Artificial Intelligence gives new hope for patients with Idiopathic Pulmonary Fibrosis
Machine Learning (ML) is an algorithmic approach that leverages big data to identify patterns and make predictions, classifications, and optimizations for specific tasks. It is a powerful tool that can deliver high performance, especially in the area of imaging diagnosis for interstitial lung disease. By learning from previously observed data, ML can effectively identify and classify patterns to aid in accurate and efficient diagnosis. In this article, let’s find out how AI gives new hope for patients with Idiopathic Pulmonary Fibrosis.
According to the National Institutes of Health (NIH), about 100,000 people in the United States have IPF. Approximately 30,000 to 40,000 new cases are diagnosed each year.
What is Idiopathic pulmonary fibrosis?
Idiopathic pulmonary fibrosis is a chronic, progressive lung disease. This condition causes scar tissue (fibrosis) to build up in the lungs, which makes the lungs unable to transport oxygen into the bloodstream effectively.
Some common signs can be mentioned that are shortness of breath and a persistent dry, hacking cough. Many affected individuals also experience a loss of appetite and gradual weight loss. Some people with idiopathic pulmonary fibrosis develop widened and rounded tips of the fingers and toes (clubbing) resulting from a shortage of oxygen.
Application of Artificial Intelligence in Idiopathic Pulmonary Fibrosis
1. Identify and segment interstitial lung disease images on high-resolution computed tomography (HRCT) scans
Machine learning approaches, specifically deep learning, can be used to identify, categorize, and segment interstitial lung disease (ILD) images on HRCT scans. The commonly used approach is a convolutional neural network (CNN) segmentation method, such as U-Net. Data augmentation techniques, such as flipping, rotation, cropping, and scaling, can be used to increase the amount of available training data.
By combining data augmentation and deep learning techniques, the accuracy of fibrosis morphological classification can be improved.
Machine learning algorithms can also be used to classify HRCT images of pulmonary fibrosis based on the 2011 ATS/ERS/JRS/ALAT criteria and the 2018 Fleischner Society criteria.
2. Evaluate the efficacy of antifibrosis therapy in IPF patients
ML used to evaluate the efficacy of antifibrosis therapy in IPF patients by analyzing and quantifying CT images of the patients’ lungs, effectively providing insights into disease progression or the impact of treatment. One such quantitative method is the CALIPER software, which extracts data by processing high-resolution computed tomography (HRCT) images to identify and track several CT-sensitive characteristics, such as reticulation, honeycombing, or texture-based features.
By correlating CALIPER-derived CT features with lung function parameters such as forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1), the software provides an objective measure of pulmonary fibrosis severity and allows monitoring treatment response.
It is important to note that some limitations and challenges exist when using machine learning for evaluating the efficacy of antifibrosis therapy in IPF patients, such as the need for large training datasets, the accurate execution of lung volume measurement, and the potential impact of radiation dose on CT-derived measures
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3. Track and and predict disease progression.
AI can help predict disease progression and treatment outcomes by analyzing patient data and medical history. This can help clinicians make informed decisions about treatment options and enable earlier intervention to slow disease progression.
CALIPER is a machine learning tool that has been shown to be useful for tracking and forecasting disease progression in ILD patients. The tool allows for quantitative analysis of HRCT images to identify CT-sensitive characteristics that can accurately predict death in ILD patients. CALIPER parameters have also been shown to correspond well with lung function results and can be a potent and objective addition to traditional lung function proxies in the absence of confounding factors affecting lung function.
In order to evaluate 2424 subjects and forecast the severity of pulmonary fibrosis patients, Sikandar et al.  built and trained the Forest model. The model’s sensitivity and accuracy were 0.71 and 0.64, respectively. This model will assist medical professionals in accurately diagnosing IPF patients, determining the disease’s severity at an early stage, and taking prompt corrective action to treat IPF.
4. Develop models for diagnosing and classifying pulmonary fibrosis
By analyzing HRCT images of patients with interstitial lung diseases (ILD). These approaches involve the use of convolutional neural networks (CNNs) like the U-Net model, which is widely employed for segmenting ILD images.
First, a dataset of HRCT images is prepared for training the deep learning model. This may include augmenting the existing data by applying various transformations like image flipping, rotation, cropping, and scaling. This data augmentation process enhances the diversity of the dataset and helps to improve the model’s performance.
Next, the training process begins, where the model learns to detect and classify different morphological features associated with pulmonary fibrosis, such as reticulation, honeycombing, and irregular linear shadows. The trained model’s accuracy is then evaluated on a separate set of HRCT images that have not been used during the training phase.
Once the model is sufficiently accurate in diagnosing and classifying pulmonary fibrosis, it can be used by clinicians to assess the severity of the disease and assist in treatment decision-making
The Forest model developed by Sikandar et al. achieved a sensitivity of 0.71 and an accuracy of 0.64 in predicting the severity of pulmonary fibrosis in patients. Such models can help clinicians make timely treatment choices and potentially avoid invasive surgical lung biopsy procedures.
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