ArticleDiagnostics (Basel, Switzerland)2021
A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 20 citations in OpenAlex.
- Artificial Intelligence and Machine Learning in Rheumatology and Systemic Inflammatory Diseases: From Pattern Recognition to Signal Analysis and Clinical Decision Support.Journal of clinical medicine · 2026Review
- AI-Assisted Forensic Analysis of Hanging-Related Ligature Marks: A Pilot Study Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2026Article
- Harnessing artificial intelligence to advance insights in systemic sclerosis skin and lung disease.Current opinion in rheumatology · 2025Review
- Leveraging Artificial Intelligence for the Diagnosis of Systemic Sclerosis Associated Pulmonary Arterial Hypertension: Opportunities, Challenges, and Future Perspectives.Advances in respiratory medicine · 2025Review
- Machine Learning Analysis of Electronic Health Records Identifies Interstitial Lung Disease and Predicts Mortality in Patients with Systemic Sclerosis.medRxiv : the preprint server for health sciences · 2025Article
- Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes.Diagnostics (Basel, Switzerland) · 2025Article
- Biomarkers in Systemic Sclerosis: An Overview.Current issues in molecular biology · 2023Review
- Pulmonary Nodules in Juvenile Systemic Sclerosis: A Case-Series from the National Registry for Childhood Onset Scleroderma (NRCOS).Diagnostics (Basel, Switzerland) · 2023Article
- Beyond Henssge's Formula: Using Regression Trees and a Support Vector Machine for Time of Death Estimation in Forensic Medicine.Diagnostics (Basel, Switzerland) · 2023Article
- Development and validation of machine learning for early mortality in systemic sclerosis.Scientific reports · 2022Article
- The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review.Journal of personalized medicine · 2022Review
- Deep Learning-Based Computer-Aided Diagnosis of Rheumatoid Arthritis with Hand X-ray Images Conforming to Modified Total Sharp/van der Heijde Score.Biomedicines · 2022Article
- COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN.Diagnostics (Basel, Switzerland) · 2022Article
- Prediction model for the pretreatment evaluation of mortality risk in anti-melanoma differentiation-associated gene 5 antibody-positive dermatomyositis with interstitial lung disease.Frontiers in immunology · 2022Article
- High-Resolution Computed Tomography and Lung Ultrasound in Patients with Systemic Sclerosis: Which One to Choose?Diagnostics (Basel, Switzerland) · 2021Review
- Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.Diagnostics (Basel, Switzerland) · 2021Article
- Radiomics in PET/CT and HRCT for systemic sclerosis-associated interstitial lung disease: breakthroughs and future directions.Radiologia brasileiraArticle
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSystemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations; however, this would lead to a risk of overtesting, with considerable costs for the health system and an unnecessary burden for the patients. To this extent, Machine Learning (ML) algorithms could represent a useful add-on to the current clinical practice for diagnostic purposes and could help retrieve the most useful exams to be carried out for diagnostic purposes.
methodHere, we retrospectively collected high resolution computed tomography, pulmonary function tests, esophageal pH impedance tests, esophageal manometry and reflux disease questionnaires of 38 patients with SSc, applying, with R, different supervised ML algorithms, including lasso, ridge, elastic net, classification and regression trees (CART) and random forest to estimate the most important predictors for pulmonary involvement from such data.
resultsIn terms of performance, the random forest algorithm outperformed the other classifiers, with an estimated root-mean-square error (RMSE) of 0.810. However, this algorithm was seen to be computationally intensive, leaving room for the usefulness of other classifiers when a shorter response time is needed.
conclusionsDespite the notably small sample size, that could have prevented obtaining fully reliable data, the powerful tools available for ML can be useful for predicting early lung involvement in SSc patients. The use of predictors coming from spirometry and pH impedentiometry together might perform optimally for predicting early lung involvement in SSc.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.