Table 1 Machine learning and artificial intelligence applications to autoimmune diseases.

From: A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Disease

Number of studies

Years

Most popular classification/prediction application(s)

Most popular machine learning method(s)

Median sample size (min, max)

Data types used

Multiple sclerosis

4130,45,50,51,60,61,71,91,92,93,100,101,111,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144

2008–2019

Diagnosis, Prognosis, Disease Subtype

Type of Regression, Random Forest, Support Vector Machine

99 (12, 12566)

Clinical, Survey, Genetic, MRI, Lipid Markers, SNPs, Gait Data, Immune repertoire, Gene Expression

Rheumatoid arthritis

3220,21,22,26,27,31,32,40,41,42,46,47,48,52,59,62,63,64,70,72,80,81,82,88,97,145,146,147,148,149,150,151

2003–2018

Risk, Diagnosis, Early Diagnosis, Identify Patients

Support Vector Machine, Variations of Random Forest, Neural Network and Decision Tree

338 (22, 922199)

Medical Database, Immunoassay, Metagenomic, Microbiome, GWAS/SNP, Clinical, Movement Data, Amino acid analytes, Transcriptomic, EMRs, Ultrasound images, Proteomic, Laser images

Inflammatory bowel disease

3033,34,35,36,43,57,69,73,79,83,84,85,86,94,95,98,152,153,154,155,156,157,158,159,160,161,162,163,164,165

2007–2018

Diagnosis, Response to Treatment, Disease Risk, Disease Severity

Random Forest, Support Vector Machine

273 (50, 53279)

Clinical, Colonoscopy Images, Metagenomic, Gene Expression, GWAS, Microbiota, miRNA Expression, EMRs, Exome, MRI

Type 1 diabetes

1737,38,39,67,68,102,103,104,166,167,168,169,170,171,172,173,174

2009–2018

Disease Management

Novel Methods/Hybrid Models, Neural Network, Support Vector Regression

23 (10, 10579)

Clinical, Red Blood Cell Images, VOCs, GWAS/SNPs

Systemic lupus erythematosus

1419,23,44,49,89,96,175,176,177,178,179,180,181,182

2009–2018

Variations of prognosis, Diagnosis

Logistic Regression, Neural Network, Random Forest Decision Tree

318 (14, 17057)

Clinical, Electronic Health Records, Drug Treatment, SNPs, MRI, Exome, Gene Expression, Proteomic, Urine Biomarkers

Psoriasis

1153,74,75,76,77,99,112,183,184,185,186

2007–2018

Diagnosis, Disease Severity

Support Vector Machine

540 (80, 22181)

Digital Image, GWAS, Proteomic, RNA Biomarkers

Coeliac disease

724,25,54,65,66,78,187

2011–2018

Diagnosis

Random Forest, Logistic Regression, Bayesian Classifier, Support Vector Machine, Logistic Model, Natural Language Processing, Combined Fuzzy Cognitive Map and Possibilistic Fuzzy c-means clustering.

465 (47, 1498)

VOCs, Clinical, Peptide, EMRs

Thyroid diseases

6188,189,190,191,192,193

2008–2018

Diagnosis

Hybrid Models

215 (215, 7200)

Clinical

Autoimmune liver diseases

558,87,90,194,195

2009–2018

Prognosis

Variations on Random Forest

288 (64, 787)

Clinical, Clinical Trial, Microbiome

Systemic sclerosis

455,113,196,197

2016–2018

Diagnosis, Treatment, Prognosis

Support Vector Machine, Random Forest

119 (37, 991)

Gene Expression, Nailfold capillaroscopy images, Peripheral Blood Mononuclear cell data (flow cytometry, DNA, mRNA)

  1. Information includes the number of studies per autoimmune disease, the years they occurred, popular applications and methods and data types used. Median sample size was a better representation than mean, due to large cohorts in studies using data from genome-wide association studies and electronic medical records.
  2. EMR electronic medical record, GWAS genome-wide association study, miRNA micro RNA, MRI magnetic resonance imaging, SNP single nucleotide polymorphism, VOC volatile organic compound.
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