1
|
Danieli MG, Brunetto S, Gammeri L, Palmeri D, Claudi I, Shoenfeld Y, Gangemi S. Machine learning application in autoimmune diseases: State of art and future prospectives. Autoimmun Rev 2024; 23:103496. [PMID: 38081493 DOI: 10.1016/j.autrev.2023.103496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 11/29/2023] [Indexed: 04/30/2024]
Abstract
Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.
Collapse
Affiliation(s)
- Maria Giovanna Danieli
- SOS Immunologia delle Malattie Rare e dei Trapianti. AOU delle Marche & Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, via Tronto 10/A, 60126 Torrette di Ancona, Italy; Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy.
| | - Silvia Brunetto
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Luca Gammeri
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Davide Palmeri
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Ilaria Claudi
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Yehuda Shoenfeld
- Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, and Reichman University Herzliya, Israel.
| | - Sebastiano Gangemi
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy.
| |
Collapse
|
2
|
Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
Collapse
Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | | | | | | | | |
Collapse
|
3
|
Rimondi A, Gottlieb K, Despott EJ, Iacucci M, Murino A, Tontini GE. Can artificial intelligence replace endoscopists when assessing mucosal healing in ulcerative colitis? A systematic review and diagnostic test accuracy meta-analysis. Dig Liver Dis 2023:S1590-8658(23)01023-X. [PMID: 38057218 DOI: 10.1016/j.dld.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUNDS AND AIMS Mucosal healing (MH) in inflammatory bowel diseases (IBD) is an important landmark for clinical decision making. Artificial intelligence systems (AI) that automatically deliver the grade of endoscopic inflammation may solve moderate interobserver agreement and the need of central reading in clinical trials. METHODS We performed a systematic review of EMBASE and MEDLINE databases up to 01/12/2022 following PRISMA and the Joanna Briggs Institute methodologies to answer the following question: "Can AI replace endoscopists when assessing MH in IBD?". The research was restricted to ulcerative colitis (UC), and a diagnostic odds ratio (DOR) meta-analysis was performed. Risk of bias was evaluated with QUADAS-2 tool. RESULTS A total of 21 / 739 records were selected for full text evaluation, and 12 were included in the meta-analysis. Deep learning algorithms based on convolutional neural networks architecture achieved a satisfactory performance in evaluating MH on UC, with sensitivity, specificity, DOR and SROC of respectively 0.91(CI95 %:0.86-0.95);0.89(CI95 %:0.84-0.93);92.42(CI95 %:54.22-157.53) and 0.957 when evaluating fixed images (n = 8) and 0.86(CI95 %:0.75-0.93);0.91(CI95 %:0.87-0.94);70.86(CI95 %:24.63-203.86) and 0.941 when evaluating videos (n = 6). Moderate-high levels of heterogeneity were noted, limiting the quality of the evidence. CONCLUSIONS AI systems showed high potential in detecting MH in UC with optimal diagnostic performance, although moderate-high heterogeneity of the data was noted. Standardised and shared AI training may reduce heterogeneity between systems.
Collapse
Affiliation(s)
- Alessandro Rimondi
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom.
| | | | - Edward J Despott
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom
| | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College of Cork, Cork, Ireland
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom; Department of Gastroenterology, Cleveland Clinic London, London, United Kingdom
| | - Gian Eugenio Tontini
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Gastroenterology and Endoscopy unit, Milan, Italy
| |
Collapse
|
4
|
Lv B, Ma L, Shi Y, Tao T, Shi Y. A systematic review and meta-analysis of artificial intelligence-diagnosed endoscopic remission in ulcerative colitis. iScience 2023; 26:108120. [PMID: 37867944 PMCID: PMC10585391 DOI: 10.1016/j.isci.2023.108120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/08/2023] [Accepted: 09/29/2023] [Indexed: 10/24/2023] Open
Abstract
Endoscopic remission is an important therapeutic goal in ulcerative colitis (UC). The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Mayo Endoscopic Score (MES) are the commonly used endoscopic scoring criteria. This systematic review and meta-analysis aimed to evaluate the accuracy of artificial intelligence (AI) in diagnosing endoscopic remission in UC. We also performed a meta-analysis of each of the four endoscopic remission criteria (UCEIS = 0, MES = 0, UCEIS = <1, MES = <1). Eighteen studies involving 13,687 patients were included. The combined sensitivity and specificity of AI for diagnosing endoscopic remission in UC was 87% (95% confidence interval [CI]:81-92%) and 92% (95% CI: 89-94%), respectively. The area under the curve (AUC) was 0.96 (95% CI: 0.94-0.97). The results showed that the AI model performed well regardless of which criteria were used to define endoscopic remission of UC.
Collapse
Affiliation(s)
- Bing Lv
- School of Computer Science and Technology, Shandong University of Technology, NO.266, Xincunxi Road, Zibo, Shandong 255000, China
| | - Lihong Ma
- Department of Gastroenterology, Zibo Central Hospital, No.10 Shanghai Road, Zibo, Shandong 255000, China
| | - Yanping Shi
- Department of Pediatrics, Zhoucun Maternal and Child Health Care Hospital, No.72 Mianhuashi Street, Zibo, Shandong 255000, China
| | - Tao Tao
- Department of Gastroenterology, Zibo Central Hospital, No.10 Shanghai Road, Zibo, Shandong 255000, China
| | - Yanting Shi
- Department of Gastroenterology, Zibo Central Hospital, No.10 Shanghai Road, Zibo, Shandong 255000, China
| |
Collapse
|
5
|
Ang TL, Wang LM. Artificial intelligence for the diagnosis of dysplasia in inflammatory bowel diseases. J Gastroenterol Hepatol 2022; 37:1469-1470. [PMID: 35922056 DOI: 10.1111/jgh.15943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Affiliation(s)
- Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, SingHealth, Singapore.,Duke-NUS Medical School, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Lai Mun Wang
- Duke-NUS Medical School, Singapore.,Section of Pathology, Department of Laboratory Medicine, Changi General Hospital, SingHealth, Singapore
| |
Collapse
|