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Erjiang E, Carey JJ, Wang T, Ebrahimiarjestan M, Yang L, Dempsey M, Yu M, Chan WP, Whelan B, Silke C, O'Sullivan M, Rooney B, McPartland A, O'Malley G, Brennan A. Modelling future bone mineral density: Simplicity or complexity? Bone 2024; 187:117178. [PMID: 38972532 DOI: 10.1016/j.bone.2024.117178] [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: 12/15/2023] [Revised: 03/14/2024] [Accepted: 06/24/2024] [Indexed: 07/09/2024]
Abstract
BACKGROUND Osteoporotic fractures are a major global public health issue, leading to patient suffering and death, and considerable healthcare costs. Bone mineral density (BMD) measurement is important to identify those with osteoporosis and assess their risk of fracture. Both the absolute BMD and the change in BMD over time contribute to fracture risk. Predicting future fracture in individual patients is challenging and impacts clinical decisions such as when to intervene or repeat BMD measurement. Although the importance of BMD change is recognised, an effective way to incorporate this marginal effect into clinical algorithms is lacking. METHODS We compared two methods using longitudinal DXA data generated from subjects with two or more hip DXA scans on the same machine between 2000 and 2018. A simpler statistical method (ZBM) was used to predict an individual's future BMD based on the mean BMD and the standard deviation of the reference group and their BMD measured in the latest scan. A more complex deep learning (DL)-based method was developed to cope with multidimensional longitudinal data, variables extracted from patients' historical DXA scan(s), as well as features drawn from the ZBM method. Sensitivity analyses of several subgroups was conducted to evaluate the performance of the derived models. RESULTS 2948 white adults aged 40-90 years met our study inclusion: 2652 (90 %) females and 296 (10 %) males. Our DL-based models performed significantly better than the ZBM models in women, particularly our Hybrid-DL model. In contrast, the ZBM-based models performed as well or better than DL-based models in men. CONCLUSIONS Deep learning-based and statistical models have potential to forecast future BMD using longitudinal clinical data. These methods have the potential to augment clinical decisions regarding when to repeat BMD testing in the assessment of osteoporosis.
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Affiliation(s)
- E Erjiang
- School of Management, Guangxi Minzu Univeristy, Nanning, China
| | - John J Carey
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Rheumatology, Galway University Hospitals, Galway, Ireland
| | - Tingyan Wang
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Lan Yang
- Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway, Ireland
| | - Mary Dempsey
- School of Engineering, College of Science and Engineering, University of Galway, Ireland
| | - Ming Yu
- Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Wing P Chan
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taiwan
| | - Bryan Whelan
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Rheumatology, Our Lady's Hospital, Manorhamilton, Co. Leitrim, Ireland
| | - Carmel Silke
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Rheumatology, Our Lady's Hospital, Manorhamilton, Co. Leitrim, Ireland
| | - Miriam O'Sullivan
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Rheumatology, Our Lady's Hospital, Manorhamilton, Co. Leitrim, Ireland
| | - Bridie Rooney
- Department of Geriatric Medicine, Sligo University Hospital, Sligo, Ireland
| | - Aoife McPartland
- Department of Rheumatology, Our Lady's Hospital, Manorhamilton, Co. Leitrim, Ireland
| | - Gráinne O'Malley
- School of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Ireland; Department of Geriatric Medicine, Sligo University Hospital, Sligo, Ireland
| | - Attracta Brennan
- School of Computer Science, College of Science and Engineering, University of Galway, Ireland.
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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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Kuliczkowska-Płaksej J, Zdrojowy-Wełna A, Jawiarczyk-Przybyłowska A, Gojny Ł, Bolanowski M. Diagnosis and therapeutic approach to bone health in patients with hypopituitarism. Rev Endocr Metab Disord 2024; 25:513-539. [PMID: 38565758 DOI: 10.1007/s11154-024-09878-w] [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] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
The results of many studies in recent years indicate a significant impact of pituitary function on bone health. The proper function of the pituitary gland has a significant impact on the growth of the skeleton and the appearance of sexual dimorphism. It is also responsible for achieving peak bone mass, which protects against the development of osteoporosis and fractures later in life. It is also liable for the proper remodeling of the skeleton, which is a physiological mechanism managing the proper mechanical resistance of bones and the possibility of its regeneration after injuries. Pituitary diseases causing hypofunction and deficiency of tropic hormones, and thus deficiency of key hormones of effector organs, have a negative impact on the skeleton, resulting in reduced bone mass and susceptibility to pathological fractures. The early appearance of pituitary dysfunction, i.e. in the pre-pubertal period, is responsible for failure to achieve peak bone mass, and thus the risk of developing osteoporosis in later years. This argues for the need for a thorough assessment of patients with hypopituitarism, not only in terms of metabolic disorders, but also in terms of bone disorders. Early and properly performed treatment may prevent patients from developing the bone complications that are so common in this pathology. The aim of this review is to discuss the physiological, pathophysiological, and clinical insights of bone involvement in pituitary disease.
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Affiliation(s)
- Justyna Kuliczkowska-Płaksej
- Department and Clinic of Endocrinology, Diabetes and Isotope Therapy, Wroclaw Medical University, Wybrzeże Pasteura 4, Wrocław, 50-367, Poland
| | - Aleksandra Zdrojowy-Wełna
- Department and Clinic of Endocrinology, Diabetes and Isotope Therapy, Wroclaw Medical University, Wybrzeże Pasteura 4, Wrocław, 50-367, Poland
| | - Aleksandra Jawiarczyk-Przybyłowska
- Department and Clinic of Endocrinology, Diabetes and Isotope Therapy, Wroclaw Medical University, Wybrzeże Pasteura 4, Wrocław, 50-367, Poland.
| | - Łukasz Gojny
- Department and Clinic of Endocrinology, Diabetes and Isotope Therapy, Wroclaw Medical University, Wybrzeże Pasteura 4, Wrocław, 50-367, Poland
| | - Marek Bolanowski
- Department and Clinic of Endocrinology, Diabetes and Isotope Therapy, Wroclaw Medical University, Wybrzeże Pasteura 4, Wrocław, 50-367, Poland
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Yu Y, Li X, Zheng M, Zhou L, Zhang J, Wang J, Sun B. The potential benefits and mechanisms of protein nutritional intervention on bone health improvement. Crit Rev Food Sci Nutr 2024; 64:6380-6394. [PMID: 36655469 DOI: 10.1080/10408398.2023.2168250] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Osteoporosis commonly occurs in the older people and severe patients, with the main reason of the imbalance of bone metabolism (the rate of bone resorption exceeding the rate of bone formation), resulting in a decrease in bone mineral density and destruction of bone microstructure and further leading to the increased risk of fragility fracture. Recent studies indicate that protein nutritional support is beneficial for attenuating osteoporosis and improving bone health. This review summarized the classical mechanisms of protein intervention for alleviating osteoporosis on both suppressing bone resorption and regulating bone formation related pathways (promoting osteoblasts generation and proliferation, enhancing calcium absorption, and increasing collagen and mineral deposition), as well as the potential novel mechanisms via activating autophagy of osteoblasts, altering bone related miRNA profiles, regulating muscle-bone axis, and modulating gut microbiota abundance. Protein nutritional intervention is expected to provide novel approaches for the prevention and adjuvant therapy of osteoporosis.
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Affiliation(s)
- Yonghui Yu
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Key Laboratory of Special Food Supervision Technology for State Market Regulation, Beijing Technology and Business University, Beijing, China
| | - Xinping Li
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Key Laboratory of Special Food Supervision Technology for State Market Regulation, Beijing Technology and Business University, Beijing, China
| | - Mengjun Zheng
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Key Laboratory of Special Food Supervision Technology for State Market Regulation, Beijing Technology and Business University, Beijing, China
| | - Linyue Zhou
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Key Laboratory of Special Food Supervision Technology for State Market Regulation, Beijing Technology and Business University, Beijing, China
| | - Jingjie Zhang
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Key Laboratory of Special Food Supervision Technology for State Market Regulation, Beijing Technology and Business University, Beijing, China
| | - Jing Wang
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Key Laboratory of Special Food Supervision Technology for State Market Regulation, Beijing Technology and Business University, Beijing, China
| | - Baoguo Sun
- China-Canada Joint Lab of Food Nutrition and Health (Beijing), Key Laboratory of Special Food Supervision Technology for State Market Regulation, Beijing Technology and Business University, Beijing, China
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Kang SJ, Kim MJ, Hur YI, Haam JH, Kim YS. Application of Machine Learning Algorithms to Predict Osteoporotic Fractures in Women. Korean J Fam Med 2024; 45:144-148. [PMID: 38282437 PMCID: PMC11116127 DOI: 10.4082/kjfm.23.0186] [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: 09/04/2023] [Accepted: 10/22/2023] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Predicting the risk of osteoporotic fractures is vital for prevention. Traditional methods such as the Fracture Risk Assessment Tool (FRAX) model use clinical factors. This study examined the predictive power of the FRAX score and machine-learning algorithms trained on FRAX parameters. METHODS We analyzed the data of 2,147 female participants from the Ansan cohort study. The FRAX parameters employed in this study included age, sex (female), height and weight, current smoking status, excessive alcohol consumption (>3 units/d of alcohol), and diagnosis of rheumatoid arthritis. Osteoporotic fracture was defined as one or more fractures of the hip, spine, or wrist during a 10-year observation period. Machine-learning algorithms, such as gradient boosting, random forest, decision tree, and logistic regression, were employed to predict osteoporotic fractures with a 70:30 training-to-test set ratio. We evaluated the area under the receiver operating characteristic curve (AUROC) scores to assess and compare the performance of these algorithms with the FRAX score. RESULTS Of the 2,147 participants, 3.5% experienced osteoporotic fractures. Those with fractures were older, shorter in height, and had a higher prevalence of rheumatoid arthritis, as well as higher FRAX scores. The AUROC for the FRAX was 0.617. The machine-learning algorithms showed AUROC values of 0.662, 0.652, 0.648, and 0.637 for gradient boosting, logistic regression, decision tree, and random forest, respectively. CONCLUSION This study highlighted the immense potential of machine-learning algorithms to improve osteoporotic fracture risk prediction in women when complete FRAX parameter information is unavailable.
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Affiliation(s)
- Su Jeong Kang
- Department of Family Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Korea
| | - Moon Jong Kim
- Department of Family Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Korea
| | - Yang-Im Hur
- Department of Family Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Korea
| | - Ji-Hee Haam
- Department of Family Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Korea
| | - Young-Sang Kim
- Department of Family Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Korea
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6
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Lee J. Clinical Applicability of Machine Learning in Family Medicine. Korean J Fam Med 2024; 45:123-124. [PMID: 38779713 PMCID: PMC11116126 DOI: 10.4082/kjfm.45.3e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024] Open
Affiliation(s)
- Jungun Lee
- Department of Family Medicine, Kangdong Sacred Heart Hospital, College of Medicine, Hallym University, Seoul, Korea
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Varney ET, Kapoor N. Utility of Machine Learning Algorithms for Opportunistic Bone Density Screening Using Conventional Radiographs. J Am Coll Radiol 2024; 21:640-641. [PMID: 37722464 DOI: 10.1016/j.jacr.2023.08.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/03/2023] [Indexed: 09/20/2023]
Affiliation(s)
- Elliot T Varney
- Department of Radiology, University of Mississippi School of Medicine, Jackson, Mississippi.
| | - Neena Kapoor
- Associate Chair of Patient Experience and Clinically Significant Results, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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Lakkimsetti M, Devella SG, Patel KB, Dhandibhotla S, Kaur J, Mathew M, Kataria J, Nallani M, Farwa UE, Patel T, Egbujo UC, Meenashi Sundaram D, Kenawy S, Roy M, Khan SF. Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature. Cureus 2024; 16:e58400. [PMID: 38756258 PMCID: PMC11098056 DOI: 10.7759/cureus.58400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.
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Affiliation(s)
| | - Swati G Devella
- Medicine, Kempegowda Institute of Medical Sciences, Bangalore, IND
| | - Keval B Patel
- Surgery, Narendra Modi Medical College, Ahmedabad, IND
| | | | | | - Midhun Mathew
- Internal Medicine, Trinitas Regional Medical Center, Elizabeth, USA
| | | | - Manisha Nallani
- Medicine, Kamineni Academy of Medical Sciences and Research Center, Hyderabad, IND
| | - Umm E Farwa
- Emergency Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Tirath Patel
- Medicine, American University of Antigua, Saint John's, ATG
| | | | - Dakshin Meenashi Sundaram
- Internal Medicine, Employees' State Insurance Corporation (ESIC) Medical College & Post Graduate Institute of Medical Science and Research (PGIMSR), Chennai, IND
| | | | - Mehak Roy
- Internal Medicine, School of Medicine Science and Research, Delhi, IND
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Bhatnagar A, Kekatpure AL, Velagala VR, Kekatpure A. A Review on the Use of Artificial Intelligence in Fracture Detection. Cureus 2024; 16:e58364. [PMID: 38756254 PMCID: PMC11097122 DOI: 10.7759/cureus.58364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) simulates intelligent behavior using computers with minimum human intervention. Recent advances in AI, especially deep learning, have made significant progress in perceptual operations, enabling computers to convey and comprehend complicated input more accurately. Worldwide, fractures affect people of all ages and in all regions of the planet. One of the most prevalent causes of inaccurate diagnosis and medical lawsuits is overlooked fractures on radiographs taken in the emergency room, which can range from 2% to 9%. The workforce will soon be under a great deal of strain due to the growing demand for fracture detection on multiple imaging modalities. A dearth of radiologists worsens this rise in demand as a result of a delay in hiring and a significant percentage of radiologists close to retirement. Additionally, the process of interpreting diagnostic images can sometimes be challenging and tedious. Integrating orthopedic radio-diagnosis with AI presents a promising solution to these problems. There has recently been a noticeable rise in the application of deep learning techniques, namely convolutional neural networks (CNNs), in medical imaging. In the field of orthopedic trauma, CNNs are being documented to operate at the proficiency of expert orthopedic surgeons and radiologists in the identification and categorization of fractures. CNNs can analyze vast amounts of data at a rate that surpasses that of human observations. In this review, we discuss the use of deep learning methods in fracture detection and classification, the integration of AI with various imaging modalities, and the benefits and disadvantages of integrating AI with radio-diagnostics.
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Affiliation(s)
- Aayushi Bhatnagar
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aditya L Kekatpure
- Orthopedic Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Vivek R Velagala
- Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Aashay Kekatpure
- Orthopedic Surgery, Narendra Kumar Prasadrao Salve Institute of Medical Sciences and Research, Nagpur, IND
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Vendrami C, Shevroja E, Gonzalez Rodriguez E, Gatineau G, Elmers J, Reginster J, Harvey NC, Lamy O, Hans D. Muscle parameters in fragility fracture risk prediction in older adults: A scoping review. J Cachexia Sarcopenia Muscle 2024; 15:477-500. [PMID: 38284511 PMCID: PMC10995267 DOI: 10.1002/jcsm.13418] [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: 09/05/2023] [Revised: 11/01/2023] [Accepted: 11/28/2023] [Indexed: 01/30/2024] Open
Abstract
Half of osteoporotic fractures occur in patients with normal/osteopenic bone density or at intermediate or low estimated risk. Muscle measures have been shown to contribute to fracture risk independently of bone mineral density. The objectives were to review the measurements of muscle health (muscle mass/quantity/quality, strength and function) and their association with incident fragility fractures and to summarize their use in clinical practice. This scoping review follows the PRISMA-ScR guidelines for reporting. Our search strategy covered the three overreaching concepts of 'fragility fractures', 'muscle health assessment' and 'risk'. We retrieved 14 745 references from Medline Ovid SP, EMBASE, Web of Science Core Collection and Google Scholar. We included original and prospective studies on community-dwelling adults aged over 50 years that analysed an association between at least one muscle parameter and incident fragility fractures. We systematically extracted 17 items from each study, including methodology, general characteristics and results. Data were summarized in tables and graphically presented in adjusted forest plots. Sixty-seven articles fulfilled the inclusion criteria. In total, we studied 60 muscle parameters or indexes and 322 fracture risk ratios over 2.8 million person-years (MPY). The median (interquartile range) sample size was 1642 (921-5756), age 69.2 (63.5-73.6) years, follow-up 10.0 (4.4-12.0) years and number of incident fragility fractures 166 (88-277). A lower muscle mass was positively/not/negatively associated with incident fragility fracture in 28 (2.0), 64 (2.5) and 10 (0.2 MPY) analyses. A lower muscle strength was positively/not/negatively associated with fractures in 53 (1.3), 57 (1.7 MPY) and 0 analyses. A lower muscle function was positively/not/negatively associated in 63 (1.9), 45 (1.0 MPY) and 0 analyses. An in-depth analysis shows how each single muscle parameter was associated with each fragility fractures subtype. This review summarizes markers of muscle health and their association with fragility fractures. Measures of muscle strength and function appeared to perform better for fracture risk prediction. Of these, hand grip strength and gait speed are likely to be the most practical measures for inclusion in clinical practice, as in the evaluation of sarcopenia or in further fracture risk assessment scores. Measures of muscle mass did not appear to predict fragility fractures and might benefit from further research, on D3-creatine dilution test, lean mass indexes and artificial intelligence methods.
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Affiliation(s)
- Colin Vendrami
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Department of Bone and JointLausanne University Hospital and University of LausanneLausanneSwitzerland
- Internal Medicine Unit, Department of Internal MedicineLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Enisa Shevroja
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Department of Bone and JointLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Elena Gonzalez Rodriguez
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Department of Bone and JointLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Guillaume Gatineau
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Department of Bone and JointLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Jolanda Elmers
- University Library of Medicine, Faculty of Biology and MedicineLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Jean‐Yves Reginster
- WHO Collaborating Center for Public Health Aspects of Musculo‐Skeletal Health and Ageing, Division of Public Health, Epidemiology and Health EconomicsUniversity of LiègeLiègeBelgium
| | - Nicholas C. Harvey
- MRC Lifecourse Epidemiology CentreUniversity of SouthamptonSouthamptonUK
| | - Olivier Lamy
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Department of Bone and JointLausanne University Hospital and University of LausanneLausanneSwitzerland
- Internal Medicine Unit, Department of Internal MedicineLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Didier Hans
- Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Department of Bone and JointLausanne University Hospital and University of LausanneLausanneSwitzerland
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Chen T, Gao Z, Wang Y, Huang J, Liu S, Lin Y, Fu S, Wan L, Li Y, Huang H, Zhang Z. Identification and immunological role of cuproptosis in osteoporosis. Heliyon 2024; 10:e26759. [PMID: 38455534 PMCID: PMC10918159 DOI: 10.1016/j.heliyon.2024.e26759] [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: 05/05/2023] [Revised: 02/11/2024] [Accepted: 02/20/2024] [Indexed: 03/09/2024] Open
Abstract
Background osteoporosis is a skeletal disorder disease features low bone mass and poor bone architecture, which predisposes to increased risk of fracture. Copper death is a newly recognized form of cell death caused by excess copper ions, which presumably involve in various disease. Accordingly, we intended to investigate the molecular clusters related to the cuproptosis in osteoporosis and to construct a predictive model. Methods we investigated the expression patterns of cuproptosis regulators and immune signatures in osteoporosis based on the GSE56815 dataset. Through analysis of 40 osteoporosis samples, we investigated molecular clustering on the basis of cuproptosis--related genes, together with the associated immune cell infiltration. The WGCNA algorithm was applied to detect cluster-specific differentially expressed genes. Afterwards, the optimum machine model was selected by calculating the performance of the support vector machine model, random forest model, eXtreme Gradient Boosting and generalized linear model. Nomogram, decision curve analysis, calibration curves, and the GSE7158 dataset was utilizing to confirm the prediction efficiency. Results Differences between osteoporotic and non-osteoporotic controls confirm poorly adjusted copper death-related genes and triggered immune responses. In osteoporosis, two clusters of molecules in connection with copper death proliferation were outlined. The assessed levels of immune infiltration showed prominent heterogeneity between the different clusters. Cluster 2 was characterized by a raised immune score accompanied with relatively high levels of immune infiltration. The functional analysis we performed showed a close relationship between the different immune responses and specific differentially expressed genes in cluster 2. The random forest machine model showed the optimum discriminatory performance due to relatively low residuals and root mean square errors. Finally, a random forest model based on 5 genes was built, showing acceptable performance in an external validation dataset (AUC = 0.750). Calibration curve, Nomogram, and decision curve analyses also evinced fidelity in predicting subtypes of osteoporosis. Conclusion Our study identifies the role of cuproptosis in OP and essentially illustrates the underlying molecular mechanisms that lead to OP heterogeneity.
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Affiliation(s)
- Tongying Chen
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Zhijie Gao
- The Second Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yuedong Wang
- The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jiachun Huang
- The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Shuhua Liu
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Yanping Lin
- The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Sai Fu
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lei Wan
- The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Laboratory Affiliated to National Key Discipline of Orthopaedic and Traumatology of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Ying Li
- The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Qifu Hospital Affiliated to Jinan University, Guangzhou, China
| | - Hongxing Huang
- The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Laboratory Affiliated to National Key Discipline of Orthopaedic and Traumatology of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Zhihai Zhang
- The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Laboratory Affiliated to National Key Discipline of Orthopaedic and Traumatology of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
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12
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Tu JB, Liao WJ, Liu WC, Gao XH. Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data. Sci Rep 2024; 14:5245. [PMID: 38438569 PMCID: PMC10912338 DOI: 10.1038/s41598-024-56114-1] [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: 11/22/2023] [Accepted: 03/01/2024] [Indexed: 03/06/2024] Open
Abstract
Osteoporosis is a major public health concern that significantly increases the risk of fractures. The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. A total of 10,000 complete patient records of primary healthcare data in the German Disease Analyzer database (IMS HEALTH) were included, of which 1293 diagnosed with osteoporosis and 8707 without the condition. The demographic characteristics and chronic disease data, including age, gender, lipid disorder, cancer, COPD, hypertension, heart failure, CHD, diabetes, chronic kidney disease, and stroke were collected from electronic health records. Ten different machine learning algorithms were employed to construct the predictive mode. The performance of the model was further validated and the relative importance of features in the model was analyzed. Out of the ten machine learning algorithms, the Stacker model based on Logistic Regression, AdaBoost Classifier, and Gradient Boosting Classifier demonstrated superior performance. The Stacker model demonstrated excellent performance through ten-fold cross-validation on the training set and ROC curve analysis on the test set. The confusion matrix, lift curve and calibration curves indicated that the Stacker model had optimal clinical utility. Further analysis on feature importance highlighted age, gender, lipid metabolism disorders, cancer, and COPD as the top five influential variables. In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies.
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Affiliation(s)
- Jun-Bo Tu
- Department of Orthopaedics, Xinfeng County People's Hospital, Jiangxi, 341600, Xinfeng, China
| | - Wei-Jie Liao
- Department of ICU, GanZhou People's Hospital, GanZhou, 341000, Jiangxi, China
| | - Wen-Cai Liu
- Department of Orthopaedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
| | - Xing-Hua Gao
- Department of Orthopaedics, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China.
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13
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Zhang K, Lin PC, Pan J, Shao R, Xu PX, Cao R, Wu CG, Crookes D, Hua L, Wang L. DeepmdQCT: A multitask network with domain invariant features and comprehensive attention mechanism for quantitative computer tomography diagnosis of osteoporosis. Comput Biol Med 2024; 170:107916. [PMID: 38237237 DOI: 10.1016/j.compbiomed.2023.107916] [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: 08/30/2023] [Revised: 12/18/2023] [Accepted: 12/29/2023] [Indexed: 02/28/2024]
Abstract
In the medical field, the application of machine learning technology in the automatic diagnosis and monitoring of osteoporosis often faces challenges related to domain adaptation in drug therapy research. The existing neural networks used for the diagnosis of osteoporosis may experience a decrease in model performance when applied to new data domains due to changes in radiation dose and equipment. To address this issue, in this study, we propose a new method for multi domain diagnostic and quantitative computed tomography (QCT) images, called DeepmdQCT. This method adopts a domain invariant feature strategy and integrates a comprehensive attention mechanism to guide the fusion of global and local features, effectively improving the diagnostic performance of multi domain CT images. We conducted experimental evaluations on a self-created OQCT dataset, and the results showed that for dose domain images, the average accuracy reached 91%, while for device domain images, the accuracy reached 90.5%. our method successfully estimated bone density values, with a fit of 0.95 to the gold standard. Our method not only achieved high accuracy in CT images in the dose and equipment fields, but also successfully estimated key bone density values, which is crucial for evaluating the effectiveness of osteoporosis drug treatment. In addition, we validated the effectiveness of our architecture in feature extraction using three publicly available datasets. We also encourage the application of the DeepmdQCT method to a wider range of medical image analysis fields to improve the performance of multi-domain images.
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Affiliation(s)
- Kun Zhang
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, 226001, China; Nantong Key Laboratory of Intelligent Control and Intelligent Computing, Nantong, Jiangsu, 226001, China; Nantong Key Laboratory of Intelligent Medicine Innovation and Transformation, Nantong, Jiangsu, 226001, China
| | - Peng-Cheng Lin
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, 226001, China
| | - Jing Pan
- Department of Radiology, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, 226001, China
| | - Rui Shao
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, 226001, China
| | - Pei-Xia Xu
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, 226001, China
| | - Rui Cao
- Department of Radiology, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, 226001, China
| | - Cheng-Gang Wu
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, 226001, China
| | - Danny Crookes
- School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, BT7 1NN, UK
| | - Liang Hua
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, 226001, China.
| | - Lin Wang
- Department of Radiology, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, 226001, China.
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14
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Patil NS, Kielar AZ. Use of Opportunistic CT Biomarkers: Population Health Opportunities in Radiology. Can Assoc Radiol J 2024; 75:22-23. [PMID: 37535874 DOI: 10.1177/08465371231186356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023] Open
Affiliation(s)
- Nikhil S Patil
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
| | - Ania Z Kielar
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
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15
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Tsai DJ, Lin C, Lin CS, Lee CC, Wang CH, Fang WH. Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk. J Med Syst 2024; 48:12. [PMID: 38217829 DOI: 10.1007/s10916-023-02030-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024]
Abstract
A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.
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Affiliation(s)
- Dung-Jang Tsai
- Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan, R.O.C
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin Lin
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- School of Public Health, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chih-Hung Wang
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Wen-Hui Fang
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C..
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C..
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16
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Hung WC, Lin YL, Cheng TT, Chin WL, Tu LT, Chen CK, Yang CH, Wu CH. Establish and validate the reliability of predictive models in bone mineral density by deep learning as examination tool for women. Osteoporos Int 2024; 35:129-141. [PMID: 37728768 DOI: 10.1007/s00198-023-06913-5] [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: 08/04/2022] [Accepted: 09/04/2023] [Indexed: 09/21/2023]
Abstract
While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of under- and over-treated patients when no BMD measurements were available. The validated models could be used to screen for patients at a high risk of fracture and osteoporosis. PURPOSE Fracture risk assessment tool (FRAX) is useful in classifying the fracture risk level, and precise prediction can be achieved by estimating both clinical risk factors and bone mineral density (BMD) using dual X-ray absorptiometry (DXA). However, DXA is not frequently feasible because of its cost and accessibility. This study aimed to establish the reliability of deep learning (DL)-based alternative tools for screening patients at a high risk of fracture and osteoporosis. METHODS Participants were enrolled from the National Bone Health Screening Project of Taiwan in this cross-sectional study. First, DL-based models were built to predict the lowest T-score value in either the lumbar spine, total hip, or femoral neck and their respective BMD values. The Bland-Altman analysis was used to compare the agreement between the models and DXA. Second, the predictive model to classify patients with a high fracture risk was built according to the estimated BMD from the first step and the FRAX score without BMD. The performance of the model was compared with the classification based on FRAX with BMD. RESULTS Approximately 10,827 women (mean age, 65.4 ± 9.4 years) were enrolled. In the prediction of the lumbar spine BMD, total hip BMD, femoral neck BMD, and lowest T-score, the root-mean-square error (RMSE) was 0.099, 0.089, 0.076, and 0.68, respectively. The Bland-Altman analysis revealed a nonsignificant difference between the predictive models and DXA. The FRAX score with femoral neck BMD for major osteoporotic fracture risk was 9.7% ± 6.7%, whereas the risk for hip fracture was 3.3% ± 4.6%. Comparison between the classification of FRAX with and without BMD revealed the accuracy rate, positive predictive value (PPV), and negative predictive value (NPV) of 78.8%, 64.6%, and 89.9%, respectively. The area under the receiver operating characteristic curve (AUROC), accuracy rate, PPV, and NPV of the classification model were 0.913 (95% confidence interval: 0.904-0.922), 83.5%, 71.2%, and 92.2%, respectively. CONCLUSION While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of under- and over-treated patients when no BMD measurements were available. The validated models could be used to screen for patients at a high risk of fracture and osteoporosis.
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Affiliation(s)
- Wei- Chieh Hung
- Department of Family Medicine and Community Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
- School of Medicine for International Students, College of Medicine, I-Shou University School, Kaohsiung, Taiwan
- Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Yih-Lon Lin
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Tien-Tsai Cheng
- Department of Internal Medicine, Division of Rheumatology, Allergy and Immunology, Kaohsiung Chang Gung Memorial Hospital and School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Wei-Leng Chin
- Department of Family Medicine and Community Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Li-Te Tu
- Enterprise Resource Planning Technical Support and Research and Design Department, InfoChamp Systems Corporation, Kaohsiung, Taiwan
| | - Chih-Kui Chen
- Enterprise Resource Planning Technical Support and Research and Design Department, InfoChamp Systems Corporation, Kaohsiung, Taiwan
| | - Chih-Hui Yang
- Departments of Biological Science and Technology, I-Shou University, Kaohsiung, Taiwan.
| | - Chih-Hsing Wu
- Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
- Department of Family Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
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17
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Nicolaes J, Liu Y, Zhao Y, Huang P, Wang L, Yu A, Dunkel J, Libanati C, Cheng X. External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans. Osteoporos Int 2024; 35:143-152. [PMID: 37674097 PMCID: PMC10786735 DOI: 10.1007/s00198-023-06903-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/29/2023] [Indexed: 09/08/2023]
Abstract
The Convolutional Neural Network algorithm achieved a sensitivity of 94% and specificity of 93% in identifying scans with vertebral fractures (VFs). The external validation results suggest that the algorithm provides an opportunity to aid radiologists with the early identification of VFs in routine CT scans of abdomen and chest. PURPOSE To evaluate the performance of a previously trained Convolutional Neural Network (CNN) model to automatically detect vertebral fractures (VFs) in CT scans in an external validation cohort. METHODS Two Chinese studies and clinical data were used to retrospectively select CT scans of the chest, abdomen and thoracolumbar spine in men and women aged ≥50 years. The CT scans were assessed using the semiquantitative (SQ) Genant classification for prevalent VFs in a process blinded to clinical information. The performance of the CNN model was evaluated against reference standard readings by the area under the receiver operating characteristics curve (AUROC), accuracy, Cohen's kappa, sensitivity, and specificity. RESULTS A total of 4,810 subjects were included, with a median age of 62 years (IQR 56-67), of which 2,654 (55.2%) were females. The scans were acquired between January 2013 and January 2019 on 16 different CT scanners from three different manufacturers. 2,773 (57.7%) were abdominal CTs. A total of 628 scans (13.1%) had ≥1 VF (grade 2-3), representing 899 fractured vertebrae out of a total of 48,584 (1.9%) visualized vertebral bodies. The CNN's performance in identifying scans with ≥1 moderate or severe fractures achieved an AUROC of 0.94 (95% CI: 0.93-0.95), accuracy of 93% (95% CI: 93%-94%), kappa of 0.75 (95% CI: 0.72-0.77), a sensitivity of 94% (95% CI: 92-96%) and a specificity of 93% (95% CI: 93-94%). CONCLUSION The algorithm demonstrated excellent performance in the identification of vertebral fractures in a cohort of chest and abdominal CT scans of Chinese patients ≥50 years.
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Affiliation(s)
- Joeri Nicolaes
- Department of Electrical Engineering (ESAT), Center for Processing Speech and Images, KU Leuven, Leuven, Belgium.
- UCB Pharma, Brussels, Belgium.
| | - Yandong Liu
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
| | - Yue Zhao
- Department of Radiology, Qingdao Fuwaicardiovascular Hospital, Qingdao, 26600, China
| | - Pengju Huang
- Department of Radiology, Beijing Anding Hospital, Beijing, 100120, China
| | - Ling Wang
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
| | - Aihong Yu
- Department of Radiology, Beijing Anding Hospital, Beijing, 100120, China
| | | | | | - Xiaoguang Cheng
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
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18
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Zhang B, Chen Z, Yan R, Lai B, Wu G, You J, Wu X, Duan J, Zhang S. Development and Validation of a Feature-Based Broad-Learning System for Opportunistic Osteoporosis Screening Using Lumbar Spine Radiographs. Acad Radiol 2024; 31:84-92. [PMID: 37495426 DOI: 10.1016/j.acra.2023.07.002] [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: 05/10/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/28/2023]
Abstract
RATIONALE AND OBJECTIVES Osteoporosis is primarily diagnosed using dual-energy X-ray absorptiometry (DXA); yet, DXA is significantly underutilized, causing osteoporosis, an underdiagnosed condition. We aimed to provide an opportunistic approach to screen for osteoporosis using artificial intelligence based on lumbar spine X-ray radiographs. MATERIALS AND METHODS In this institutional review board-approved retrospective study, female patients aged ≥50 years who received both X-ray scans and DXA of the lumbar vertebrae, in three centers, were included. A total of 1180 cases were used for training and 145 cases were used for testing. We proposed a novel broad-learning system (BLS) and then compared the performance of BLS models using radiomic features and deep features as a source of input. The deep features were extracted using ResNet18 and VGG11, respectively. The diagnostic performances of these BLS models were evaluated with the area under the curve (AUC), sensitivity, and specificity. RESULTS The incidence rate of osteoporosis in the training and test sets was 35.9% and 37.9%, respectively. The radiomic feature-based BLS model achieved higher testing AUC (0.802 vs. 0.654 vs. 0.632, both P = .002), sensitivity (78.2% vs. 56.4% vs. 50.9%), and specificity (82.2% vs. 74,4% vs. 75.6%) than the two deep feature-based BLS models. CONCLUSION Our proposed radiomic feature-based BLS model has the potential to expand osteoporosis screening to a broader population by identifying osteoporosis on lumbar spine X-ray radiographs.
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Affiliation(s)
- Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, Guangdong 510627, China (B.Z., R.Y., J.Y., X.W., S.Z.)
| | - Zhangtianyi Chen
- College of Information Science and Technology, Jinan University, Guangzhou, Guangdong, China (Z.C., B.L., G.W., J.D.)
| | - Ruike Yan
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, Guangdong 510627, China (B.Z., R.Y., J.Y., X.W., S.Z.)
| | - Bifan Lai
- College of Information Science and Technology, Jinan University, Guangzhou, Guangdong, China (Z.C., B.L., G.W., J.D.)
| | - Guangheng Wu
- College of Information Science and Technology, Jinan University, Guangzhou, Guangdong, China (Z.C., B.L., G.W., J.D.)
| | - Jingjing You
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, Guangdong 510627, China (B.Z., R.Y., J.Y., X.W., S.Z.)
| | - Xuewei Wu
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, Guangdong 510627, China (B.Z., R.Y., J.Y., X.W., S.Z.)
| | - Junwei Duan
- College of Information Science and Technology, Jinan University, Guangzhou, Guangdong, China (Z.C., B.L., G.W., J.D.); Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Jinan University, Guangzhou, Guangdong, China (J.D.)
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, Guangdong 510627, China (B.Z., R.Y., J.Y., X.W., S.Z.).
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19
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Guermazi A, Omoumi P, Tordjman M, Fritz J, Kijowski R, Regnard NE, Carrino J, Kahn CE, Knoll F, Rueckert D, Roemer FW, Hayashi D. How AI May Transform Musculoskeletal Imaging. Radiology 2024; 310:e230764. [PMID: 38165245 PMCID: PMC10831478 DOI: 10.1148/radiol.230764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 06/18/2023] [Accepted: 07/11/2023] [Indexed: 01/03/2024]
Abstract
While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.
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Affiliation(s)
- Ali Guermazi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Patrick Omoumi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Mickael Tordjman
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Jan Fritz
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Richard Kijowski
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Nor-Eddine Regnard
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - John Carrino
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Charles E. Kahn
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Florian Knoll
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daniel Rueckert
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Frank W. Roemer
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daichi Hayashi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
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20
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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21
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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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22
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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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23
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Nicolaes J, Skjødt MK, Raeymaeckers S, Smith CD, Abrahamsen B, Fuerst T, Debois M, Vandermeulen D, Libanati C. Towards Improved Identification of Vertebral Fractures in Routine Computed Tomography (CT) Scans: Development and External Validation of a Machine Learning Algorithm. J Bone Miner Res 2023; 38:1856-1866. [PMID: 37747147 DOI: 10.1002/jbmr.4916] [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: 02/17/2023] [Revised: 09/06/2023] [Accepted: 09/17/2023] [Indexed: 09/26/2023]
Abstract
Vertebral fractures (VFs) are the hallmark of osteoporosis, being one of the most frequent types of fragility fracture and an early sign of the disease. They are associated with significant morbidity and mortality. VFs are incidentally found in one out of five imaging studies, however, more than half of the VFs are not identified nor reported in patient computed tomography (CT) scans. Our study aimed to develop a machine learning algorithm to identify VFs in abdominal/chest CT scans and evaluate its performance. We acquired two independent data sets of routine abdominal/chest CT scans of patients aged 50 years or older: a training set of 1011 scans from a non-interventional, prospective proof-of-concept study at the Universitair Ziekenhuis (UZ) Brussel and a validation set of 2000 subjects from an observational cohort study at the Hospital of Holbaek. Both data sets were externally reevaluated to identify reference standard VF readings using the Genant semiquantitative (SQ) grading. Four independent models have been trained in a cross-validation experiment using the training set and an ensemble of four models has been applied to the external validation set. The validation set contained 15.3% scans with one or more VF (SQ2-3), whereas 663 of 24,930 evaluable vertebrae (2.7%) were fractured (SQ2-3) as per reference standard readings. Comparison of the ensemble model with the reference standard readings in identifying subjects with one or more moderate or severe VF resulted in an area under the receiver operating characteristic curve (AUROC) of 0.88 (95% confidence interval [CI], 0.85-0.90), accuracy of 0.92 (95% CI, 0.91-0.93), kappa of 0.72 (95% CI, 0.67-0.76), sensitivity of 0.81 (95% CI, 0.76-0.85), and specificity of 0.95 (95% CI, 0.93-0.96). We demonstrated that a machine learning algorithm trained for VF detection achieved strong performance on an external validation set. It has the potential to support healthcare professionals with the early identification of VFs and prevention of future fragility fractures. © 2023 UCB S.A. and 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)
- Joeri Nicolaes
- Department of Electrical Engineering (ESAT), Center for Processing Speech and Images, KU Leuven, Leuven, Belgium
- UCB Pharma, Brussels, Belgium
| | - Michael Kriegbaum Skjødt
- Department of Medicine, Hospital of Holbaek, Holbaek, Denmark
- OPEN-Open Patient Data Explorative Network, Department of Clinical Research, University of Southern Denmark and Odense University Hospital, Odense, Denmark
| | | | - Christopher Dyer Smith
- OPEN-Open Patient Data Explorative Network, Department of Clinical Research, University of Southern Denmark and Odense University Hospital, Odense, Denmark
| | - Bo Abrahamsen
- Department of Medicine, Hospital of Holbaek, Holbaek, Denmark
- OPEN-Open Patient Data Explorative Network, Department of Clinical Research, University of Southern Denmark and Odense University Hospital, Odense, Denmark
- NDORMS, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University Hospitals, Oxford, UK
| | | | | | - Dirk Vandermeulen
- Department of Electrical Engineering (ESAT), Center for Processing Speech and Images, KU Leuven, Leuven, Belgium
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24
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Wu X, Zhai F, Chang A, Wei J, Guo Y, Zhang J. Application of machine learning algorithms to predict osteoporosis in postmenopausal women with type 2 diabetes mellitus. J Endocrinol Invest 2023; 46:2535-2546. [PMID: 37171784 DOI: 10.1007/s40618-023-02109-0] [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: 10/04/2022] [Accepted: 05/03/2023] [Indexed: 05/13/2023]
Abstract
PURPOSE The screening and diagnosis of osteoporosis in patients with type 2 diabetes mellitus (T2DM) based on bone mineral density remains challenging because of the limited availability and accessibility of dual-energy X-ray absorptiometry. We aimed to develop and validate models to predict the risk of osteoporosis in postmenopausal women with T2DM based on machine learning (ML) algorithms. METHODS This retrospective study included 303 postmenopausal women with T2DM. To develop prediction models for osteoporosis, we applied nine ML algorithms combined with demographic, clinical, and laboratory data. The least absolute shrinkage and selection operator were used to perform feature selection. We used the bootstrap resampling technique for model training and validation. To test the performance of the models, we calculated indices including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve, and decision curve analysis. Furthermore, we conducted fivefold cross-validation for parameter optimization and model validation. Feature importance was assessed using the SHapley additive explanation (SHAP). RESULTS We identified 10 independent predictors as the most valuable features. An AUROC of 0.616-1.000 was observed for nine ML algorithms. The extreme gradient boosting (XGBoost) model exhibited the best performance, outperforming conventional risk assessment tools and registering 0.993 in the training set, 0.798 in the validation set, and 0.786 in the test set for fivefold cross-validation. Using SHAP, we found that the explanatory variables contributed to the model and their relationship with osteoporosis occurrence. Furthermore, we developed a user-friendly tool for calculating the risk of osteoporosis. CONCLUSIONS With the integration of demographic and clinical risk factors, ML algorithms can accurately predict osteoporosis. The XGBoost model showed ideal performance. With the incorporation of these models in the clinic, patients may benefit from early osteoporosis diagnosis and treatment.
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Affiliation(s)
- X Wu
- Department of Endocrinology, Cangzhou Central Hospital, 16 Xinhua West Road, Cangzhou, 061000, Hebei, People's Republic of China.
| | - F Zhai
- Gynecological Clinic, Cangzhou Central Hospital, 16 Xinhua West Road, Cangzhou, 061000, Hebei, People's Republic of China
| | - A Chang
- Department of Endocrinology, Cangzhou Central Hospital, 16 Xinhua West Road, Cangzhou, 061000, Hebei, People's Republic of China
| | - J Wei
- Department of Endocrinology, Cangzhou Central Hospital, 16 Xinhua West Road, Cangzhou, 061000, Hebei, People's Republic of China
| | - Y Guo
- Department of Endocrinology, Cangzhou Central Hospital, 16 Xinhua West Road, Cangzhou, 061000, Hebei, People's Republic of China
| | - J Zhang
- Department of Endocrinology, Cangzhou Central Hospital, 16 Xinhua West Road, Cangzhou, 061000, Hebei, People's Republic of China
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Allam AK, Anand A, Flores AR, Ropper AE. Computer Vision in Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review. Neurospine 2023; 20:1112-1123. [PMID: 38171281 PMCID: PMC10762393 DOI: 10.14245/ns.2347022.511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Osteoporotic vertebral fractures (OVFs) are a significant health concern linked to increased morbidity, mortality, and diminished quality of life. Traditional OVF risk assessment tools like bone mineral density (BMD) only capture a fraction of the risk profile. Artificial intelligence, specifically computer vision, has revolutionized other fields of medicine through analysis of videos, histopathology slides and radiological scans. In this review, we provide an overview of computer vision algorithms and current computer vision models used in predicting OVF risk. We highlight the clinical applications, future directions and limitations of computer vision in OVF risk prediction.
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Affiliation(s)
- Anthony K. Allam
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Adrish Anand
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Alex R. Flores
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
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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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Zhu Y, Liu Y, Wang Q, Niu S, Wang L, Cheng C, Chen X, Liu J, Zhao S. Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis. J Cancer Res Clin Oncol 2023; 149:17479-17493. [PMID: 37897658 DOI: 10.1007/s00432-023-05472-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 10/10/2023] [Indexed: 10/30/2023]
Abstract
INTRODUCTION Osteoporosis that emerges subsequent to gastrectomy poses a significant threat to the long-term health of patients. The primary objective of this investigation was to formulate a machine learning algorithm capable of identifying substantial preoperative, intraoperative, and postoperative risk factors. This algorithm, in turn, would enable the anticipation of osteoporosis occurrence after gastrectomy. METHODS This research encompassed a cohort of 1125 patients diagnosed with gastric cancer, including 108 individuals with low bone density or osteoporosis. A total of 40 distinct variables were collected, comprising patient demographics, pertinent medical history, medication records, preoperative examination attributes, surgical procedure specifics, and intraoperative details. Four distinct machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)-were employed to establish the predictive model. Evaluation of the models involved receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Shapley additive explanation (SHAP) was employed for visualization and analysis. RESULTS Among the four prediction models employed, the XGBoost algorithm demonstrated exceptional performance. The ROC analysis yielded excellent predictive accuracy, showcasing area under the curve (AUC) values of 0.957 and 0.896 for training and validation sets, respectively. The calibration curve further confirmed the robust predictive capacity of the XGBoost model. The DCA demonstrated a notably higher benefit rate for patients undergoing intervention based on the XGBoost model. Moreover, the AUC value of 0.73 for the external validation set indicated favorable extrapolation of the XGBoost prediction model. SHAP analysis outcomes unveiled numerous high-risk factors for osteoporosis development after gastrectomy, including a history of chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), hypoproteinemia, postoperative neutrophil-to-lymphocyte ratio (NLR) exceeding 3, steroid usage history, advanced age, and absence of calcitonin use. CONCLUSION The osteoporosis prediction model derived through the XGBoost machine learning algorithm in this study displays remarkable predictive precision and carries significant clinical applicability.
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Affiliation(s)
- Yanfei Zhu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Yuan Liu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Qi Wang
- Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Sen Niu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Lanyu Wang
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Chao Cheng
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Xujin Chen
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China
| | - Jinhui Liu
- Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Songyun Zhao
- Wuxi Medical Center of Nanjing Medical University, Wuxi, China.
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Beckmann NM. The Rising Utilization of Opportunistic CT Screening and Machine Learning in Bone Mineral Density. Can Assoc Radiol J 2023; 74:616-617. [PMID: 37147917 DOI: 10.1177/08465371231176716] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023] Open
Affiliation(s)
- Nicholas M Beckmann
- Department of Diagnostic and Interventional Imaging, UTHealth - McGovern School of Medicine, Houston, TX, USA
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Liu D, Hu Z, Tang Z, Li P, Yuan W, Li F, Chen Q, Min W, Zhao C. Early risk assessment and prediction model for osteoporosis based on traditional Chinese medicine syndromes. Heliyon 2023; 9:e21501. [PMID: 38027808 PMCID: PMC10663826 DOI: 10.1016/j.heliyon.2023.e21501] [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: 06/16/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Objective To evaluate the risk factors of osteoporosis and establish a risk prediction model based on routine clinical information and traditional Chinese medicine (TCM) syndromes. Methods Adults aged 30-82 who lived in 12 grass-roots communities or rural towns in Shanghai, Jilin Province, and Jiangsu Province from December 2019 to January 2022 through a multi-stage sampling method were included in this study. The risk factors and risk prediction of osteoporosis in women and men were explored and established by univariate analysis and multivariate logistic regression model. ROC curve and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the prediction model. Results A total of 3000 subjects including 2243 females (75 %) and 757 males (25 %) were included in this study. The logistic prediction model of osteoporosis in women was Logit (P) = -2.946 + 0.960 (age ≥50 years old) + 0.633 (BMI ≥24 kg/m2) - 0.545 (daily exposure to sunlight >30 min) + 0.519 (no intake of dairy products) + 0.827 (coronary heart disease) + 0.383 (lumbar disc herniation) + 0.654 (no intake of calcium tablets and vitamin D) - 0.509 (insomnia) + 0.580 (flushed face and congested eyes) + 1.194 (thready and rapid pulse) + 1.309 (sunken and slow pulse). The logistic prediction model of osteoporosis in men was Logit (P) = -1.152-0.644 (daily exposure to sunlight >30 min) + 0.975 (no intake of calcium tablets and vitamin D) - 0.488 (insomnia). The area under the ROC curve (AUC) of female and male osteoporosis prediction models was 0.743 and 0.679, respectively. The Hosmer-Lemeshow goodness-of-fit test was >0.5. Conclusions There are some significant differences in risk factors between female and male patients with osteoporosis. The risk of osteoporosis are found to be associated with TCM syndromes, and osteoporosis risk prediction models based on routine clinical information and TCM syndrome is effective.
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Affiliation(s)
- Dan Liu
- Department of Rehabilitation Medicine, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Zhijun Hu
- Department of Rehabilitation Medicine, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Zhanying Tang
- Department of Rehabilitation Medicine, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Pan Li
- Department of Rehabilitation Medicine, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Weina Yuan
- Department of Rehabilitation Medicine, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Fangfang Li
- Department of Rehabilitation Medicine, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Qian Chen
- Department of Rehabilitation Medicine, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
| | - Wen Min
- Department of Orthopedics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210004, Jiangsu Province, China
| | - Changwei Zhao
- Department of Orthopedics, Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, 130021, Jilin Province, China
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Zhang K, Wang M, Han W, Yi W, Yang D. Construction of a predictive model for osteoporosis risk in men: using the IOF 1-min osteoporosis test. J Orthop Surg Res 2023; 18:770. [PMID: 37821993 PMCID: PMC10568916 DOI: 10.1186/s13018-023-04266-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/04/2023] [Indexed: 10/13/2023] Open
Abstract
OBJECTIVE To construct a clinical prediction nomogram model using the 1-min IOF osteoporosis risk test as an evaluation tool for male osteoporosis. METHODS The 1-min test results and the incidence of osteoporosis were collected from 354 patients in the osteoporotic clinic of our hospital. LASSO regression model and multi-factor logistic regression were used to analyze the risk factors of osteoporosis in patients, and the risk prediction model of osteoporosis was established. Verify with an additional 140 objects. RESULTS We used logistic regression to construct a nomogram model. According to the model, the AUC value of the training set was 0.760 (0.704-0.817). The validation set has an AUC value of 0.806 (0.733-0.879). The test set AUC value is 0.714 (0.609-0.818). The calibration curve shows that its advantage is that the deviation correction curve of the nomogram model can maintain a good consistency with the ideal curve. In terms of clinical applicability, compared with the "total intervention" and "no intervention" schemes, the clinical net return rate of the nomogram model showed certain advantages. CONCLUSION Using the 1-min osteoporosis risk test provided by IOF, we built a male osteoporosis risk prediction model with good prediction effect, which can provide greater reference and help for clinicians.
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Affiliation(s)
- Kun Zhang
- Health Science Center, Shenzhen University, Shenzhen, 518061, Guangdong, China
| | - Min Wang
- Department of Spine Surgery, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, 518052, Guangdong, China
| | - Weidong Han
- Department of Pain, The 8th Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518033, Guangdong, China
| | - Weihong Yi
- Department of Spine Surgery, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, 518052, Guangdong, China
| | - Dazhi Yang
- Department of Spine Surgery, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, 518052, Guangdong, China.
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Zhang J, Xu Z, Fu Y, Chen L. Prediction of the Risk of Bone Mineral Density Decrease in Type 2 Diabetes Mellitus Patients Based on Traditional Multivariate Logistic Regression and Machine Learning: A Preliminary Study. Diabetes Metab Syndr Obes 2023; 16:2885-2898. [PMID: 37744700 PMCID: PMC10517691 DOI: 10.2147/dmso.s422515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/05/2023] [Indexed: 09/26/2023] Open
Abstract
Purpose There remains a lack of a machine learning (ML) model incorporating body composition to assess the risk of bone mineral density (BMD) decreases in type 2 diabetes mellitus (T2DM) patients. We aimed to use ML algorithms and the traditional multivariate logistic regression to establish prediction models for BMD decreases in T2DM patients over 50 years of age, and compare the performance of the two methods. Patients and Methods This cross-sectional study was conducted among 450 patients with T2DM from 1 August 2016 to 31 December 2022. The participants were divided into a normal BMD group and a decreased BMD group. Traditional multivariate logistic regression and six ML algorithms were selected to construct male and female models. Two nomograms were constructed to evaluate the risk of BMD decreases in the male and female T2DM patients, respectively. The ML models with the highest area under the curve (AUC) were compared with the traditional multivariate logistic regression models in terms of discriminant ability and clinical applicability. Results The optimal ML model was the extreme gradient boost (XGBoost) model. The AUCs of the traditional multivariate logistic regression and the XGBoost models were 0.722 and 0.800 in the male testing dataset, respectively, and 0.876 and 0.880 in the female testing dataset, respectively. The decision curve analysis results suggested that using the XGBoost models to predict the risk of BMD decreases obtained more net benefits compared with the traditional models in both sexes. Conclusion We preliminarily proved that the XGBoost models outperformed most other ML models in both sexes and achieved higher accuracy than traditional analyses. Due to the limited sample size in the study, it is necessary to validate our findings in larger prospective cohort studies.
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Affiliation(s)
- Junli Zhang
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Soochow University, Changzhou, People’s Republic of China
| | - Zhenghui Xu
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Soochow University, Changzhou, People’s Republic of China
| | - Yu Fu
- Department of Clinical Nutrition, The Third Affiliated Hospital of Soochow University, Changzhou, People’s Republic of China
| | - Lu Chen
- Department of Endocrinology and Metabolism, The Third Affiliated Hospital of Soochow University, Changzhou, People’s Republic of China
- Department of Clinical Nutrition, The Third Affiliated Hospital of Soochow University, Changzhou, People’s Republic of China
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Constant C, Aubin CE, Kremers HM, Garcia DVV, Wyles CC, Rouzrokh P, Larson AN. The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications. NORTH AMERICAN SPINE SOCIETY JOURNAL 2023; 15:100236. [PMID: 37599816 PMCID: PMC10432249 DOI: 10.1016/j.xnsj.2023.100236] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 06/14/2023] [Indexed: 08/22/2023]
Abstract
Background Artificial intelligence is a revolutionary technology that promises to assist clinicians in improving patient care. In radiology, deep learning (DL) is widely used in clinical decision aids due to its ability to analyze complex patterns and images. It allows for rapid, enhanced data, and imaging analysis, from diagnosis to outcome prediction. The purpose of this study was to evaluate the current literature and clinical utilization of DL in spine imaging. Methods This study is a scoping review and utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2012 to 2021. A search in PubMed, Web of Science, Embased, and IEEE Xplore databases with syntax specific for DL and medical imaging in spine care applications was conducted to collect all original publications on the subject. Specific data was extracted from the available literature, including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest. Results A total of 365 studies (total sample of 232,394 patients) were included and grouped into 4 general applications: diagnostic tools, clinical decision support tools, automated clinical/instrumentation assessment, and clinical outcome prediction. Notable disparities exist in the selected algorithms and the training across multiple disparate databases. The most frequently used algorithms were U-Net and ResNet. A DL model was developed and validated in 92% of included studies, while a pre-existing DL model was investigated in 8%. Of all developed models, only 15% of them have been externally validated. Conclusions Based on this scoping review, DL in spine imaging is used in a broad range of clinical applications, particularly for diagnosing spinal conditions. There is a wide variety of DL algorithms, database characteristics, and training methods. Future studies should focus on external validation of existing models before bringing them into clinical use.
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Affiliation(s)
- Caroline Constant
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
- AO Research Institute Davos, Clavadelerstrasse 8, CH 7270, Davos, Switzerland
| | - Carl-Eric Aubin
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
| | - Hilal Maradit Kremers
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Diana V. Vera Garcia
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Cody C. Wyles
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Pouria Rouzrokh
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Radiology Informatics Laboratory, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Annalise Noelle Larson
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
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Shen L, Gao C, Hu S, Kang D, Zhang Z, Xia D, Xu Y, Xiang S, Zhu Q, Xu G, Tang F, Yue H, Yu W, Zhang Z. Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs. J Bone Miner Res 2023; 38:1278-1287. [PMID: 37449775 DOI: 10.1002/jbmr.4879] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/18/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
Osteoporotic vertebral fracture (OVF) is a risk factor for morbidity and mortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep-learning-based vertebral fracture diagnostic system using area loss ratio, which assisted a multitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participants were recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X-ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH model were 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity for moderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. © 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)
- Li Shen
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chao Gao
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shundong Hu
- Department of Radiology, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dan Kang
- Shanghai Jiyinghui Intelligent Technology Co, Shanghai, China
| | - Zhaogang Zhang
- Shanghai Jiyinghui Intelligent Technology Co, Shanghai, China
| | - Dongdong Xia
- Department of Orthopaedics, Ning Bo First Hospital, Zhejiang, China
| | - Yiren Xu
- Department of Radiology, Ning Bo First Hospital, Zhejiang, China
| | - Shoukui Xiang
- Department of Endocrinology and Metabolism, The First People's Hospital of Changzhou, Changzhou, China
| | - Qiong Zhu
- Kangjian Community Health Service Center, Shanghai, China
| | - GeWen Xu
- Kangjian Community Health Service Center, Shanghai, China
| | - Feng Tang
- Jinhui Community Health Service Center, Shanghai, China
| | - Hua Yue
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Yu
- Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Zhenlin Zhang
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Bae JH, Seo JW, Kim DY. Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023. Front Public Health 2023; 11:1241388. [PMID: 37614451 PMCID: PMC10443707 DOI: 10.3389/fpubh.2023.1241388] [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] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 07/24/2023] [Indexed: 08/25/2023] Open
Abstract
Introduction Physical fitness is regarded as a significant indicator of sarcopenia. This study aimed to develop and evaluate a deep-learning model for predicting the decline in physical fitness due to sarcopenia in individuals with potential sarcopenia. Methods This study used the 2010-2023 Korean National Physical Fitness Award data. The data comprised exercise- and health-related measurements in Koreans aged >65 years and included body composition and physical fitness variables. Appendicular muscle mass (ASM) was calculated as ASM/height2 to define normal and possible sarcopenia. The deep-learning model was created with EarlyStopping and ModelCheckpoint to prevent overfitting and was evaluated using stratified k-fold cross-validation (k = 5). The model was trained and tested using training data and validation data from each fold. The model's performance was assessed using a confusion matrix, receiver operating characteristic curve, and area under the curve. The average performance metrics obtained from each cross-validation were determined. For the analysis of feature importance, SHAP, permutation feature importance, and LIME were employed as model-agnostic explanation methods. Results The deep-learning model proved effective in distinguishing from sarcopenia, with an accuracy of 87.55%, precision of 85.57%, recall of 90.34%, and F1 score of 87.89%. Waist circumference (WC, cm), absolute grip strength (kg), and body fat (BF, %) had an influence on the model output. SHAP, LIME, and permutation feature importance analyses revealed that WC and absolute grip strength were the most important variables. WC, figure-of-8 walk, BF, timed up-and-go, and sit-and-reach emerged as key factors for predicting possible sarcopenia. Conclusion The deep-learning model showed high accuracy and recall with respect to possible sarcopenia prediction. Considering the need for the development of a more detailed and accurate sarcopenia prediction model, the study findings hold promise for enhancing sarcopenia prediction using deep learning.
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Affiliation(s)
- Jun-Hyun Bae
- Able-Art Sport, Department of Theory, Hyupsung University, Hwaseong, Gyeonggi-do, Republic of Korea
| | - Ji-won Seo
- Department of Physical Education, Seoul National University, Seoul, Republic of Korea
| | - Dae Young Kim
- Senior Exercise Rehabilitation Laboratory, Department of Gerokinesiology, Kyungil University, Gyeongsan, Gyeongsangbuk-do, Republic of Korea
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Albuquerque GA, Carvalho DDA, Cruz AS, Santos JPQ, Machado GM, Gendriz IS, Fernandes FRS, Barbalho IMP, Santos MM, Teixeira CAD, Henriques JMO, Gil P, Neto ADD, Campos ALPS, Lima JG, Paiva JC, Morais AHF, Lima TS, Valentim RAM. Osteoporosis screening using machine learning and electromagnetic waves. Sci Rep 2023; 13:12865. [PMID: 37553424 PMCID: PMC10409756 DOI: 10.1038/s41598-023-40104-w] [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: 03/20/2023] [Accepted: 08/04/2023] [Indexed: 08/10/2023] Open
Abstract
Osteoporosis is a disease characterized by impairment of bone microarchitecture that causes high socioeconomic impacts in the world because of fractures and hospitalizations. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing the disease, access to DXA in developing countries is still limited due to its high cost, being present only in specialized hospitals. In this paper, we analyze the performance of Osseus, a low-cost portable device based on electromagnetic waves that measures the attenuation of the signal that crosses the medial phalanx of a patient's middle finger and was developed for osteoporosis screening. The analysis is carried out by predicting changes in bone mineral density using Osseus measurements and additional common risk factors used as input features to a set of supervised classification models, while the results from DXA are taken as target (real) values during the training of the machine learning algorithms. The dataset consisted of 505 patients who underwent osteoporosis screening with both devices (DXA and Osseus), of whom 21.8% were healthy and 78.2% had low bone mineral density or osteoporosis. A cross-validation with k-fold = 5 was considered in model training, while 20% of the whole dataset was used for testing. The obtained performance of the best model (Random Forest) presented a sensitivity of 0.853, a specificity of 0.879, and an F1 of 0.859. Since the Random Forest (RF) algorithm allows some interpretability of its results (through the impurity check), we were able to identify the most important variables in the classification of osteoporosis. The results showed that the most important variables were age, body mass index, and the signal attenuation provided by Osseus. The RF model, when used together with Osseus measurements, is effective in screening patients and facilitates the early diagnosis of osteoporosis. The main advantages of such early screening are the reduction of costs associated with exams, surgeries, treatments, and hospitalizations, as well as improved quality of life for patients.
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Affiliation(s)
- Gabriela A Albuquerque
- Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil.
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil.
| | - Dionísio D A Carvalho
- Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil
| | - Agnaldo S Cruz
- Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil
| | - João P Q Santos
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil
| | | | - Ignácio S Gendriz
- Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil
| | | | | | - Marquiony M Santos
- Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil
| | - César A D Teixeira
- Department of Informatics Engineering, Univ. Coimbra, Centre for Informatics and Systems of the University of Coimbra (CISUC), Coimbra, Portugal
| | - Jorge M O Henriques
- Department of Informatics Engineering, Univ. Coimbra, Centre for Informatics and Systems of the University of Coimbra (CISUC), Coimbra, Portugal
| | - Paulo Gil
- Department of Electrical and Computer Engineering, School of Science and Technology, New University of Lisbon, Lisbon, Portugal
| | - Adrião D D Neto
- Post-Graduation Program on Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - Antonio L P S Campos
- Post-Graduation Program on Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - Josivan G Lima
- University Hospital Onofre Lopes, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil
| | - Jailton C Paiva
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil
| | - Antonio H F Morais
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal, RN, Brazil
| | - Thaisa Santos Lima
- Laboratory of Technological Innovation in Health (LAIS), Natal, RN, Brazil
- Ministry of Health, Brasília, Brazil
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Verhoeven F, Wendling D, Prati C. ChatGPT: when artificial intelligence replaces the rheumatologist in medical writing. Ann Rheum Dis 2023; 82:1015-1017. [PMID: 37041067 PMCID: PMC10359572 DOI: 10.1136/ard-2023-223936] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/06/2023] [Indexed: 04/13/2023]
Abstract
In this editorial we discuss the place of artificial intelligence (AI) in the writing of scientific articles and especially editorials. We asked chatGPT « to write an editorial for Annals of Rheumatic Diseases about how AI may replace the rheumatologist in editorial writing ». chatGPT's response is diplomatic and describes AI as a tool to help the rheumatologist but not replace him. AI is already used in medicine, especially in image analysis, but the domains are infinite and it is possible that AI could quickly help or replace rheumatologists in the writing of scientific articles. We discuss the ethical aspects and the future role of rheumatologists.
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Affiliation(s)
- Frank Verhoeven
- Rheumatology, CHU Besancon, Besancon, France
- EA 4267 PEPITE, Université de Franche-Comté, Besancon, France
| | - Daniel Wendling
- Rheumatology, CHU Besancon, Besancon, France
- EA4266 EPILAB, Université de Franche-Comté, Besancon, France
| | - Clément Prati
- Rheumatology, CHU Besancon, Besancon, France
- EA 4267 PEPITE, Université de Franche-Comté, Besancon, France
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Madrid-García A, Merino-Barbancho B, Rodríguez-González A, Fernández-Gutiérrez B, Rodríguez-Rodríguez L, Menasalvas-Ruiz E. Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature. Semin Arthritis Rheum 2023; 61:152213. [PMID: 37315379 DOI: 10.1016/j.semarthrit.2023.152213] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 06/16/2023]
Abstract
The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.
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Affiliation(s)
- Alfredo Madrid-García
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain; Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain.
| | - Beatriz Merino-Barbancho
- Escuela Técnica Superior de Ingenieros de Telecomunicación. Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain
| | | | - Benjamín Fernández-Gutiérrez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Luis Rodríguez-Rodríguez
- Grupo de Patología Musculoesquelética. Hospital Clínico San Carlos, Prof. Martin Lagos s/n, Madrid, 28040, Spain
| | - Ernestina Menasalvas-Ruiz
- Centro de Tecnología Biomédica. Universidad Politécnica de Madrid, Pozuelo de Alarcón, Madrid, 28223, Spain
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Chen YC, Li YT, Kuo PC, Cheng SJ, Chung YH, Kuo DP, Chen CY. Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography. Eur Radiol 2023; 33:5097-5106. [PMID: 36719495 DOI: 10.1007/s00330-023-09421-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 12/24/2022] [Accepted: 01/01/2023] [Indexed: 02/01/2023]
Abstract
OBJECTIVE This study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT). METHODS A total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient's bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation. RESULTS The model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05. CONCLUSION The combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening. KEY POINTS • This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.
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Affiliation(s)
- Yung-Chieh Chen
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Tien Li
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan
| | - Po-Chih Kuo
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Sho-Jen Cheng
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Hsiang Chung
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
| | - Duen-Pang Kuo
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan.
| | - Cheng-Yu Chen
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Radiology, National Defense Medical Center, Taipei, Taiwan
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Cui J, Liu CL, Jennane R, Ai S, Dai K, Tsai TY. A highly generalized classifier for osteoporosis radiography based on multiscale fractal, lacunarity, and entropy distributions. Front Bioeng Biotechnol 2023; 11:1054991. [PMID: 37274169 PMCID: PMC10235631 DOI: 10.3389/fbioe.2023.1054991] [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: 09/27/2022] [Accepted: 03/20/2023] [Indexed: 06/06/2023] Open
Abstract
Background: Osteoporosis is a common degenerative disease with high incidence among aging populations. However, in regular radiographic diagnostics, asymptomatic osteoporosis is often overlooked and does not include tests for bone mineral density or bone trabecular condition. Therefore, we proposed a highly generalized classifier for osteoporosis radiography based on the multiscale fractal, lacunarity, and entropy distributions. Methods: We collected a total of 104 radiographs (92 for training and 12 for testing) of lumbar spine L4 and divided them into three groups (normal, osteopenia, and osteoporosis). In parallel, 174 radiographs (116 for training and 58 for testing) of calcaneus from health and osteoporotic fracture groups were collected. The texture feature data of all the radiographs were pulled out and analyzed. The Davies-Bouldin index was applied to optimize hyperparameters of feature counting. Neighborhood component analysis was performed to reduce feature dimension and increase generalization. A support vector machine classifier was trained with only the most effective six features for each binary classification scenario. The accuracy and sensitivity performance were estimated by calculating the area under the curve. Results: Interpretable feature trends of osteoporotic pathological changes were depicted. On the spine test dataset, the accuracy and sensitivity of binary classifiers were 0.851 (95% CI: 0.730-0.922), 0.813 (95% CI: 0.718-0.878), and 0.936 (95% CI: 0.826-1) for osteoporosis diagnosis; 0.721 (95% CI: 0.578-0.824), 0.675 (95% CI: 0.563-0.772), and 0.774 (95% CI: 0.635-0.878) for osteopenia diagnosis; and 0.935 (95% CI: 0.830-0.968), 0.928 (95% CI: 0.863-0.963), and 0.910 (95% CI: 0.746-1) for osteoporosis diagnosis from osteopenia. On the calcaneus test dataset, they were 0.767 (95% CI: 0.629-0.879), 0.672 (95% CI: 0.545-0.793), and 0.790 (95% CI: 0.621-0.923) for osteoporosis diagnosis. Conclusion: This method showed the capacity of resisting disturbance on lateral spine radiographs and high generalization on the calcaneus dataset. Pixel-wise texture features not only helped to understand osteoporosis on radiographs better but also shed new light on computer-aided osteopenia and osteoporosis diagnosis.
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Affiliation(s)
- Jingnan Cui
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Cheng Lei Liu
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rachid Jennane
- IDP Institute, UMR CNRS 7013, University of Orléans, Orléans, France
| | - Songtao Ai
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kerong Dai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Department of Orthopaedic Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Tsung-Yuan Tsai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Levi R, Garoli F, Battaglia M, Rizzo DAA, Mollura M, Savini G, Riva M, Tomei M, Ortolina A, Fornari M, Rohatgi S, Angelotti G, Savevski V, Mazziotti G, Barbieri R, Grimaldi M, Politi LS. CT-based radiomics can identify physiological modifications of bone structure related to subjects' age and sex. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01641-6. [PMID: 37147473 DOI: 10.1007/s11547-023-01641-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 04/26/2023] [Indexed: 05/07/2023]
Abstract
PURPOSE Radiomics of vertebral bone structure is a promising technique for identification of osteoporosis. We aimed at assessing the accuracy of machine learning in identifying physiological changes related to subjects' sex and age through analysis of radiomics features from CT images of lumbar vertebrae, and define its generalizability across different scanners. MATERIALS AND METHODS We annotated spherical volumes-of-interest (VOIs) in the center of the vertebral body for each lumbar vertebra in 233 subjects who had undergone lumbar CT for back pain on 3 different scanners, and we evaluated radiomics features from each VOI. Subjects with history of bone metabolism disorders, cancer, and vertebral fractures were excluded. We performed machine learning classification and regression models to identify subjects' sex and age respectively, and we computed a voting model which combined predictions. RESULTS The model was trained on 173 subjects and tested on an internal validation dataset of 60. Radiomics was able to identify subjects' sex within single CT scanner (ROC AUC: up to 0.9714), with lower performance on the combined dataset of the 3 scanners (ROC AUC: 0.5545). Higher consistency among different scanners was found in identification of subjects' age (R2 0.568 on all scanners, MAD 7.232 years), with highest results on a single CT scanner (R2 0.667, MAD 3.296 years). CONCLUSION Radiomics features are able to extract biometric data from lumbar trabecular bone, and determine bone modifications related to subjects' sex and age with great accuracy. However, acquisition from different CT scanners reduces the accuracy of the analysis.
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Affiliation(s)
- Riccardo Levi
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Federico Garoli
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Massimiliano Battaglia
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Dario A A Rizzo
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Maximilliano Mollura
- Department of Electronics, Information and Bioengineering, Politecnico Di Milano, 20133, Milan, Italy
| | - Giovanni Savini
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Marco Riva
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neurosurgery, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Massimo Tomei
- Department of Neurosurgery, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Alessandro Ortolina
- Department of Neurosurgery, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Maurizio Fornari
- Department of Neurosurgery, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Saurabh Rohatgi
- Department of Neuroradiology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Giovanni Angelotti
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Gherardo Mazziotti
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Metabolic Bone Diseases and Osteoporosis Section, Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Riccardo Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico Di Milano, 20133, Milan, Italy
| | - Marco Grimaldi
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Letterio S Politi
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy.
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy.
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End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:3018320. [PMID: 36970245 PMCID: PMC10036193 DOI: 10.1155/2023/3018320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 02/23/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energy X-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.
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Dimai HP. New Horizons: Artificial Intelligence Tools for Managing Osteoporosis. J Clin Endocrinol Metab 2023; 108:775-783. [PMID: 36477337 PMCID: PMC9999362 DOI: 10.1210/clinem/dgac702] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022]
Abstract
Osteoporosis is a disease characterized by low bone mass and microarchitectural deterioration leading to increased bone fragility and fracture risk. Typically, osteoporotic fractures occur at the spine, hip, distal forearm, and proximal humerus, but other skeletal sites may be affected as well. One of the major challenges in the management of osteoporosis lies in the fact that although the operational diagnosis is based on bone mineral density (BMD) as measured by dual x-ray absorptiometry, the majority of fractures occur at nonosteoporotic BMD values. Furthermore, osteoporosis often remains undiagnosed regardless of the low severity of the underlying trauma. Also, there is only weak consensus among the major guidelines worldwide, when to treat, whom to treat, and which drug to use. Against this background, increasing efforts have been undertaken in the past few years by artificial intelligence (AI) developers to support and improve the management of this disease. The performance of many of these newly developed AI algorithms have been shown to be at least comparable to that of physician experts, or even superior. However, even if study results appear promising at a first glance, they should always be interpreted with caution. Use of inadequate reference standards or selection of variables that are of little or no value in clinical practice are limitations not infrequently found. Consequently, there is a clear need for high-quality clinical research in this field of AI. This could, eg, be achieved by establishing an internationally consented "best practice framework" that considers all relevant stakeholders.
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Affiliation(s)
- Hans Peter Dimai
- Correspondence: Hans Peter Dimai, MD, Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Auenbruggerplatz 15, A-8036 Graz, Austria.
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Uygur MM, Frara S, di Filippo L, Giustina A. New tools for bone health assessment in secreting pituitary adenomas. Trends Endocrinol Metab 2023; 34:231-242. [PMID: 36869001 DOI: 10.1016/j.tem.2023.01.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 01/22/2023] [Accepted: 01/24/2023] [Indexed: 03/05/2023]
Abstract
Pituitary hormones regulate skeletal physiology, and excess levels affect bone remodeling and alter bone microstructure. Vertebral fractures (VFs) are an early phenomenon of impaired bone health in secreting pituitary adenomas. However, they are not accurately predicted by areal bone mineral density (BMD). Emerging data demonstrate that a morphometric approach is essential for evaluating bone health in this clinical setting and is considered to be the gold standard method in acromegaly. Several novel tools have been proposed as alternative or additional methods for the prediction of fractures, particularly in pituitary-driven osteopathies. This review highlights the novel potential biomarkers and diagnostic methods for bone fragility, including their pathophysiological, clinical, radiological, and therapeutic implications in acromegaly, prolactinomas, and Cushing's disease.
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Affiliation(s)
- Meliha Melin Uygur
- Institute of Endocrine and Metabolic Sciences, Università Vita-Salute San Raffaele, and Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Ospedale San Raffaele, Milan, Italy; Department of Endocrinology and Metabolism Disease, School of Medicine, Recep Tayyip Erdoğan University, Rize, Turkey.
| | - Stefano Frara
- Institute of Endocrine and Metabolic Sciences, Università Vita-Salute San Raffaele, and Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Ospedale San Raffaele, Milan, Italy
| | - Luigi di Filippo
- Institute of Endocrine and Metabolic Sciences, Università Vita-Salute San Raffaele, and Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Ospedale San Raffaele, Milan, Italy
| | - Andrea Giustina
- Institute of Endocrine and Metabolic Sciences, Università Vita-Salute San Raffaele, and Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Ospedale San Raffaele, Milan, Italy
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Gu S, Wang L, Han R, Liu X, Wang Y, Chen T, Zheng Z. Detection of sarcopenia using deep learning-based artificial intelligence body part measure system (AIBMS). Front Physiol 2023; 14:1092352. [PMID: 36776966 PMCID: PMC9909827 DOI: 10.3389/fphys.2023.1092352] [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: 11/10/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
Background: Sarcopenia is an aging syndrome that increases the risks of various adverse outcomes, including falls, fractures, physical disability, and death. Sarcopenia can be diagnosed through medical images-based body part analysis, which requires laborious and time-consuming outlining of irregular contours of abdominal body parts. Therefore, it is critical to develop an efficient computational method for automatically segmenting body parts and predicting diseases. Methods: In this study, we designed an Artificial Intelligence Body Part Measure System (AIBMS) based on deep learning to automate body parts segmentation from abdominal CT scans and quantification of body part areas and volumes. The system was developed using three network models, including SEG-NET, U-NET, and Attention U-NET, and trained on abdominal CT plain scan data. Results: This segmentation model was evaluated using multi-device developmental and independent test datasets and demonstrated a high level of accuracy with over 0.9 DSC score in segment body parts. Based on the characteristics of the three network models, we gave recommendations for the appropriate model selection in various clinical scenarios. We constructed a sarcopenia classification model based on cutoff values (Auto SMI model), which demonstrated high accuracy in predicting sarcopenia with an AUC of 0.874. We used Youden index to optimize the Auto SMI model and found a better threshold of 40.69. Conclusion: We developed an AI system to segment body parts in abdominal CT images and constructed a model based on cutoff value to achieve the prediction of sarcopenia with high accuracy.
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Affiliation(s)
- Shangzhi Gu
- Department of Computer Science and Technology, Institute for Artificial Intelligence, and BNRist, Tsinghua University, Beijing, China,School of Medicine, Tsinghua University, Beijing, China
| | - Lixue Wang
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Rong Han
- Department of Computer Science and Technology, Institute for Artificial Intelligence, and BNRist, Tsinghua University, Beijing, China
| | - Xiaohong Liu
- Department of Computer Science and Technology, Institute for Artificial Intelligence, and BNRist, Tsinghua University, Beijing, China
| | - Yizhe Wang
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Ting Chen
- Department of Computer Science and Technology, Institute for Artificial Intelligence, and BNRist, Tsinghua University, Beijing, China,Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China,*Correspondence: Ting Chen, ; Zhuozhao Zheng,
| | - Zhuozhao Zheng
- Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China,*Correspondence: Ting Chen, ; Zhuozhao Zheng,
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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: 0] [Impact Index Per Article: 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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46
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Burns JS, Kassem M. Identifying Biomarkers for Osteogenic Potency Assay Development. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1420:39-58. [PMID: 37258783 DOI: 10.1007/978-3-031-30040-0_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
There has been extensive exploration of how cells may serve as advanced therapy medicinal products to treat skeletal pathologies. Osteoblast progenitors responsible for production of extracellular matrix that is subsequently mineralized during bone formation have been characterised as a rare bone marrow subpopulation of cell culture plastic adherent cells. Conveniently, they proliferate to form single-cell derived colonies of fibroblastoid cells, termed colony forming unit fibroblasts that can subsequently differentiate to aggregates resembling small areas of cartilage or bone. However, donor heterogeneity and loss of osteogenic differentiation capacity during extended cell culture have made the discovery of reliable potency assay biomarkers difficult. Nonetheless, functional osteoblast models derived from telomerised human bone marrow stromal cells have allowed extensive comparative analysis of gene expression, microRNA, morphological phenotypes and secreted proteins. This chapter highlights numerous insights into the molecular mechanisms underpinning osteogenic differentiation of multipotent stromal cells and bone formation, discussing aspects involved in the choice of useful biomarkers for functional attributes that can be quantitively measured in osteogenic potency assays.
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Affiliation(s)
- Jorge S Burns
- Department of Environmental and Prevention Sciences, University of Ferrara, Ferrara, Italy.
| | - Moustapha Kassem
- University Hospital of Odense, University of Southern Denmark, Odense, Denmark
- Danish Stem Cell Center, University of Copenhagen, Copenhagen, Denmark
- College of Medicine, King Saud University, Riyadh, Saudi Arabia
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47
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Walle M, Eggemann D, Atkins PR, Kendall JJ, Stock K, Müller R, Collins CJ. Motion grading of high-resolution quantitative computed tomography supported by deep convolutional neural networks. Bone 2023; 166:116607. [PMID: 36368464 DOI: 10.1016/j.bone.2022.116607] [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: 06/07/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022]
Abstract
Image quality degradation due to subject motion confounds the precision and reproducibility of measurements of bone density, morphology and mechanical properties from high-resolution peripheral quantitative computed tomography (HR-pQCT). Time-consuming operator-based scoring of motion artefacts remains the gold standard to determine the degree of acceptable motion. However, due to the subjectiveness of manual grading, HR-pQCT scans of poor quality, which cannot be used for analysis, may be accepted upon initial review, leaving patients with incomplete or inaccurate imaging results. Convolutional Neural Networks (CNNs) enable fast image analysis with relatively few pre-processing requirements in an operator-independent and fully automated way for image classification tasks. This study aimed to develop a CNN that can predict motion scores from HR-pQCT images, while also being aware of uncertain predictions that require manual confirmation. The CNN calculated motion scores within seconds and achieved a high F1-score (86.8 ± 2.8 %), with good precision (87.5 ± 2.7 %), recall (86.7 ± 2.9 %) and a substantial agreement with the ground truth measured by Cohen's kappa (κ = 68.6 ± 6.2 %); motion scores of the test dataset were predicted by the algorithm with comparable accuracy, precision, sensitivity and agreement as by the operators (p > 0.05). This post-processing approach may be used to assess the effect of motion scores on microstructural analysis and can be immediately implemented into clinical protocols, significantly reducing the time for quality assessment and control of HR-pQCT scans.
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Affiliation(s)
- Matthias Walle
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | | | - Penny R Atkins
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Jack J Kendall
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Kerstin Stock
- University Hospital for Orthopedics and Traumatology, Innsbruck, Austria
| | - Ralph Müller
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Caitlyn J Collins
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland; Virginia Tech, Department of Biomedical Engineering and Mechanics, Blacksburg, United States.
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Minonzio JG, Ramiandrisoa D, Schneider J, Kohut E, Streichhahn M, Stervbo U, Wirth R, Westhoff TH, Raum K, Babel N. Bi-Directional Axial Transmission measurements applied in a clinical environment. PLoS One 2022; 17:e0277831. [PMID: 36584002 PMCID: PMC9803229 DOI: 10.1371/journal.pone.0277831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 11/03/2022] [Indexed: 12/31/2022] Open
Abstract
Accurate measurement of cortical bone parameters may improve fracture risk assessment and help clinicians on the best treatment strategy. Patients at risk of fracture are currently detected using the current X-Ray gold standard DXA (Dual XRay Absorptiometry). Different alternatives, such as 3D X-Rays, Magnetic Resonance Imaging or Quantitative Ultrasound (QUS) devices, have been proposed, the latter having advantages of being portable and sensitive to mechanical and geometrical properties. The objective of this cross-sectional study was to evaluate the performance of a Bi-Directional Axial Transmission (BDAT) device used by trained operators in a clinical environment with older subjects. The device, positioned at one-third distal radius, provides two velocities: VFAS (first arriving signal) and VA0 (first anti-symmetrical guided mode). Moreover, two parameters are obtained from an inverse approach: Ct.Th (cortical thickness) and Ct.Po (cortical porosity), along with their ratio Ct.Po/Ct.Th. The areal bone mineral density (aBMD) was obtained using DXA at the femur and spine. One hundred and six patients (81 women, 25 men) from Marien Hospital and St. Anna Hospital (Herne, Germany) were included in this study. Age ranged from 41 to 95 years, while body mass index (BMI) ranged from 16 to 47 kg.m-2. Three groups were considered: 79 non-fractured patients (NF, 75±13years), 27 with non-traumatic fractures (F, 80±9years) including 14 patients with non-vertebral fractures (NVF, 84±7years). Weak to moderate significant Spearman correlations (R ranging from 0.23 to 0.53, p < 0.05) were found between ultrasound parameters and age, BMI. Using multivariate Partial Least Square discrimination analyses with Leave-One-Out Cross-Validation (PLS-LOOCV), we found the combination of VFAS and the ratio Ct.Po/Ct.Th to be predictive for all non traumatic fractures (F) with the odds ratio (OR) equals to 2.5 [1.6-3.4] and the area under the ROC curve (AUC) equal to 0.63 [0.62-0.65]. For the group NVF, combination of four parameters VA0. Ct.Th, Ct.Po and Ct.Po/Ct.Po, along with age provides a discrimination model with OR and AUC equals to 7.5 [6.0-9.1] and 0.75 [0.73-0.76]. When restricted to a smaller population (87 patients) common to both BDAT and DXA, BDAT ORs and AUCs are comparable or slightly higher to values obtained with DXA. The fracture risk assessment by BDAT method in older patients, in a clinical setting, suggests the benefit of the affordable and transportable device for the routine use.
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Affiliation(s)
- Jean-Gabriel Minonzio
- Sorbonne Université, INSERM UMR S 1146, CNRS UMR 7371, Laboratoire d’Imagerie Biomédicale, Paris, France
- Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso, Chile
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
- * E-mail:
| | | | - Johannes Schneider
- Berlin-Brandenburg School for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Germany
| | - Eva Kohut
- Medical Clinic I, Marien Hospital Herne, Ruhr University, Bochum, Herne, Germany
| | - Melanie Streichhahn
- Medical Clinic I, Marien Hospital Herne, Ruhr University, Bochum, Herne, Germany
| | - Ulrik Stervbo
- Berlin-Brandenburg School for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Germany
- Center for Translational Medicine and Immune Diagnostics Laboratory, Medical Department I, Marien Hospital Herne, Ruhr University, Bochum, Herne, Germany
| | - Rainer Wirth
- Department for Geriatric Medicine, Marien Hospital Herne, Ruhr University Bochum, Herne, Germany
| | - Timm Henning Westhoff
- Center for Translational Medicine and Immune Diagnostics Laboratory, Medical Department I, Marien Hospital Herne, Ruhr University, Bochum, Herne, Germany
| | - Kay Raum
- Berlin-Brandenburg School for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Germany
| | - Nina Babel
- Berlin-Brandenburg School for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Germany
- Center for Translational Medicine and Immune Diagnostics Laboratory, Medical Department I, Marien Hospital Herne, Ruhr University, Bochum, Herne, Germany
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Dzierżak R, Omiotek Z. Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis. SENSORS (BASEL, SWITZERLAND) 2022; 22:8189. [PMID: 36365886 PMCID: PMC9655338 DOI: 10.3390/s22218189] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
The aim of this study was to assess the possibility of using deep convolutional neural networks (DCNNs) to develop an effective method for diagnosing osteoporosis based on CT images of the spine. The research material included the CT images of L1 spongy tissue belonging to 100 patients (50 healthy and 50 diagnosed with osteoporosis). Six pre-trained DCNN architectures with different topological depths (VGG16, VGG19, MobileNetV2, Xception, ResNet50, and InceptionResNetV2) were used in the study. The best results were obtained for the VGG16 model characterised by the lowest topological depth (ACC = 95%, TPR = 96%, and TNR = 94%). A specific challenge during the study was the relatively small (for deep learning) number of observations (400 images). This problem was solved using DCNN models pre-trained on a large dataset and a data augmentation technique. The obtained results allow us to conclude that the transfer learning technique yields satisfactory results during the construction of deep models for the diagnosis of osteoporosis based on small datasets of CT images of the spine.
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50
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Osteoporosis Screening: Applied Methods and Technological Trends. Med Eng Phys 2022; 108:103887. [DOI: 10.1016/j.medengphy.2022.103887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/30/2022] [Accepted: 09/01/2022] [Indexed: 11/15/2022]
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