when predicting retirement decisions? This exploratory article addresses the question by constructing a pseudo-panel with retirement data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The analysis consists of two methodological steps prompted by the nature of the data. First, a discrete Cox survival model of transitions to retirement with time-dependent covariates is compared to a Cox model without time-dependent covariates and a survival random forest. Second, the best performing model (Cox with time-dependent covariates) is compared to random forests adapted to time-dependent covariates by means of simulations. The results from the analysis do not clearly favor a single method; whereas machine learning algorithms have a stronger predictive power, the variables they use in their predictions do not necessarily display causal relationships with the outcome variable. Therefore, the two methods should be seen as complements rather than substitutes. In addition, simulations shed a new light on the role of some variables—such as education and health—in retirement decisions. This amounts to both substantive and methodological contributions to the literature on the modeling of retirement.
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AIM in Endocrinology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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Kim B, Jang YJ, Cho HR, Kim SY, Jeong JE, Shim MK, Kim MG. Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models. Clin Transl Sci 2021; 15:691-699. [PMID: 34735737 PMCID: PMC8932703 DOI: 10.1111/cts.13187] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/25/2021] [Accepted: 10/21/2021] [Indexed: 12/01/2022] Open
Abstract
This study aimed to develop a model for predicting the completion of clinical trials involving pregnant women using the Cox proportional hazard model and neural network model (DeepSurv) and to compare the predictive performance of both methods. We collected data on 819 clinical trials performed on pregnant women and intervention studies using at least one drug as intervention from 2009 to 2018 from ClinicalTrials.gov. The Cox proportional hazard model and DeepSurv were used to develop models that predict clinical trial completion. The concordance index (C‐index) was used to evaluate the predictive performance. The Cox proportional hazard model revealed that a sample size of n ≥ 329 (hazard ratio [HR] = 0.53), very high human development index (HDI) country (HR = 0.28), abortion (HR = 3.30), labor (HR = 2.16), and iron deficiency anemia (HR = 2.29) were significantly related to the probability of clinical trial completion (all p value < 0.01). The C‐index of the model development dataset and test dataset were 0.72 and 0.73, respectively. DeepSurv model consisted of one hidden layer with 16 nodes. DeepSurv showed the C‐index comparable to the Cox proportional hazard model. The C‐index of the training dataset and test dataset were 0.76 and 0.72, respectively. Further a nomogram that calculate a probability of clinical trial completion at 1 year, 3 years, and 5 years was developed. Both the Cox proportional hazard model and DeepSurv yielded sufficient predicting performance. We hope that this study will contribute to the execution of future clinical trials in pregnant women.
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Affiliation(s)
- Bomee Kim
- Graduate School of Clinical Biohealth, Ewha Womans University, Seoul, Korea
| | - Yun Ji Jang
- College of Pharmacy, CHA University, Pocheon, Korea
| | - Hae Ram Cho
- College of Pharmacy, CHA University, Pocheon, Korea
| | - So Yeon Kim
- College of Pharmacy, CHA University, Pocheon, Korea
| | - Ji Eun Jeong
- College of Pharmacy, CHA University, Pocheon, Korea
| | | | - Myeong Gyu Kim
- College of Pharmacy, Ewha Womans University, Seoul, Korea.,Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Korea
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Ulivieri FM, Rinaudo L, Messina C, Piodi LP, Capra D, Lupi B, Meneguzzo C, Sconfienza LM, Sardanelli F, Giustina A, Grossi E. Bone Strain Index predicts fragility fracture in osteoporotic women: an artificial intelligence-based study. Eur Radiol Exp 2021; 5:47. [PMID: 34664136 PMCID: PMC8523735 DOI: 10.1186/s41747-021-00242-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 08/23/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND We applied an artificial intelligence-based model to predict fragility fractures in postmenopausal women, using different dual-energy x-ray absorptiometry (DXA) parameters. METHODS One hundred seventy-four postmenopausal women without vertebral fractures (VFs) at baseline (mean age 66.3 ± 9.8) were retrospectively evaluated. Data has been collected from September 2010 to August 2018. All subjects performed a spine x-ray to assess VFs, together with lumbar and femoral DXA for bone mineral density (BMD) and the bone strain index (BSI) evaluation. Follow-up exams were performed after 3.34 ± 1.91 years. Considering the occurrence of new VFs at follow-up, two groups were created: fractured versus not-fractured. We applied an artificial neural network (ANN) analysis with a predictive tool (TWIST system) to select relevant input data from a list of 13 variables including BMD and BSI. A semantic connectivity map was built to analyse the connections among variables within the groups. For group comparisons, an independent-samples t-test was used; variables were expressed as mean ± standard deviation. RESULTS For each patient, we evaluated a total of n = 6 exams. At follow-up, n = 69 (39.6%) women developed a VF. ANNs reached a predictive accuracy of 79.56% within the training testing procedure, with a sensitivity of 80.93% and a specificity of 78.18%. The semantic connectivity map showed that a low BSI at the total femur is connected to the absence of VFs. CONCLUSION We found a high performance of ANN analysis in predicting the occurrence of VFs. Femoral BSI appears as a useful DXA index to identify patients at lower risk for lumbar VFs.
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Affiliation(s)
- Fabio Massimo Ulivieri
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy
- Current address: Università Vita-Salute San Raffaele, Via Olgettina, 58 20132, Milan, Italy
| | - Luca Rinaudo
- BSE TECHNOLOGIC S.r.l., Lungo Dora Voghera, 34/36A, 10153, Turin, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milan, Italy
| | - Luca Petruccio Piodi
- Former: Gastroenterology and Digestive Endoscopy Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy
| | - Davide Capra
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Pascal, 36, 20133, Milan, Italy
| | - Barbara Lupi
- Scuola di Specializzazione in Medicina Fisica e Riabilitativa, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122, Milan, Italy
| | - Camilla Meneguzzo
- Scuola di Specializzazione in Medicina Fisica e Riabilitativa, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milan, Italy.
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Pascal, 36, 20133, Milan, Italy.
| | - Francesco Sardanelli
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Pascal, 36, 20133, Milan, Italy
- IRCCS Policlinico San Donato, Via Rodolfo Morandi, 30, 20097, San Donato Milanese, Milan, Italy
| | - Andrea Giustina
- Institute of Endocrine and Metabolic Sciences (IEMS) San Raffaele Vita-Salute University, IRCCS San Raffaele Hospital, Via Olgettina Milano, 20, 20132, Milan, MI, Italy
| | - Enzo Grossi
- Villa Santa Maria Foundation, Via IV Novembre, 15, 22038, Tavernerio, Como, Italy
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Hans D, Shevroja E, Leslie WD. Evolution in fracture risk assessment: artificial versus augmented intelligence. Osteoporos Int 2021; 32:209-212. [PMID: 33415376 DOI: 10.1007/s00198-020-05737-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 11/08/2020] [Indexed: 12/23/2022]
Affiliation(s)
- D Hans
- Interdisciplinary Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland.
| | - E Shevroja
- Interdisciplinary Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - W D Leslie
- Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
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Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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