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Lattanzi R. Methods for the Clinical Translation of Quantitative MRI for the Evaluation of Patients With Femoroacetabular Impingement. HSS J 2023; 19:442-446. [PMID: 37937089 PMCID: PMC10626928 DOI: 10.1177/15563316231193404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 07/17/2023] [Indexed: 11/09/2023]
Affiliation(s)
- Riccardo Lattanzi
- Center for Advanced Imaging Innovation and Research (CAIR) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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Guest JD, Kelly-Hedrick M, Williamson T, Park C, Ali DM, Sivaganesan A, Neal CJ, Tator CH, Fehlings MG. Development of a Systems Medicine Approach to Spinal Cord Injury. J Neurotrauma 2023; 40:1849-1877. [PMID: 37335060 PMCID: PMC10460697 DOI: 10.1089/neu.2023.0024] [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] [Indexed: 06/21/2023] Open
Abstract
Traumatic spinal cord injury (SCI) causes a sudden onset multi-system disease, permanently altering homeostasis with multiple complications. Consequences include aberrant neuronal circuits, multiple organ system dysfunctions, and chronic phenotypes such as neuropathic pain and metabolic syndrome. Reductionist approaches are used to classify SCI patients based on residual neurological function. Still, recovery varies due to interacting variables, including individual biology, comorbidities, complications, therapeutic side effects, and socioeconomic influences for which data integration methods are lacking. Infections, pressure sores, and heterotopic ossification are known recovery modifiers. However, the molecular pathobiology of the disease-modifying factors altering the neurological recovery-chronic syndrome trajectory is mainly unknown, with significant data gaps between intensive early treatment and chronic phases. Changes in organ function such as gut dysbiosis, adrenal dysregulation, fatty liver, muscle loss, and autonomic dysregulation disrupt homeostasis, generating progression-driving allostatic load. Interactions between interdependent systems produce emergent effects, such as resilience, that preclude single mechanism interpretations. Due to many interacting variables in individuals, substantiating the effects of treatments to improve neurological outcomes is difficult. Acute injury outcome predictors, including blood and cerebrospinal fluid biomarkers, neuroimaging signal changes, and autonomic system abnormalities, often do not predict chronic SCI syndrome phenotypes. In systems medicine, network analysis of bioinformatics data is used to derive molecular control modules. To better understand the evolution from acute SCI to chronic SCI multi-system states, we propose a topological phenotype framework integrating bioinformatics, physiological data, and allostatic load tested against accepted established recovery metrics. This form of correlational phenotyping may reveal critical nodal points for intervention to improve recovery trajectories. This study examines the limitations of current classifications of SCI and how these can evolve through systems medicine.
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Affiliation(s)
- James D. Guest
- Neurological Surgery and the Miami Project to Cure Paralysis, University of Miami, Miami, Florida, USA
| | | | - Theresa Williamson
- Massachusetts General Neurosurgery, Harvard University, Boston, Massachusetts, USA
| | - Christine Park
- Department of Neurosurgery, Duke University, Durham, North Carolina, USA
| | - Daniyal Mansoor Ali
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Ahilan Sivaganesan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Chris J. Neal
- Division of Neurosurgery, Walter Reed National Military Medical Center, Bethesda, Maryland, USA
| | - Charles H. Tator
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Michael G. Fehlings
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski LH, Mikkilineni R, Rushmore RJ, Wagner T. The evolution of Big Data in neuroscience and neurology. JOURNAL OF BIG DATA 2023; 10:116. [PMID: 37441339 PMCID: PMC10333390 DOI: 10.1186/s40537-023-00751-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/08/2023] [Indexed: 07/15/2023]
Abstract
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00751-2.
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Affiliation(s)
| | - Paola Gonzalez-Mego
- Spaulding Rehabilitation/Neuromodulation Lab, Harvard Medical School, Cambridge, MA USA
| | | | | | | | | | - Timothy Wagner
- Highland Instruments, Cambridge, MA USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA USA
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Ma LY, Feng T, He C, Li M, Ren K, Tu J. A progression analysis of motor features in Parkinson's disease based on the mapper algorithm. Front Aging Neurosci 2023; 15:1047017. [PMID: 36896420 PMCID: PMC9989279 DOI: 10.3389/fnagi.2023.1047017] [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: 09/17/2022] [Accepted: 02/02/2023] [Indexed: 02/25/2023] Open
Abstract
Background Parkinson's disease (PD) is a neurodegenerative disease with a broad spectrum of motor and non-motor symptoms. The great heterogeneity of clinical symptoms, biomarkers, and neuroimaging and lack of reliable progression markers present a significant challenge in predicting disease progression and prognoses. Methods We propose a new approach to disease progression analysis based on the mapper algorithm, a tool from topological data analysis. In this paper, we apply this method to the data from the Parkinson's Progression Markers Initiative (PPMI). We then construct a Markov chain on the mapper output graphs. Results The resulting progression model yields a quantitative comparison of patients' disease progression under different usage of medications. We also obtain an algorithm to predict patients' UPDRS III scores. Conclusions By using mapper algorithm and routinely gathered clinical assessments, we developed a new dynamic models to predict the following year's motor progression in the early stage of PD. The use of this model can predict motor evaluations at the individual level, assisting clinicians to adjust intervention strategy for each patient and identifying at-risk patients for future disease-modifying therapy clinical trials.
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Affiliation(s)
- Ling-Yan Ma
- Department of Neurology, Center for Movement Disorders, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurology, China National Clinical Research Center for Neurological Disease, Beijing, China
| | - Tao Feng
- Department of Neurology, Center for Movement Disorders, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurology, China National Clinical Research Center for Neurological Disease, Beijing, China.,Parkinson's Disease Center, Beijing Institute for Brain Disorders, Beijing, China
| | - Chengzhang He
- Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China
| | - Mujing Li
- Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China
| | - Kang Ren
- GYENNO Science Co., LTD., Shenzhen, China.,Department of Neurology, HUST-GYENNO Central Neural System Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Junwu Tu
- Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China
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Skaf Y, Laubenbacher R. Topological data analysis in biomedicine: A review. J Biomed Inform 2022; 130:104082. [PMID: 35508272 DOI: 10.1016/j.jbi.2022.104082] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/20/2022] [Accepted: 04/23/2022] [Indexed: 01/22/2023]
Abstract
Significant technological advances made in recent years have shepherded a dramatic increase in utilization of digital technologies for biomedicine- everything from the widespread use of electronic health records to improved medical imaging capabilities and the rising ubiquity of genomic sequencing contribute to a "digitization" of biomedical research and clinical care. With this shift toward computerized tools comes a dramatic increase in the amount of available data, and current tools for data analysis capable of extracting meaningful knowledge from this wealth of information have yet to catch up. This article seeks to provide an overview of emerging mathematical methods with the potential to improve the abilities of clinicians and researchers to analyze biomedical data, but may be hindered from doing so by a lack of conceptual accessibility and awareness in the life sciences research community. In particular, we focus on topological data analysis (TDA), a set of methods grounded in the mathematical field of algebraic topology that seeks to describe and harness features related to the "shape" of data. We aim to make such techniques more approachable to non-mathematicians by providing a conceptual discussion of their theoretical foundations followed by a survey of their published applications to scientific research. Finally, we discuss the limitations of these methods and suggest potential avenues for future work integrating mathematical tools into clinical care and biomedical informatics.
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Affiliation(s)
- Yara Skaf
- University of Florida, Department of Mathematics, Gainesville, FL, USA; University of Florida, Department of Medicine, Division of Pulmonary, Critical Care, & Sleep Medicine, Gainesville, FL, USA.
| | - Reinhard Laubenbacher
- University of Florida, Department of Mathematics, Gainesville, FL, USA; University of Florida, Department of Medicine, Division of Pulmonary, Critical Care, & Sleep Medicine, Gainesville, FL, USA.
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Binvignat M, Pedoia V, Butte AJ, Louati K, Klatzmann D, Berenbaum F, Mariotti-Ferrandiz E, Sellam J. Use of machine learning in osteoarthritis research: a systematic literature review. RMD Open 2022; 8:rmdopen-2021-001998. [PMID: 35296530 PMCID: PMC8928401 DOI: 10.1136/rmdopen-2021-001998] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 02/16/2022] [Indexed: 11/21/2022] Open
Abstract
Objective The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA). Methods A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected. Results From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles. Conclusion This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.
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Affiliation(s)
- Marie Binvignat
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France.,Bakar Computational Health Science Institute, University of California, San Francisco, California, USA.,Immunology Immunopathology Immunotherapy UMRS_959, Sorbonne Universite, Paris, France
| | - Valentina Pedoia
- Center for Intelligent Imaging (CI2), Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Atul J Butte
- Bakar Computational Health Science Institute, University of California, San Francisco, California, USA
| | - Karine Louati
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
| | - David Klatzmann
- Immunology Immunopathology Immunotherapy UMRS_959, Sorbonne Universite, Paris, France.,Biotherapy (CIC-BTi) and Inflammation Immunopathology-Biotherapy Department (i2B), Hôpital Pitié-Salpêtrière, AP-HP, Paris, France
| | - Francis Berenbaum
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
| | | | - Jérémie Sellam
- Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France
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Tibrewala R, Bahroos E, Mehrabian H, Foreman SC, Link TM, Pedoia V, Majumdar S. [ 18 F]-Sodium Fluoride PET/MR Imaging for Bone-Cartilage Interactions in Hip Osteoarthritis: A Feasibility Study. J Orthop Res 2019; 37:2671-2680. [PMID: 31424110 PMCID: PMC6899769 DOI: 10.1002/jor.24443] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 08/02/2019] [Indexed: 02/04/2023]
Abstract
This study characterized the distribution of [18 F]-sodium fluoride (NaF) uptake and blood flow in the femur and acetabulum in hip osteoarthritis (OA) patients to find associations between bone remodeling and cartilage composition in the presence of morphological abnormalities using simultaneous positron emission tomography and magnetic resonance imaging (PET/MR), quantitative magnetic resonance imaging (MRI) and femur shape modeling. Ten patients underwent a [18 F]-NaF PET/MR dynamic scan of the hip simultaneously with: (i) fast spin-echo CUBE for morphology grading and (ii) T1ρ /T2 magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots for cartilage, bone segmentation, bone shape modeling, and T1ρ /T2 quantification. The standardized uptake values (SUVs) and Patlak kinetic parameter (Kpat ) were calculated for each patient as PET outcomes, using an automated post-processing pipeline. Shape modeling was performed to extract the variations in bone shapes in the patients. Pearson's correlation coefficients were used to study the associations between bone shapes, PET outcomes, and patient reported pain. Direct associations between quantitative MR and PET evidence of bone remodeling were established in the acetabulum and femur. Associations of shaft thickness with SUV in the femur (p = 0.07) and Kpat in the acetabulum (p = 0.02), cam deformity with acetabular score (p = 0.09), osteophytic growth on the femur head with Kpat (p = 0.01) were observed. Pain had increased correlations with SUV in the acetabulum (p = 0.14) and femur (p = 0.09) when shaft thickness was accounted for. This study demonstrated the ability of [18 F]-NaF PET-MRI, 3D shape modeling, and quantitative MRI to investigate cartilage-bone interactions and bone shape features in hip OA, providing potential investigative tools to diagnose OA. © 2019 The Authors. Journal of Orthopaedic Research® published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society J Orthop Res 37:2671-2680, 2019.
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Affiliation(s)
- Radhika Tibrewala
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCalifornia
| | - Emma Bahroos
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCalifornia
| | - Hatef Mehrabian
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCalifornia
| | - Sarah C. Foreman
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCalifornia
| | - Thomas M. Link
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCalifornia
| | - Valentina Pedoia
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCalifornia
| | - Sharmila Majumdar
- Department of Radiology and Biomedical ImagingUniversity of California, San FranciscoSan FranciscoCalifornia
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Geniesse C, Sporns O, Petri G, Saggar M. Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis. Netw Neurosci 2019; 3:763-778. [PMID: 31410378 PMCID: PMC6663215 DOI: 10.1162/netn_a_00093] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 04/25/2019] [Indexed: 01/07/2023] Open
Abstract
In this article, we present an open source neuroinformatics platform for exploring, analyzing, and validating distilled graphical representations of high-dimensional neuroimaging data extracted using topological data analysis (TDA). TDA techniques like Mapper have been recently applied to examine the brain's dynamical organization during ongoing cognition without averaging data in space, in time, or across participants at the outset. Such TDA-based approaches mark an important deviation from standard neuroimaging analyses by distilling complex high-dimensional neuroimaging data into simple-yet neurophysiologically valid and behaviorally relevant-representations that can be interactively explored at the single-participant level. To facilitate wider use of such techniques within neuroimaging and general neuroscience communities, our work provides several tools for visualizing, interacting with, and grounding TDA-generated graphical representations in neurophysiology. Through Python-based Jupyter notebooks and open datasets, we provide a platform to assess and visualize different intermittent stages of Mapper and examine the influence of Mapper parameters on the generated representations. We hope this platform could enable researchers and clinicians alike to explore topological representations of neuroimaging data and generate biological insights underlying complex mental disorders.
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Affiliation(s)
- Caleb Geniesse
- Biophysics Program, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Giovanni Petri
- ISI Foundation, Turin, Italy
- ISI Global Science Foundation, New York, NY, USA
| | - Manish Saggar
- Biophysics Program, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
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Pedoia V, Lee J, Norman B, Link TM, Majumdar S. Diagnosing osteoarthritis from T 2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort. Osteoarthritis Cartilage 2019; 27:1002-1010. [PMID: 30905742 PMCID: PMC6579664 DOI: 10.1016/j.joca.2019.02.800] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 01/23/2019] [Accepted: 02/21/2019] [Indexed: 02/02/2023]
Abstract
OBJECTIVE We aim to study to what extent conventional and deep-learning-based T2 relaxometry patterns are able to distinguish between knees with and without radiographic osteoarthritis (OA). METHODS T2 relaxation time maps were analyzed for 4,384 subjects from the baseline Osteoarthritis Initiative (OAI) Dataset. Voxel Based Relaxometry (VBR) was used for automatic quantification and voxel-based analysis of the differences in T2 between subjects with and without radiographic OA. A Densely Connected Convolutional Neural Network (DenseNet) was trained to diagnose OA from T2 data. For comparison, more classical feature extraction techniques and shallow classifiers were used to benchmark the performance of our algorithm's results. Deep and shallow models were evaluated with and without the inclusion of risk factors. Sensitivity and Specificity values and McNemar test were used to compare the performance of the different classifiers. RESULTS The best shallow model was obtained when the first ten Principal Components, demographics and pain score were included as features (AUC = 77.77%, Sensitivity = 67.01%, Specificity = 71.79%). In comparison, DenseNet trained on raw T2 data obtained AUC = 83.44%, Sensitivity = 76.99%, Specificity = 77.94%. McNemar test on two misclassified proportions form the shallow and deep model showed that the boost in performance was statistically significant (McNemar's chi-squared = 10.33, degree of freedom (DF) = 1, P-value = 0.0013). CONCLUSION In this study, we presented a Magnetic Resonance Imaging (MRI)-based data-driven platform using T2 measurements to characterize radiographic OA. Our results showed that feature learning from T2 maps has potential in uncovering information that can potentially better diagnose OA than simple averages or linear patterns decomposition.
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Affiliation(s)
- V Pedoia
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA; Center of Digital Health Innovation (CDHI), USA.
| | - J Lee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
| | - B Norman
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
| | - T M Link
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
| | - S Majumdar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA; Center of Digital Health Innovation (CDHI), USA.
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Pedoia V, Majumdar S. Translation of morphological and functional musculoskeletal imaging. J Orthop Res 2019; 37:23-34. [PMID: 30273968 DOI: 10.1002/jor.24151] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 09/24/2018] [Indexed: 02/04/2023]
Abstract
In an effort to develop quantitative biomarkers for degenerative joint disease and fill the void that exists for diagnosing, monitoring, and assessing the extent of whole joint degeneration, the past decade has been marked by a greatly increased role of noninvasive imaging. This coupled with recent advances in image processing and deep learning opens new possibilities for promising quantitative techniques. The clinical translation of quantitative imaging was previously hampered by tedious non-scalable and subjective image analysis. Osteoarthritis (OA) diagnosis using X-rays can be automated by the use of deep learning models and pilot studies showed feasibility of using similar techniques to reliably segment multiple musculoskeletal tissues and detect and stage the severity of morphological abnormalities in magnetic resonance imaging (MRI). Automation and more advanced feature extraction techniques have applications on larger more heterogeneous samples. Analyses based on voxel based relaxometry have shown local patterns in relaxation time elevations and local correlations with outcome variables. Bone cartilage interactions are also enhanced by the analysis of three-dimensional bone morphology and the potential for the assessment of metabolic activity with simultaneous Positron Emission Tomography (PET)/MR systems. Novel techniques in image processing and deep learning are augmenting imaging to be a source of quantitative and reliable data and new multidimensional analytics allow us to exploit the interactions of data from various sources. In this review, we aim to summarize recent advances in quantitative imaging, the application of image processing and deep learning techniques to study knee and hip OA. ©2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res XX:XX-XX, 2018.
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Grants
- GE Healthcare
- P50 AR060752 National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, (NIH-NIAMS)
- R01AR046905 National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, (NIH-NIAMS)
- K99AR070902 National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, (NIH-NIAMS)
- R00AR070902 National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, (NIH-NIAMS)
- R61AR073552 National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, (NIH-NIAMS)
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Affiliation(s)
- Valentina Pedoia
- Department of Radiology and Biomedical Imaging, QB3 Building, 2nd Floor, Suite 203, 1700 - 4th Street, University of California, San Francisco, California, 94158
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, QB3 Building, 2nd Floor, Suite 203, 1700 - 4th Street, University of California, San Francisco, California, 94158
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