Integrating Machine Learning into Myositis Research: a Systematic Review.
Authors
Affiliations (10)
Affiliations (10)
- Instituto de Investigación en Reumatología y del Sistema Músculo-Esquelético (IIRSME), Centro Universitario de Ciencias de La Salud, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico.
- Doctorado en Ciencias en Biología Molecular en Medicina, Centro Universitario de Ciencias de La Salud, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico.
- Estancias Posdoctorales Por México 2023, Humanidades y Tecnologías (SECIHTI), Secretaría de Ciencias, Ciudad de Mexico, Mexico.
- Tecnológico de Monterrey, Campus Guadalajara, Guadalajara, Jalisco, Mexico.
- Doctorado en Ciencias en Biomedicas, Centro Universitario de Ciencias de La Salud, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico.
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de La Salud, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico.
- Instituto de Investigación en Reumatología y del Sistema Músculo-Esquelético (IIRSME), Centro Universitario de Ciencias de La Salud, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico. [email protected].
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de La Salud, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico. [email protected].
- División de Medicina Interna, Servicio de Reumatología, SNP, Nuevo Hospital Civil Dr. Juan I. Menchaca, 004086-SECIHTI, Guadalajara, Jalisco, Mexico. [email protected].
- Profesor Invitado, Instituto Transdisciplinar de Investigación y Servicios (ITRANS), Centro Universitario de Ciencias Exactas E Ingenierias, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico. [email protected].
Abstract
Idiopathic inflammatory myopathies (IIM) are a group of autoimmune rheumatic diseases characterized by proximal muscle weakness and extra muscular manifestations. Since 1975, these IIM have been classified into different clinical phenotypes. Each clinical phenotype is associated with a better or worse prognosis and a particular physiopathology. Machine learning (ML) is a fascinating field of knowledge with worldwide applications in different fields. In IIM, ML is an emerging tool assessed in very specific clinical contexts as a complementary tool for research purposes, including transcriptome profiles in muscle biopsies, differential diagnosis using magnetic resonance imaging (MRI), and ultrasound (US). With the cancer-associated risk and predisposing factors for interstitial lung disease (ILD) development, this systematic review evaluates 23 original studies using supervised learning models, including logistic regression (LR), random forest (RF), support vector machines (SVM), and convolutional neural networks (CNN), with performance assessed primarily through the area under the curve coupled with the receiver operating characteristic (AUC-ROC).