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  • 1Department of Radiology, Istituto Figlie di San Camillo, 26100 Cremona, Italy.

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Summary

Artificial intelligence (AI) tools show promise in diagnosing and monitoring interstitial lung diseases (ILDs) and interstitial lung abnormalities (ILAs) using high-resolution computed tomography (HRCT). Further research and collaboration are needed to integrate these AI advancements into clinical practice.

Keywords:
AI (artificial intelligence)hierarchical learninginterstitial lung disease

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Area of Science:

  • Pulmonology and Radiology
  • Artificial Intelligence in Medical Imaging

Background:

  • Recent years have seen evolving approaches to diagnosing and treating interstitial lung diseases (ILDs), including the emergence of interstitial lung abnormalities (ILAs) and progressive pulmonary fibrosis (PPF).
  • Clinicians and radiologists face significant challenges in the screening, diagnosis, prognosis, and follow-up of ILDs, exacerbated by high inter-reader variability in interpreting high-resolution computed tomography (HRCT) scans.
  • The accurate detection and classification of ILAs and the identification of fibrosis progression on HRCT remain difficult, especially for non-expert readers.

Purpose of the Study:

  • To review the advancements and potential of artificial intelligence (AI) software in the evaluation of ILDs using HRCT.
  • To highlight the capabilities of AI in classifying ILDs, quantifying disease extent, identifying subtle imaging features, and predicting prognosis and progression.
  • To discuss the need for further collaboration and robust clinical trials to validate AI tools for routine clinical implementation in ILD management.

Main Methods:

  • Review of recently developed software tools for ILD evaluation at HRCT.
  • Analysis of AI capabilities in classifying ILDs, quantifying disease extent, and predicting prognosis.
  • Consideration of AI's potential to integrate clinical and radiological data for personalized prognosis and treatment guidance.

Main Results:

  • AI software has demonstrated excellent results in ILD evaluation at HRCT, often matching or exceeding human reliability.
  • AI tools can effectively classify ILDs, quantify disease extent, detect features not apparent to the human eye, and predict prognosis and disease progression.
  • Advanced AI can incorporate multimodal data for personalized prognostication, potentially guiding treatment decisions.

Conclusions:

  • AI tools offer significant potential to improve the accuracy and efficiency of ILD diagnosis, prognosis, and monitoring via HRCT.
  • Implementation of these tools in daily practice requires increased collaboration for collecting homogeneous data and conducting robust prospective trials.
  • Further research comparing AI-derived biomarkers is essential to establish their real-world reliability in computer-aided ILD evaluation.