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Agaronnik ND, Giberson-Chen C, Bono CM. Using advanced imaging to measure bone density, compression fracture risk, and risk for construct failure after spine surgery. Spine J 2024; 24:1135-1152. [PMID: 38437918 DOI: 10.1016/j.spinee.2024.02.018] [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: 07/04/2023] [Revised: 01/22/2024] [Accepted: 02/23/2024] [Indexed: 03/06/2024]
Abstract
Low bone mineral density (BMD) can predispose to vertebral body compression fractures and postoperative instrumentation failure. DEXA is considered the gold standard for measurement of BMD, however it is not obtained for all spine surgery patients preoperatively. There is a growing body of evidence suggesting that more routinely acquired spine imaging studies such as computed tomography (CT) and magnetic resonance imaging (MRI) can be opportunistically used to measure BMD. Here we review available studies that assess the validity of opportunistic screening with CT-derived Hounsfield Units (HU) and MRI-derived vertebral vone quality (VBQ) to measure BMD of the spine as well the utility of these measures in predicting postoperative outcomes. Additionally, we provide screening thresholds based on HU and VBQ for prediction of osteopenia/ osteoporosis and postoperative outcomes such as cage subsidence, screw loosening, proximal junctional kyphosis, and implant failure.
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Affiliation(s)
| | - Carew Giberson-Chen
- Harvard Combined Orthopaedic Residency Program, 55 Fruit Street, Yawkey Building, Suite 3A, Boston, MA 02114
| | - Christopher M Bono
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115; Harvard Combined Orthopaedic Residency Program, 55 Fruit Street, Yawkey Building, Suite 3A, Boston, MA 02114; Department of Orthopaedic Surgery, Massachusetts General Hospital, 55 Fruit Street, Yawkey Building, Suite 3A, Boston, MA 02114.
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2
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Praveen AD, Sollmann N, Baum T, Ferguson SJ, Benedikt H. CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis. Osteoporos Int 2024; 35:971-996. [PMID: 38353706 PMCID: PMC11136833 DOI: 10.1007/s00198-024-07029-0] [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: 09/17/2023] [Accepted: 01/19/2024] [Indexed: 05/30/2024]
Abstract
The use of opportunistic computed tomography (CT) image-based biomarkers may be a low-cost strategy for screening older individuals at high risk for osteoporotic fractures and populations that are not sufficiently targeted. This review aimed to assess the discriminative ability of image-based biomarkers derived from existing clinical routine CT scans for hip, vertebral, and major osteoporotic fracture prediction. A systematic search in PubMed MEDLINE, Embase, Cochrane, and Web of Science was conducted from the earliest indexing date until July 2023. The evaluation of study quality was carried out using a modified Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2) checklist. The primary outcome of interest was the area under the curve (AUC) and its corresponding 95% confidence intervals (CIs) obtained for four main categories of biomarkers: areal bone mineral density (BMD), image attenuation, volumetric BMD, and finite element (FE)-derived biomarkers. The meta-analyses were performed using random effects models. Sixty-one studies were included in this review, among which 35 were synthesized in a meta-analysis and the remaining articles were qualitatively synthesized. In comparison to the pooled AUC of areal BMD (0.73 [95% CI 0.71-0.75]), the pooled AUC values for predicting osteoporotic fractures for FE-derived parameters (0.77 [95% CI 0.72-0.81]; p < 0.01) and volumetric BMD (0.76 [95% CI 0.71-0.81]; p < 0.01) were significantly higher, but there was no significant difference with the pooled AUC for image attenuation (0.73 [95% CI 0.66-0.79]; p = 0.93). Compared to areal BMD, volumetric BMD and FE-derived parameters may provide a significant improvement in the discrimination of osteoporotic fractures using opportunistic CT assessments.
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Affiliation(s)
- Anitha D Praveen
- Early Detection of Health Risks and Prevention, Future Health Technologies, Singapore-ETH Centre (SEC), Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore.
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen J Ferguson
- Early Detection of Health Risks and Prevention, Future Health Technologies, Singapore-ETH Centre (SEC), Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore
- Institute for Biomechanics, ETH-Zurich, Zurich, Switzerland
| | - Helgason Benedikt
- Early Detection of Health Risks and Prevention, Future Health Technologies, Singapore-ETH Centre (SEC), Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore
- Institute for Biomechanics, ETH-Zurich, Zurich, Switzerland
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3
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Liu D, Garrett JW, Perez AA, Zea R, Binkley NC, Summers RM, Pickhardt PJ. Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures. Br J Radiol 2024; 97:770-778. [PMID: 38379423 PMCID: PMC11027263 DOI: 10.1093/bjr/tqae041] [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: 04/05/2023] [Revised: 09/27/2023] [Accepted: 02/19/2024] [Indexed: 02/22/2024] Open
Abstract
OBJECTIVE Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls. METHODS In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived. RESULTS Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686. CONCLUSION Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging. ADVANCES IN KNOWLEDGE CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.
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Affiliation(s)
- Daniel Liu
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - John W Garrett
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Alberto A Perez
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Ryan Zea
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Neil C Binkley
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Potomac, MD, 20892, United States
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, WI, 53792, United States
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Ebstein E, Brocard P, Soussi G, Khoury R, Forien M, Khalil A, Vauchier C, Juge PA, Léger B, Ottaviani S, Dieudé P, Zalcman G, Gounant V. Burden of comorbidities: Osteoporotic vertebral fracture during non-small cell lung cancer - the BONE study. Eur J Cancer 2024; 200:113604. [PMID: 38340385 DOI: 10.1016/j.ejca.2024.113604] [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: 10/09/2023] [Revised: 11/29/2023] [Accepted: 02/01/2024] [Indexed: 02/12/2024]
Abstract
INTRODUCTION Immunotherapy and targeted therapy have extended life expectancy in non-small cell lung cancer (NSCLC) patients, shifting it into a chronic condition with comorbidities, including osteoporosis. This study aims to evaluate the prevalence and incidence of osteoporotic vertebral fracture (OPVF) during NSCLC follow-up, identify risk factors of OPVF, and determine the impact on overall survival (OS). METHODS We performed a longitudinal single-center retrospective cohort study involving patients with histologically proven NSCLC of any stage. Chest-abdomen-pelvis computed tomography (CAP CT) at diagnosis and during follow-up were double-blind reviewed to determine OPVF site, count, type, time to incident OPVF, and trabecular volumetric bone density (TVBD). An institutional expert committee adjudicated discrepancies. Binary logistic regression was used to predict the occurrence of incident OPVF. OS was calculated using the Kaplan-Meier method. RESULTS We included 289 patients with a median follow-up of 29.7 months. OPVF prevalence was 10.7% at inclusion and 23.2% at the end of follow-up. Cumulative incidence was 12.5%, with an incidence rate of 4 per 100 patient-years. Median time to incident OPVF was 13 months (IQR: 6.7-21.2). Seven of the 36 patients with incident OPVF received denosumab or bisphosphonates. In multivariable analysis, independent risk factors for incident OPVF were BMI < 19 kg/m2 (OR: 5.62, 95%CI 1.84-17.20, p = 0.002), lower TVBD (OR: 0.982 per HU, 95%CI 0.97-0.99, p = 0.001) and corticosteroid use (OR: 4.77, 95%CI: 1.76-12.89, p = 0.001). OPVF was not significantly associated with OS. CONCLUSIONS Osteoporosis should be screened for in NSCLC patients. Thoracic oncologists must broaden the use of steroid-induced osteoporosis recommendations.
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Affiliation(s)
- E Ebstein
- Université Paris Cité, Rheumatology Department, Hôpital Bichat Claude-Bernard, Paris, France
| | - P Brocard
- Université Paris Cité, Rheumatology Department, Hôpital Bichat Claude-Bernard, Paris, France
| | - G Soussi
- Pulmonology Department, Hôpital Forcilles - Fondation Cognacq-Jay, 77150 Férolles-Attily, France
| | - R Khoury
- Université Paris Cité, Radiology Department, Hôpital Bichat Claude-Bernard, Paris, France
| | - M Forien
- Université Paris Cité, Rheumatology Department, Hôpital Bichat Claude-Bernard, Paris, France
| | - A Khalil
- Université Paris Cité, Radiology Department, Hôpital Bichat Claude-Bernard, Paris, France
| | - C Vauchier
- Université Paris Cité, Thoracic Oncology Department, CIC INSERM 1425, Institut du Cancer AP-HP.Nord, Hôpital Bichat Claude-Bernard, Paris, France
| | - P A Juge
- Université Paris Cité, Rheumatology Department, Hôpital Bichat Claude-Bernard, Paris, France
| | - B Léger
- Université Paris Cité, Rheumatology Department, Hôpital Bichat Claude-Bernard, Paris, France
| | - S Ottaviani
- Université Paris Cité, Rheumatology Department, Hôpital Bichat Claude-Bernard, Paris, France
| | - P Dieudé
- Université Paris Cité, Rheumatology Department, Hôpital Bichat Claude-Bernard, Paris, France
| | - G Zalcman
- Université Paris Cité, Thoracic Oncology Department, CIC INSERM 1425, Institut du Cancer AP-HP.Nord, Hôpital Bichat Claude-Bernard, Paris, France
| | - V Gounant
- Université Paris Cité, Thoracic Oncology Department, CIC INSERM 1425, Institut du Cancer AP-HP.Nord, Hôpital Bichat Claude-Bernard, Paris, France.
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Maki S, Furuya T, Inoue M, Shiga Y, Inage K, Eguchi Y, Orita S, Ohtori S. Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis. J Clin Med 2024; 13:705. [PMID: 38337399 PMCID: PMC10856760 DOI: 10.3390/jcm13030705] [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: 11/13/2023] [Revised: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024] Open
Abstract
Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.
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Affiliation(s)
- Satoshi Maki
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Takeo Furuya
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Masahiro Inoue
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yasuhiro Shiga
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Kazuhide Inage
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yawara Eguchi
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Sumihisa Orita
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Seiji Ohtori
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
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6
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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: 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: 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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7
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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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Ong W, Liu RW, Makmur A, Low XZ, Sng WJ, Tan JH, Kumar N, Hallinan JTPD. Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography. Bioengineering (Basel) 2023; 10:1364. [PMID: 38135954 PMCID: PMC10741220 DOI: 10.3390/bioengineering10121364] [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: 10/20/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Ren Wei Liu
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Weizhong Jonathan Sng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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9
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Paccou J, Philippoteaux C, Cortet B, Fardellone P. Effectiveness of fracture liaison services in osteoporosis. Joint Bone Spine 2023; 90:105574. [PMID: 37080285 DOI: 10.1016/j.jbspin.2023.105574] [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: 02/13/2023] [Revised: 03/21/2023] [Accepted: 04/03/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND In response to the gradual decline in the number of prescriptions for anti-osteoporosis medication (AOM) following fragility fractures, fracture liaison services (FLSs) have been set up around the world with the aim of filling this treatment gap. Several studies have already reported the benefits of such organizations, particularly in reducing fracture risk, mortality rates and healthcare costs, and literature on FLSs has increased at a steady pace over time. METHODS A narrative review was conducted on the latest available findings on the effectiveness of FLSs. Various approaches to implementing an effective FLS program are discussed. RESULTS FLS programs have enhanced the management of osteoporosis-related fractures. However, several studies have highlighted that not all FLSs are necessarily effective in reducing subsequent fracture risk and mortality. Long-term AOM persistence and monitoring are another critical issue in FLS programs. A few studies have reported that FLSs are associated with an improvement in AOM persistence, regardless of the type of AOM. Practitioners in the FLS setting need to be aware of the impact of recency of fracture and fracture recurrence rates, and the need for timely interventions. The administration of zoledronic acid in an in-patient setting may improve AOM treatment rates in patients, who often encounter obstacles to outpatient follow-up. Introducing 'vertebral fracture identification services' in FLS programs is also an option. However, doing so leads to an increase in workload and this would need to be considered by any FLS that is considering introducing such a service. Evidence suggests that digital technologies can support (i) multidisciplinary teams in providing the best possible patient care based on current evidence, and (ii) patient self-management. However, as the methodological quality of many of the studies evaluating these technologies was poor, their validity of their results is limited. CONCLUSION Further research should focus on the optimal implementation of post-fracture care using automated systems, and standardized reporting of patient's characteristics and outcome measures using key performance indicators.
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Affiliation(s)
- Julien Paccou
- Department of Rheumatology, MABlab ULR 4490, Université de Lille, CHU de Lille, 59000 Lille, France.
| | | | - Bernard Cortet
- Department of Rheumatology, MABlab ULR 4490, Université de Lille, CHU de Lille, 59000 Lille, France
| | - Patrice Fardellone
- Department of Rheumatology, CHU d'Amiens, Unité EA MP3CV, Amiens, France
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10
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Guenoun D, Champsaur P. Opportunistic Computed Tomography Screening for Osteoporosis and Fracture. Semin Musculoskelet Radiol 2023; 27:451-456. [PMID: 37748468 DOI: 10.1055/s-0043-1771037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Osteoporosis is underdiagnosed and undertreated, leading to loss of treatment for the patient and high costs for the health care system. Routine thoracic and/or abdominal computed tomography (CT) performed for other indications can screen opportunistically for osteoporosis with no extra cost, time, or irradiation. Various methods can quantify fracture risk on opportunistic clinical CT: vertebral Hounsfield unit bone mineral density (BMD), usually of L1; BMD measurement with asynchronous or internal calibration; quantitative CT; bone texture assessment; and finite element analysis. Screening for osteoporosis and vertebral fractures on opportunistic CT is a promising approach, providing automated fracture risk scores by means of artificial intelligence, thus enabling earlier management.
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Affiliation(s)
- Daphne Guenoun
- APHM, Sainte-Marguerite Hospital, Institute for Locomotion, Department of Radiology, Marseille, France
- Aix-Marseille University, CNRS, Institut des Sciences du Mouvement, Marseille, France
| | - Pierre Champsaur
- APHM, Sainte-Marguerite Hospital, Institute for Locomotion, Department of Radiology, Marseille, France
- Aix-Marseille University, CNRS, Institut des Sciences du Mouvement, Marseille, France
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11
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Page JH, Moser FG, Maya MM, Prasad R, Pressman BD. Opportunistic CT Screening-Machine Learning Algorithm Identifies Majority of Vertebral Compression Fractures: A Cohort Study. JBMR Plus 2023; 7:e10778. [PMID: 37614306 PMCID: PMC10443072 DOI: 10.1002/jbm4.10778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 05/17/2023] [Indexed: 08/25/2023] Open
Abstract
Vertebral compression fractures (VCF) are common in patients older than 50 years but are often undiagnosed. Zebra Medical Imaging developed a VCF detection algorithm, with machine learning, to detect VCFs from CT images of the chest and/or abdomen/pelvis. In this study, we evaluated the diagnostic performance of the algorithm in identifying VCF. We conducted a blinded validation study to estimate the operating characteristics of the algorithm in identifying VCFs using previously completed CT scans from 1200 women and men aged 50 years and older at a tertiary-care center. Each scan was independently evaluated by two of three neuroradiologists to identify and grade VCF. Disagreements were resolved by a senior neuroradiologist. The algorithm evaluated the CT scans in a separate workstream. The VCF algorithm was not able to evaluate CT scans for 113 participants. Of the remaining 1087 study participants, 588 (54%) were women. Median age was 73 years (range 51-102 years; interquartile range 66-81). For the 1087 algorithm-evaluated participants, the sensitivity and specificity of the VCF algorithm in diagnosing any VCF were 0.66 (95% confidence interval [CI] 0.59-0.72) and 0.90 (95% CI 0.88-0.92), respectively, and for diagnosing moderate/severe VCF were 0.78 (95% CI 0.70-0.85) and 0.87 (95% CI 0.85-0.89), respectively. Implementing this VCF algorithm within radiology systems may help to identify patients at increased fracture risk and could support the diagnosis of osteoporosis and facilitate appropriate therapy. © 2023 Amgen, Inc. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
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Affiliation(s)
- John H Page
- Center for Observational Research, Amgen Inc.Thousand OaksCAUSA
| | - Franklin G Moser
- Department of ImagingCedars‐Sinai Medical CenterLos AngelesCAUSA
| | - Marcel M Maya
- Department of ImagingCedars‐Sinai Medical CenterLos AngelesCAUSA
| | - Ravi Prasad
- Department of ImagingCedars‐Sinai Medical CenterLos AngelesCAUSA
| | - Barry D Pressman
- Department of ImagingCedars‐Sinai Medical CenterLos AngelesCAUSA
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12
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Abstract
PURPOSE OF REVIEW Opportunistic screening is a combination of techniques to identify subjects of high risk for osteoporotic fracture using routine clinical CT scans prescribed for diagnoses unrelated to osteoporosis. The two main components are automated detection of vertebral fractures and measurement of bone mineral density (BMD) in CT scans, in which a phantom for calibration of CT to BMD values is not used. This review describes the particular challenges of opportunistic screening and provides an overview and comparison of current techniques used for opportunistic screening. The review further outlines the performance of opportunistic screening. RECENT FINDINGS A wide range of technologies for the automatic detection of vertebral fractures have been developed and successfully validated. Most of them are based on artificial intelligence algorithms. The automated differentiation of osteoporotic from traumatic fractures and vertebral deformities unrelated to osteoporosis, the grading of vertebral fracture severity, and the detection of mild vertebral fractures is still problematic. The accuracy of automated fracture detection compared to classical radiological semi-quantitative Genant scoring is about 80%. Accuracy errors of alternative BMD calibration methods compared to simultaneous phantom-based calibration used in standard quantitative CT (QCT) range from below 5% to about 10%. The impact of contrast agents, frequently administered in clinical CT on the determination of BMD and on fracture risk determination is still controversial. Opportunistic screening, the identification of vertebral fracture and the measurement of BMD using clinical routine CT scans, is feasible but corresponding techniques still need to be integrated into the clinical workflow and further validated with respect to the prediction of fracture risk.
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Affiliation(s)
- Klaus Engelke
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany.
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany.
| | - Oliver Chaudry
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany
| | - Stefan Bartenschlager
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany
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13
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Bousson V, Benoist N, Guetat P, Attané G, Salvat C, Perronne L. Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going? Joint Bone Spine 2023; 90:105493. [PMID: 36423783 DOI: 10.1016/j.jbspin.2022.105493] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 11/23/2022]
Abstract
The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a clinical question. Convolutional neural networks are the most common architecture for performing deep learning on medical images. The various musculoskeletal applications of deep learning are the detection of abnormalities on X-rays or cross-sectional images (CT, MRI), for example the detection of fractures, meniscal tears, anterior cruciate ligament tears, degenerative lesions of the spine, bone metastases, classification of e.g., dural sac stenosis, degeneration of intervertebral discs, assessment of skeletal age, and segmentation, for example of cartilage. Software developments are already impacting the daily practice of orthopedic imaging by automatically detecting fractures on radiographs. Improving image acquisition protocols, improving the quality of low-dose CT images, reducing acquisition times in MRI, or improving MR image resolution is possible through deep learning. Deep learning offers an automated way to offload time-consuming manual processes and improve practitioner performance. This article reviews the current state of AI in musculoskeletal imaging.
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Affiliation(s)
- Valérie Bousson
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France.
| | - Nicolas Benoist
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France
| | - Pierre Guetat
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France
| | - Grégoire Attané
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France
| | - Cécile Salvat
- Department of Medical Physics, hôpital Lariboisière, AP-HP Nord-université Paris Cité, Paris, France
| | - Laetitia Perronne
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France
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Pickhardt PJ, Nguyen T, Perez AA, Graffy PM, Jang S, Summers RM, Garrett JW. Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool. Radiol Artif Intell 2022; 4:e220042. [PMID: 36204542 PMCID: PMC9530763 DOI: 10.1148/ryai.220042] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 08/12/2022] [Accepted: 08/17/2022] [Indexed: 11/11/2022]
Abstract
Purpose To develop, test, and validate a deep learning (DL) tool that improves upon a previous feature-based CT image processing bone mineral density (BMD) algorithm and compare it against the manual reference standard. Materials and Methods This single-center, retrospective, Health Insurance Portability and Accountability Act-compliant study included manual L1 trabecular Hounsfield unit measurements from abdominal CT scans in 11 035 patients (mean age, 58 years ± 12 [SD]; 6311 women) as the reference standard. Automated level selection and L1 trabecular region of interest (ROI) placement were then performed in this CT cohort with both a previously validated feature-based image processing tool and a new DL tool. Overall technical success rates and agreement with the manual reference standard were assessed. Results The overall success rate of the DL tool in this heterogeneous patient cohort was significantly higher than that of the older image processing BMD algorithm (99.3% vs 89.4%, P < .001). Using this DL tool, the closest median Hounsfield unit values for single-, three-, and seven-slice vertebral ROIs were within 5% of the manual reference standard Hounsfield unit values in 35.1%, 56.9%, and 85.8% of scans; within 10% in 56.6%, 75.6%, and 92.9% of scans; and within 25% in 76.5%, 89.3%, and 97.1% of scans, respectively. Trade-offs in sensitivity and specificity for osteoporosis assessment were observed from the single-slice approach (sensitivity, 39.4%; specificity, 98.3%) to the minimum value of the multislice approach (for seven contiguous slices; sensitivity, 71.3% and specificity, 94.6%). Conclusion The new DL BMD tool demonstrated a higher success rate than the older feature-based image processing tool, and its outputs can be targeted for higher specificity or sensitivity for osteoporosis assessment.Keywords: CT, CT-Quantitative, Abdomen/GI, Skeletal-Axial, Spine, Deep Learning, Machine Learning Supplemental material is available for this article. © RSNA, 2022.
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Affiliation(s)
- Perry J. Pickhardt
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Thang Nguyen
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Alberto A. Perez
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Peter M. Graffy
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Samuel Jang
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - Ronald M. Summers
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
| | - John W. Garrett
- From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (P.J.P., T.N., A.A.P., P.M.G., S.J., J.W.G.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.)
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15
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Xiao BH, Zhu MSY, Du EZ, Liu WH, Ma JB, Huang H, Gong JS, Diacinti D, Zhang K, Gao B, Liu H, Jiang RF, Ji ZY, Xiong XB, He LC, Wu L, Xu CJ, Du MM, Wang XR, Chen LM, Wu KY, Yang L, Xu MS, Diacinti D, Dou Q, Kwok TYC, Wáng YXJ. A software program for automated compressive vertebral fracture detection on elderly women's lateral chest radiograph: Ofeye 1.0. Quant Imaging Med Surg 2022; 12:4259-4271. [PMID: 35919046 PMCID: PMC9338385 DOI: 10.21037/qims-22-433] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/25/2022] [Indexed: 11/17/2022]
Abstract
Background Because osteoporotic vertebral fracture (OVF) on chest radiographs is commonly missed in radiological reports, we aimed to develop a software program which offers automated detection of compressive vertebral fracture (CVF) on lateral chest radiographs, and which emphasizes CVF detection specificity with a low false positivity rate. Methods For model training, we retrieved 3,991 spine radiograph cases and 1,979 chest radiograph cases from 16 sources, with among them in total 1,404 cases had OVF. For model testing, we retrieved 542 chest radiograph cases and 162 spine radiograph cases from four independent clinics, with among them 215 cases had OVF. All cases were female subjects, and except for 31 training data cases which were spine trauma cases, all the remaining cases were post-menopausal women. Image data included DICOM (Digital Imaging and Communications in Medicine) format, hard film scanned PNG (Portable Network Graphics) format, DICOM exported PNG format, and PACS (Picture Archiving and Communication System) downloaded resolution reduced DICOM format. OVF classification included: minimal and mild grades with <20% or ≥20–25% vertebral height loss respectively, moderate grade with ≥25–40% vertebral height loss, severe grade with ≥40%–2/3 vertebral height loss, and collapsed grade with ≥2/3 vertebral height loss. The CVF detection base model was mainly composed of convolution layers that include convolution kernels of different sizes, pooling layers, up-sampling layers, feature merging layers, and residual modules. When the model loss function could not be further decreased with additional training, the model was considered to be optimal and termed ‘base-model 1.0’. A user-friendly interface was also developed, with the synthesized software termed ‘Ofeye 1.0’. Results Counting cases and with minimal and mild OVFs included, base-model 1.0 demonstrated a specificity of 97.1%, a sensitivity of 86%, and an accuracy of 93.9% for the 704 testing cases. In total, 33 OVFs in 30 cases had a false negative reading, which constituted a false negative rate of 14.0% (30/215) by counting all OVF cases. Eighteen OVFs in 15 cases had OVFs of ≥ moderate grades missed, which constituted a false negative rate of 7.0% (15/215, i.e., sensitivity 93%) if only counting cases with ≥ moderate grade OVFs missed. False positive reading was recorded in 13 vertebrae in 13 cases (one vertebra in each case), which constituted a false positivity rate of 2.7% (13/489). These vertebrae with false positivity labeling could be readily differentiated from a true OVF by a human reader. The software Ofeye 1.0 allows ‘batch processing’, for example, 100 radiographs can be processed in a single operation. This software can be integrated into hospital PACS, or installed in a standalone personal computer. Conclusions A user-friendly software program was developed for CVF detection on elderly women’s lateral chest radiographs. It has an overall low false positivity rate, and for moderate and severe CVFs an acceptably low false negativity rate. The integration of this software into radiological practice is expected to improve osteoporosis management for elderly women.
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Affiliation(s)
- Ben-Heng Xiao
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | | | - Er-Zhu Du
- Department of Radiology, Dongguan Traditional Chinese Medicine Hospital, Dongguan, China
| | - Wei-Hong Liu
- Department of Radiology, General Hospital of China Resources & Wuhan Iron and Steel Corporation, Wuhan, China
| | - Jian-Bing Ma
- Department of Radiology, the First Hospital of Jiaxing, The Affiliated Hospital of Jiaxing University, Jiaxing, China
| | - Hua Huang
- Department of Radiology, The Third People's Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, National Clinical Research Center for Infectious Diseases, Shenzhen, China
| | - Jing-Shan Gong
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China
| | - Davide Diacinti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Sapienza University of Rome, Rome, Italy.,Department of Diagnostic and Molecular Imaging, Radiology and Radiotherapy, University Foundation Hospital Tor Vergata, Rome, Italy
| | - Kun Zhang
- Department of Radiology, First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
| | - Bo Gao
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Heng Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Ri-Feng Jiang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhong-You Ji
- PET-CT Center, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xiao-Bao Xiong
- Department of Radiology, Zhejiang Provincial Tongde Hospital, Hangzhou, China
| | - Lai-Chang He
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lei Wu
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Chuan-Jun Xu
- Department of Radiology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Mei-Mei Du
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Xiao-Rong Wang
- Department of Radiology, Ningbo First Hospital, Ningbo, China
| | - Li-Mei Chen
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Kong-Yang Wu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.,College of Electrical and Information Engineering, Jinan University, Guangzhou, China
| | - Liu Yang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Mao-Sheng Xu
- Department of Radiology, the First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Daniele Diacinti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Sapienza University of Rome, Rome, Italy
| | - Qi Dou
- Department of Computer Science and Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Timothy Y C Kwok
- JC Centre for Osteoporosis Care and Control, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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