1
|
Damer A, Chaudry E, Eftekhari D, Benseler SM, Safi F, Aviv RI, Tyrrell PN. Neuroimaging Scoring Tools to Differentiate Inflammatory Central Nervous System Small-Vessel Vasculitis: A Need for Artificial Intelligence/Machine Learning?-A Scoping Review. Tomography 2023; 9:1811-1828. [PMID: 37888736 PMCID: PMC10610796 DOI: 10.3390/tomography9050144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
Neuroimaging has a key role in identifying small-vessel vasculitis from common diseases it mimics, such as multiple sclerosis. Oftentimes, a multitude of these conditions present similarly, and thus diagnosis is difficult. To date, there is no standardized method to differentiate between these diseases. This review identifies and presents existing scoring tools that could serve as a starting point for integrating artificial intelligence/machine learning (AI/ML) into the clinical decision-making process for these rare diseases. A scoping literature review of EMBASE and MEDLINE included 114 articles to evaluate what criteria exist to diagnose small-vessel vasculitis and common mimics. This paper presents the existing criteria of small-vessel vasculitis conditions and mimics them to guide the future integration of AI/ML algorithms to aid in diagnosing these conditions, which present similarly and non-specifically.
Collapse
Affiliation(s)
- Alameen Damer
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
- Department of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Emaan Chaudry
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
| | - Daniel Eftekhari
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
| | - Susanne M. Benseler
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Frozan Safi
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
| | - Richard I. Aviv
- Department of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Pascal N. Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
- Institute of Medical Science, Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1X6, Canada
| |
Collapse
|
2
|
Barbetta A, Rocque B, Sarode D, Bartlett JA, Emamaullee J. Revisiting transplant immunology through the lens of single-cell technologies. Semin Immunopathol 2023; 45:91-109. [PMID: 35980400 PMCID: PMC9386203 DOI: 10.1007/s00281-022-00958-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022]
Abstract
Solid organ transplantation (SOT) is the standard of care for end-stage organ disease. The most frequent complication of SOT involves allograft rejection, which may occur via T cell- and/or antibody-mediated mechanisms. Diagnosis of rejection in the clinical setting requires an invasive biopsy as there are currently no reliable biomarkers to detect rejection episodes. Likewise, it is virtually impossible to identify patients who exhibit operational tolerance and may be candidates for reduced or complete withdrawal of immunosuppression. Emerging single-cell technologies, including cytometry by time-of-flight (CyTOF), imaging mass cytometry, and single-cell RNA sequencing, represent a new opportunity for deep characterization of pathogenic immune populations involved in both allograft rejection and tolerance in clinical samples. These techniques enable examination of both individual cellular phenotypes and cell-to-cell interactions, ultimately providing new insights into the complex pathophysiology of allograft rejection. However, working with these large, highly dimensional datasets requires expertise in advanced data processing and analysis using computational biology techniques. Machine learning algorithms represent an optimal strategy to analyze and create predictive models using these complex datasets and will likely be essential for future clinical application of patient level results based on single-cell data. Herein, we review the existing literature on single-cell techniques in the context of SOT.
Collapse
Affiliation(s)
- Arianna Barbetta
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA
- University of Southern California, Los Angeles, CA, USA
| | - Brittany Rocque
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA
- University of Southern California, Los Angeles, CA, USA
| | - Deepika Sarode
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA
- University of Southern California, Los Angeles, CA, USA
| | - Johanna Ascher Bartlett
- Pediatric Gastroenterology, Hepatology and Nutrition, Children's Hospital of Los Angeles, Los Angeles, CA, USA
| | - Juliet Emamaullee
- Department of Surgery, Division of Abdominal Organ Transplant, University of Southern California, 1510 San Pablo St. Suite 412, Los Angeles, CA, 90033, USA.
- University of Southern California, Los Angeles, CA, USA.
- Division of Hepatobiliary and Abdominal Organ Transplantation Surgery, Children's Hospital Los Angeles, Los Angeles, CA, USA.
| |
Collapse
|
3
|
Bhat M, Rabindranath M. The promise of artificial intelligence for predictive biomarkers in hepatology. Hepatol Int 2022; 16:523-525. [PMID: 35575965 DOI: 10.1007/s12072-022-10342-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/13/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
- Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
| | - Madhumitha Rabindranath
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
4
|
Suri JS, Bhagawati M, Paul S, Protogerou AD, Sfikakis PP, Kitas GD, Khanna NN, Ruzsa Z, Sharma AM, Saxena S, Faa G, Laird JR, Johri AM, Kalra MK, Paraskevas KI, Saba L. A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12030722. [PMID: 35328275 PMCID: PMC8947682 DOI: 10.3390/diagnostics12030722] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/10/2022] [Accepted: 03/13/2022] [Indexed: 12/16/2022] Open
Abstract
Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.
Collapse
Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong 793022, India; (M.B.); (S.P.)
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong 793022, India; (M.B.); (S.P.)
| | - Athanasios D. Protogerou
- Research Unit Clinic, Laboratory of Pathophysiology, Department of Cardiovascular Prevention, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 11527 Athens, Greece;
| | - George D. Kitas
- Arthritis Research UK Centre for Epidemiology, Manchester University, Manchester 46962, UK;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110020, India;
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary;
| | - Aditya M. Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22903, USA;
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India;
| | - Gavino Faa
- Department of Pathology, A.O.U., di Cagliari-Polo di Monserrato s.s., 09045 Cagliari, Italy;
| | - John R. Laird
- Cardiology Department, St. Helena Hospital, St. Helena, CA 94574, USA;
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, N. Iraklio, 14122 Athens, Greece;
| | - Luca Saba
- Department of Radiology, A.O.U., di Cagliari-Polo di Monserrato s.s., 09045 Cagliari, Italy;
| |
Collapse
|