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Khadivi G, Akhtari A, Sharifi F, Zargarian N, Esmaeili S, Ahsaie MG, Shahbazi S. Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis. Osteoporos Int 2024:10.1007/s00198-024-07229-8. [PMID: 39177815 DOI: 10.1007/s00198-024-07229-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 08/10/2024] [Indexed: 08/24/2024]
Abstract
The current study aimed to systematically review the literature on the accuracy of artificial intelligence (AI) models for osteoporosis (OP) diagnosis using dental images. A thorough literature search was executed in October 2022 and updated in November 2023 across multiple databases, including PubMed, Scopus, Web of Science, and Google Scholar. The research targeted studies using AI models for OP diagnosis from dental radiographs. The main outcomes were the sensitivity and specificity of AI models regarding OP diagnosis. The "meta" package from the R Foundation was selected for statistical analysis. A random-effects model, along with 95% confidence intervals, was utilized to estimate pooled values. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was employed for risk of bias and applicability assessment. Among 640 records, 22 studies were included in the qualitative analysis and 12 in the meta-analysis. The overall sensitivity for AI-assisted OP diagnosis was 0.85 (95% CI, 0.70-0.93), while the pooled specificity equaled 0.95 (95% CI, 0.91-0.97). Conventional algorithms led to a pooled sensitivity of 0.82 (95% CI, 0.57-0.94) and a pooled specificity of 0.96 (95% CI, 0.93-0.97). Deep convolutional neural networks exhibited a pooled sensitivity of 0.87 (95% CI, 0.68-0.95) and a pooled specificity of 0.92 (95% CI, 0.83-0.96). This systematic review corroborates the accuracy of AI in OP diagnosis using dental images. Future research should expand sample sizes in test and training datasets and standardize imaging techniques to establish the reliability of AI-assisted methods in OP diagnosis through dental images.
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Affiliation(s)
- Gita Khadivi
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Abtin Akhtari
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farshad Sharifi
- Elderly Health Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Nicolette Zargarian
- School of Dentistry, Research Institute for Dental Sciences, Mkhitar Heratsi Yerevan State Medical University, Yerevan, Armenia
| | - Saharnaz Esmaeili
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Soheil Shahbazi
- Dental Research Center, Research Institute for Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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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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Comparison of the Classification Results Accuracy for CT Soft Tissue and Bone Reconstructions in Detecting the Porosity of a Spongy Tissue. J Clin Med 2022; 11:jcm11154526. [PMID: 35956142 PMCID: PMC9369728 DOI: 10.3390/jcm11154526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/09/2022] [Accepted: 07/31/2022] [Indexed: 02/04/2023] Open
Abstract
The aim of the study was to compare the accuracy of the classification pertaining to the results of two types of soft tissue and bone reconstructions of the spinal CT in detecting the porosity of L1 vertebral body spongy tissue. The dataset for each type of reconstruction (high-resolution bone reconstruction and soft tissue reconstruction) included 400 sponge tissue images from 50 healthy patients and 50 patients with osteoporosis. Texture feature descriptors were calculated based on the statistical analysis of the grey image histogram, autoregression model, and wavelet transform. The data dimensional reduction was applied by feature selection using nine methods representing various approaches (filter, wrapper, and embedded methods). Eleven methods were used to build the classifier models. In the learning process, hyperparametric optimization based on the grid search method was applied. On this basis, the most effective model and the optimal subset of features for each selection method used were determined. In the case of bone reconstruction images, four models achieved a maximum accuracy of 92%, one of which had the highest sensitivity of 95%, with a specificity of 89%. For soft tissue reconstruction images, five models achieved the highest testing accuracy of 95%, whereas the other quality indices (TPR and TNR) were also equal to 95%. The research showed that the images derived from soft tissue reconstruction allow for obtaining more accurate values of texture parameters, which increases the accuracy of the classification and offers better possibilities for diagnosing osteoporosis.
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Gao L, Jiao T, Feng Q, Wang W. Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis. Osteoporos Int 2021; 32:1279-1286. [PMID: 33640997 DOI: 10.1007/s00198-021-05887-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 02/09/2021] [Indexed: 01/14/2023]
Abstract
Artificial intelligence (AI) is a potentially reliable assistant in the diagnosis of osteoporosis. This meta-analysis aims to assess the diagnostic accuracy of the AI-based systems using medical images. We searched PubMed and Web of Science from inception to June 15, 2020, for eligible articles that applied AI approaches to diagnosing osteoporosis using medical images. Quality and bias of the included studies were evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The main outcome was the sensitivity and specificity of the performance of the AI-based systems. The data analysis utilized the R Foundation packages of "meta" for univariate analysis and Stata for bivariate analysis. Random effects model was utilized. Seven studies with 3186 patients were included in the meta-analysis. The overall risk of bias of the included studies was assessed as low. The pooled sensitivity was 0.96 (95% CI 0.93-1.00), and the pooled specificity was 0.95 (95% CI 0.91-0.99). However, high heterogeneity was found in this meta-analysis. The results supported that the AI-based systems had good accuracy in diagnosing osteoporosis. However, the high risk of bias in patient selection and high heterogeneity in the meta-analysis made the conclusion less convincing. The application of AI-based systems in osteoporosis diagnosis needs to be further confirmed by more prospective studies in multi-centers including more random samples from complete patient types.
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Affiliation(s)
- L Gao
- Beijing University of Chinese Medicine, Beijing, 100029, China.
- Applied Health Research Centre (AHRC), Li Ka Shing Knowledge Institute, St Michael's Hospital, University of Toronto, Toronto, M5B 1W8, Canada.
| | - T Jiao
- Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Q Feng
- Beijing University of Chinese Medicine, Beijing, 100029, China
| | - W Wang
- Beijing University of Chinese Medicine, Beijing, 100029, China.
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Cancellous bone structure assessment using a new trabecular connectivity. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Gilmour RJ, Brickley MB, Hoogland M, Jurriaans E, Mays S, Prowse TL. Quantifying cortical bone in fragmentary archeological second metacarpals. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2021; 174:812-821. [PMID: 33580992 DOI: 10.1002/ajpa.24248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 11/23/2020] [Accepted: 01/25/2021] [Indexed: 11/10/2022]
Abstract
OBJECTIVES Skeletal variation in cortical bone thickness is an indicator of bone quality and health in archeological populations. Second metacarpal radiogrammetry, which measures cortical thickness at the shaft midpoint, is traditionally used to evaluate bone loss in bioarcheological and some clinical contexts. However fragmentary elements are regularly omitted because the midpoint cannot be determined. This methodological limitation reduces sample sizes and biases them against individuals prone to fracture, such as older individuals with low bone mass. This study introduces a new technique for measuring cortical bone in second metacarpals, the "Region of Interest" (ROI) method, which quantifies bone in archeological remains with less-than-ideal preservation while accounting for cortical heterogeneity. MATERIALS AND METHODS The ROI method was adapted from digital X-ray radiogrammetry (DXR), a clinical method used to estimate bone mineral density, and tested using second metacarpals from Middenbeemster, Netherlands, a 19th century known age and sex skeletal collection. The ROI method quantifies cortical bone area within a 1.9 cm-long, mid-diaphyseal region, standardized for body size differences using total area (CAIROI ). CAIROI values were compared to traditional radiogrammetric cortical indices (CI) to assess the method's ability to identify age-related bone loss. RESULTS CAIROI values have high intra- and interobserver replicability and are strongly and significantly correlated with CI values for both males (r[n = 39] = 0.906, p = 0.000) and females (r[n = 58] = 0.925, p = 0.000). CONCLUSION The ROI method complements traditional radiogrammetry analyses and provides a reliable way to quantify cortical bone in incomplete second metacarpals, thereby maximizing sample sizes, allowing patterns in bone acquisition and loss to be more comprehensively depicted in archeological assemblages.
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Affiliation(s)
- Rebecca J Gilmour
- Department of Sociology and Anthropology, Mount Royal University, Calgary, Alberta, Canada.,Department of Anthropology, McMaster University, Hamilton, Ontario, Canada
| | - Megan B Brickley
- Department of Anthropology, McMaster University, Hamilton, Ontario, Canada
| | - Menno Hoogland
- Faculty of Archaeology, Leiden University, Leiden, The Netherlands
| | - Erik Jurriaans
- Department of Radiology, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, Hamilton, Ontario, Canada.,Department of Radiology, McMaster University, Hamilton, Ontario, Canada
| | - Simon Mays
- Research Department, Historic England, Fort Cumberland, Portsmouth, UK.,Department of Archaeology, University of Southampton, Avenue Campus, Southampton, UK.,Faculty of History, Classics and Archaeology, University of Edinburgh, Edinburgh, UK
| | - Tracy L Prowse
- Department of Anthropology, McMaster University, Hamilton, Ontario, Canada
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Yamamoto N, Sukegawa S, Kitamura A, Goto R, Noda T, Nakano K, Takabatake K, Kawai H, Nagatsuka H, Kawasaki K, Furuki Y, Ozaki T. Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates. Biomolecules 2020; 10:biom10111534. [PMID: 33182778 PMCID: PMC7697189 DOI: 10.3390/biom10111534] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/08/2020] [Accepted: 11/08/2020] [Indexed: 01/10/2023] Open
Abstract
This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.
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Affiliation(s)
- Norio Yamamoto
- Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital, Takamatsu, Kagawa 760-8557, Japan; (N.Y.); (K.K.)
| | - Shintaro Sukegawa
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, Japan;
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
- Correspondence: ; Tel.: +81-87-811-3333; Fax: +81-87-835-8363
| | - Akira Kitamura
- Search Space Inc., Tokyo 151-0072, Japan; (A.K.); (R.G.)
| | - Ryosuke Goto
- Search Space Inc., Tokyo 151-0072, Japan; (A.K.); (R.G.)
| | - Tomoyuki Noda
- Department of Musculoskeletal Traumatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan;
| | - Keisuke Nakano
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
| | - Kiyofumi Takabatake
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
| | - Hotaka Kawai
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
| | - Hitoshi Nagatsuka
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
| | - Keisuke Kawasaki
- Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital, Takamatsu, Kagawa 760-8557, Japan; (N.Y.); (K.K.)
| | - Yoshihiko Furuki
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, Japan;
| | - Toshifumi Ozaki
- Department of Orthopaedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan;
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Wani IM, Arora S. Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey. Med Biol Eng Comput 2020; 58:1873-1917. [PMID: 32583141 DOI: 10.1007/s11517-020-02171-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 03/26/2020] [Indexed: 12/18/2022]
Abstract
Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.
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Affiliation(s)
- Insha Majeed Wani
- School of Computer Science and Engineering, SMVDU, Katra, J&K, India
| | - Sakshi Arora
- School of Computer Science and Engineering, SMVDU, Katra, J&K, India.
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E Elahi GMM, Kalra S, Zinman L, Genge A, Korngut L, Yang YH. Texture classification of MR images of the brain in ALS using M-CoHOG: A multi-center study. Comput Med Imaging Graph 2019; 79:101659. [PMID: 31786374 DOI: 10.1016/j.compmedimag.2019.101659] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 08/16/2019] [Accepted: 09/24/2019] [Indexed: 01/07/2023]
Abstract
Gradient-based texture analysis methods have become popular in computer vision and image processing and has many applications including medical image analysis. This motivates us to develop a texture feature extraction method to discriminate Amyotrophic Lateral Sclerosis (ALS) patients from controls. But, the lack of data in ALS research is a major constraint and can be mitigated by using data from multiple centers. However, multi-center data gives some other challenges such as differing scanner parameters and variation in intensity of the medical images, which motivate the development of the proposed method. To investigate these challenges, we propose a gradient-based texture feature extraction method called Modified Co-occurrence Histograms of Oriented Gradients (M-CoHOG) to extract texture features from 2D Magnetic Resonance Images (MRI). We also propose a new feature-normalization technique before feeding the normalized M-CoHOG features into an ensemble of classifiers, which can accommodate for variation of data from different centers. ALS datasets from four different centers are used in the experiments. We analyze the classification accuracy of single center data as well as that arising from multiple centers. It is observed that the extracted texture features from downsampled images are more significant in distinguishing between patients and controls. Moreover, using an ensemble of classifiers shows improvement in classification accuracy over a single classifier in multi-center data. The proposed method outperforms the state-of-the-art methods by a significant margin.
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Affiliation(s)
- G M Mashrur E Elahi
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Sanjay Kalra
- Departments of Medicine (Neurology) and Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Lorne Zinman
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Angela Genge
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Lawrence Korngut
- Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Yee-Hong Yang
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
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