8
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      An update on animal models of intervertebral disc degeneration and low back pain: Exploring the potential of artificial intelligence to improve research analysis and development of prospective therapeutics

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Animal models have been invaluable in the identification of molecular events occurring in and contributing to intervertebral disc (IVD) degeneration and important therapeutic targets have been identified. Some outstanding animal models (murine, ovine, chondrodystrophoid canine) have been identified with their own strengths and weaknesses. The llama/alpaca, horse and kangaroo have emerged as new large species for IVD studies, and only time will tell if they will surpass the utility of existing models. The complexity of IVD degeneration poses difficulties in the selection of the most appropriate molecular target of many potential candidates, to focus on in the formulation of strategies to effect disc repair and regeneration. It may well be that many therapeutic objectives should be targeted simultaneously to effect a favorable outcome in human IVD degeneration. Use of animal models in isolation will not allow resolution of this complex issue and a paradigm shift and adoption of new methodologies is required to provide the next step forward in the determination of an effective repairative strategy for the IVD. AI has improved the accuracy and assessment of spinal imaging supporting clinical diagnostics and research efforts to better understand IVD degeneration and its treatment. Implementation of AI in the evaluation of histology data has improved the usefulness of a popular murine IVD model and could also be used in an ovine histopathological grading scheme that has been used to quantify degenerative IVD changes and stem cell mediated regeneration. These models are also attractive candidates for the evaluation of novel anti‐oxidant compounds that counter inflammatory conditions in degenerate IVDs and promote IVD regeneration. Some of these compounds also have pain‐relieving properties. AI has facilitated development of facial recognition pain assessment in animal IVD models offering the possibility of correlating the potential pain alleviating properties of some of these compounds with IVD regeneration.

          Abstract

          Animal models of IVD deneration have yielded invaluable information on the pathobiology of this degenerative condition and identified prospective therapeutic targets.The complexity of the degenerative changes and multiple therapeutic targets identified by these models suggests artificial intelligence methodology may be required to unravel these complexities and provide a rationale way forward in the development of effective repair strategies.

          Related collections

          Most cited references394

          • Record: found
          • Abstract: found
          • Article: found

          Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

          Non-fatal health outcomes from diseases and injuries are a crucial consideration in the promotion and monitoring of individual and population health. The Global Burden of Disease (GBD) studies done in 1990 and 2000 have been the only studies to quantify non-fatal health outcomes across an exhaustive set of disorders at the global and regional level. Neither effort quantified uncertainty in prevalence or years lived with disability (YLDs). Of the 291 diseases and injuries in the GBD cause list, 289 cause disability. For 1160 sequelae of the 289 diseases and injuries, we undertook a systematic analysis of prevalence, incidence, remission, duration, and excess mortality. Sources included published studies, case notification, population-based cancer registries, other disease registries, antenatal clinic serosurveillance, hospital discharge data, ambulatory care data, household surveys, other surveys, and cohort studies. For most sequelae, we used a Bayesian meta-regression method, DisMod-MR, designed to address key limitations in descriptive epidemiological data, including missing data, inconsistency, and large methodological variation between data sources. For some disorders, we used natural history models, geospatial models, back-calculation models (models calculating incidence from population mortality rates and case fatality), or registration completeness models (models adjusting for incomplete registration with health-system access and other covariates). Disability weights for 220 unique health states were used to capture the severity of health loss. YLDs by cause at age, sex, country, and year levels were adjusted for comorbidity with simulation methods. We included uncertainty estimates at all stages of the analysis. Global prevalence for all ages combined in 2010 across the 1160 sequelae ranged from fewer than one case per 1 million people to 350,000 cases per 1 million people. Prevalence and severity of health loss were weakly correlated (correlation coefficient -0·37). In 2010, there were 777 million YLDs from all causes, up from 583 million in 1990. The main contributors to global YLDs were mental and behavioural disorders, musculoskeletal disorders, and diabetes or endocrine diseases. The leading specific causes of YLDs were much the same in 2010 as they were in 1990: low back pain, major depressive disorder, iron-deficiency anaemia, neck pain, chronic obstructive pulmonary disease, anxiety disorders, migraine, diabetes, and falls. Age-specific prevalence of YLDs increased with age in all regions and has decreased slightly from 1990 to 2010. Regional patterns of the leading causes of YLDs were more similar compared with years of life lost due to premature mortality. Neglected tropical diseases, HIV/AIDS, tuberculosis, malaria, and anaemia were important causes of YLDs in sub-Saharan Africa. Rates of YLDs per 100,000 people have remained largely constant over time but rise steadily with age. Population growth and ageing have increased YLD numbers and crude rates over the past two decades. Prevalences of the most common causes of YLDs, such as mental and behavioural disorders and musculoskeletal disorders, have not decreased. Health systems will need to address the needs of the rising numbers of individuals with a range of disorders that largely cause disability but not mortality. Quantification of the burden of non-fatal health outcomes will be crucial to understand how well health systems are responding to these challenges. Effective and affordable strategies to deal with this rising burden are an urgent priority for health systems in most parts of the world. Bill & Melinda Gates Foundation. Copyright © 2012 Elsevier Ltd. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The potential for artificial intelligence in healthcare

            The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A systematic review of the global prevalence of low back pain.

              To perform a systematic review of the global prevalence of low back pain, and to examine the influence that case definition, prevalence period, and other variables have on prevalence. We conducted a new systematic review of the global prevalence of low back pain that included general population studies published between 1980 and 2009. A total of 165 studies from 54 countries were identified. Of these, 64% had been published since the last comparable review. Low back pain was shown to be a major problem throughout the world, with the highest prevalence among female individuals and those aged 40-80 years. After adjusting for methodologic variation, the mean ± SEM point prevalence was estimated to be 11.9 ± 2.0%, and the 1-month prevalence was estimated to be 23.2 ± 2.9%. As the population ages, the global number of individuals with low back pain is likely to increase substantially over the coming decades. Investigators are encouraged to adopt recent recommendations for a standard definition of low back pain and to consult a recently developed tool for assessing the risk of bias of prevalence studies. Copyright © 2012 by the American College of Rheumatology.
                Bookmark

                Author and article information

                Contributors
                james.melrose@sydney.edu.au
                Journal
                JOR Spine
                JOR Spine
                10.1002/(ISSN)2572-1143
                JSP2
                JOR Spine
                John Wiley & Sons, Inc. (Hoboken, USA )
                2572-1143
                30 January 2023
                March 2023
                : 6
                : 1 ( doiID: 10.1002/jsp2.v6.1 )
                : e1230
                Affiliations
                [ 1 ] AO Research Institute Davos Platz Switzerland
                [ 2 ] Spine Service, Department of Orthopedic Surgery, St. George & Sutherland Campus, Clinical School University of New South Wales Sydney New South Wales Australia
                [ 3 ] Department of Surgery University of Toronto Ontario Canada
                [ 4 ] Raymond Purves Bone and Joint Research Laboratory Kolling Institute, Sydney University Faculty of Medicine and Health, Northern Sydney Area Health District, Royal North Shore Hospital St. Leonards New South Wales Australia
                [ 5 ] Graduate School of Biomedical Engineering The University of New South Wales Sydney New South Wales Australia
                Author notes
                [*] [* ] Correspondence

                James Melrose, Graduate School of Biomedical Engineering, The University of New South Wales, Sydney, New South Wales 2052, Australia.

                Email: james.melrose@ 123456sydney.edu.au

                Author information
                https://orcid.org/0000-0002-0262-1412
                https://orcid.org/0000-0001-9237-0524
                Article
                JSP21230
                10.1002/jsp2.1230
                10041392
                36994457
                40244e35-4a56-4bac-a937-c0c80732a567
                © 2023 The Authors. JOR Spine published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 31 August 2022
                : 13 February 2022
                : 11 September 2022
                Page count
                Figures: 5, Tables: 4, Pages: 29, Words: 28752
                Funding
                Funded by: National Health and Medical Research Council , doi 10.13039/501100000925;
                Award ID: 10010163
                Award ID: 1004032
                Award ID: 910508
                Categories
                Review
                Reviews
                Custom metadata
                2.0
                March 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.6 mode:remove_FC converted:27.03.2023

                animal models of disc degeneration,artificial intelligence and deep machine learning,intervertebral disc,intervertebral disc degeneration,intervertebral disc regeneration,low back‐pain

                Comments

                Comment on this article