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How to achieve earlier lung disease detection: the automated approach

Apr 11, 2022

5 minutes, 23 seconds

Most of us never think about our breathing; it’s just something we do. For example, research by Lung Foundation Australia shows that almost half of all Australians rarely or never think about their lung health. Despite this, almost two-thirds of Australians reported that they had experienced at least one lung-related health issue. People often assume that there are symptoms of aging or lack of fitness. Any problem that prevents the lungs from working properly is called lung disease – this may be lung circulation, lung tissue, or a disease of the airways. There are over 30 arts of lung disease, beginning from influenza and asthma to occupational lung disease and lung cancer. And don’t forget about COVID-19 pandemic!

Lung disease can’t discriminate and affects women, men, children, ex-smokers, smokers, and non-smokers. Improving outcomes, bettering treatment options, and saving lives are critical to getting an early diagnosis.

Symptoms of lung disease include weariness, coughing up mucus, coughing up blood, and chest pain. Often lung disease is diagnosed in its later stages, reducing the chance of effective treatment. Early detection can help to treat or, at the very least, fully stall the progression of lung disease.

Challenges of diagnosing pneumonia, COVID-19, and other lung diseases

Pneumonia is a complicated disease to diagnose because it could be caused by any number of pathogens that lead to a bacterial, fungal, or viral infection in the lungs. It can be contracted almost anywhere, including in hospitals. The problem is that pneumonia could be caused not just by a single disease but even by a group of specific infections. Each such infection has different epidemiology, pathogenesis, presentation, and clinical course.

According to the WHO statistics, nowadays, pneumonia is one of the leading causes of death worldwide.

However, the research of Le Roux and Zar says that pneumonia is the most common cause of death in children who have passed the neonatal period in the world. In the UK, pneumonia accounts for much more admissions and bed days than any other lung disease, with over 200,000 entries per year – this equates to 2.3 million bed days. Between 5% and 15% of patients hospitalized with pneumonia die within 30 days of admission. This figure rises to 30% for patients who are admitted to intensive care.

Why are images so important?

The main clinical diagnostic criteria for many types of pneumonia and coronavirus disease (COVID-19) have been imaging exams, which are especially important. The characteristic manifestations on chest X-Ray or CT imaging features of multiple patchy areas of ground-glass opacity and consolidation predominantly in the periphery of the lungs are beneficial in the early detection and diagnosis of this disease. That aids in prompt diagnosis and the eventual control of this emerging global health emergency.

In the clinical work of this pandemic, radiologists play a crucial role in the fast detection and early diagnosis of a suspected patient. This can become a great benefit for patients, public health surveillance, and response systems.

Related: Artificial intelligence can help fight COVID-19

Chest radiography is the most helpful screening tool on the frontlines in medical settings with limited resources. Chest radiography is also beneficial in cases where the patient’s physical condition does not allow for transport to the radiology department CT scanner. Chest radiography can detect multiple patchy opacities throughout the lungs as the disease progresses beyond the early stage.

Computed tomography (CT) imaging is very sensitive to detecting early disease, assessing the nature and extent of lesions, and discovering subtle changes that are often not visible on chest radiography. The imaging features of lesions are always described with the following factors: distribution, quantity, shape, pattern, density, and accompanying signs.

Chest X-rays have long been considered the best tool to detect any form of pneumonia. But the problem is that pneumonia can appear similar to other conditions on a scan, and imaging cannot identify the infectious pathogen, making the diagnosis of pneumonia via X-ray a challenge. This is especially true when patients are experiencing multiple health problems simultaneously.

However, artificial intelligence and machine learning can be used for solving complex data analysis problems, optimization of practices, and the diagnosis of life-threatening diseases like pneumonia.​

  • For example, the algorithm ‘CheXNet’ is an artificial neural network designed to detect pneumonia from chest X-rays at a performance rate more significant than the average radiologist.​​
  • The next example, ‘hive mind’, uses Artificial Swarm Intelligence (ASI) to combine and utilize the individual capacities of a small group of radiologists together, in real-time, to procure an optimal diagnosis or solution. This technique results better than individual doctors or algorithms detecting pneumonia by X-ray.

Software solutions for early lung disease detection

The current situation requires fast, widely accessible diagnostic tools that would preferably be available yesterday or now. Speed, availability, and ease of application in a realistic clinical context of the current COVID-19 pandemic are most important. In this case, AI and ML solutions will play a key role.

The AI-based algorithms identify the infected areas that the physician should pay attention to for diagnosis.

Based on the reference information provided in the development, algorithms show the likelihood of a particular disease as a percentage. Users can also share the received data with colleagues through the network for consultations which reduces the probability of making the wrong diagnosis.

Such applications are very useful for physician assistants. And its goal is to reduce the burden on radiologists when the healthcare systems are operating beyond their capabilities and reduce the likelihood of a diagnostic error caused by overwork and psychological pressure.

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