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A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation.

Giuseppe Murdaca1, Simone Caprioli2, Alessandro Tonacci3

  • 1Department of Internal Medicine, Scleroderma Unit, Clinical Immunology Unit, University of Genoa, 16143 Genoa, Italy.

Diagnostics (Basel, Switzerland)
|October 23, 2021
PubMed
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This summary is machine-generated.

Machine learning algorithms can predict early lung involvement in Systemic Sclerosis (SSc). Combining spirometry and pH impedentiometry shows optimal prediction for early diagnosis in SSc patients.

Area of Science:

  • Rheumatology
  • Pulmonology
  • Medical Informatics

Background:

  • Systemic Sclerosis (SSc) is a severe immune-mediated disease with high mortality, primarily due to lung involvement.
  • Early diagnosis of pulmonary complications in SSc is crucial but challenging with current screening methods.
  • Machine Learning (ML) offers potential for improved diagnostic accuracy and efficiency in SSc management.

Purpose of the Study:

  • To evaluate the utility of Machine Learning (ML) algorithms in predicting early lung involvement in Systemic Sclerosis (SSc).
  • To identify key clinical and diagnostic predictors for early pulmonary disease in SSc patients.
  • To assess the performance of various ML models in this diagnostic context.

Main Methods:

  • Retrospective analysis of data from 38 SSc patients, including high-resolution computed tomography (HRCT), pulmonary function tests (PFTs), and esophageal tests.
Keywords:
HRCT chestartificial intelligenceesophageal dilatationmachine learningsystemic sclerosis

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  • Application of supervised ML algorithms (lasso, ridge, elastic net, CART, random forest) using R.
  • Estimation of important predictors for pulmonary involvement using the collected data.

    • Main Results:

    • The random forest algorithm demonstrated superior performance with a root-mean-square error (RMSE) of 0.810.
    • Random forest was computationally intensive, suggesting alternative classifiers may be preferable for faster results.
    • Predictors from spirometry and esophageal pH impedentiometry were identified as potentially optimal.

    Conclusions:

    • ML tools show promise for predicting early lung involvement in Systemic Sclerosis (SSc), even with small sample sizes.
    • Combining spirometry and pH impedentiometry data may offer an optimal approach for early SSc lung involvement prediction.
    • ML can aid in identifying the most relevant diagnostic tests, potentially reducing costs and patient burden.