As human beings, we all have a chance to health issues, and respiratory problems are among the most prevalent. Our lungs, essential respiratory system components, require vigilant care to function optimally. Protecting lung health begins with avoiding unhealthy habits like smoking and exposure to pollutants, as well as prioritizing regular check-ups and screenings to catch potential diseases early.

While we can make choices to improve our lung health, advances in artificial intelligence are also changing how we understand and treat it. In this blog, let’s discuss how AI is transforming lung health and shaping the promising future of respiratory care.

Prediction and Early Detection

Prediction and early detection are powerful methods for surviving or preventing health issues. In the era of AI, we have the opportunity to know earlier if there is a chance of lung disease developing in our body.

The AI model integrates multiple data sources, including genetic information, smoking habits, living environment, and medical history. By combining these data, AI can create personalised profiles and predict the likelihood of developing lung diseases. AI can also analyze changes in lung structure and function over time. This enables the prediction of future disease development before symptoms appear. AI not only predicts the chances of a disease, but it can also forecast a patient’s condition after hospitalisation. For example, researchers in Taiwan developed a model that predicts health outcomes for Chronic Obstructive Pulmonary Disease (COPD) patients after hospital admission. The model helps doctors predict mortality rates, ventilator dependence, and respiratory failure.

AI can detect disease early. For instance, lung cancer may present with early-stage nodules that AI can identify, while radiologists might miss. In conditions like COPD, AI can detect early fibrosis or tissue damage. Google created an AI model that can analyze 3D images from CT scans. This model looks for tiny signs of lung cancer that can be hard for human doctors to spot. It even compares current scans with older ones to see if anything has changed, which helps predict whether someone is at risk for lung cancer.

Diagnosis

Prediction and early detection are the first steps, but diagnosing a disease is crucial, because treatment can only begin after diagnosis. It is the responsibility of experienced medical professionals. In this era, AI is becoming a valuable tool in the diagnostic process. AI helps medical professionals diagnose lung diseases by improving accuracy and reducing the time needed for diagnosis.

AI models are trained on large datasets of medical information, including CT images, MRIs, X-rays, clinical reports, and even human sounds. These models are trained with millions of data samples, allowing them to accurately identify abnormalities in new, unseen data. AI algorithms can quickly analyse data, often faster than human radiologists. For example, according to the Lung Cancer Network Malaysia (LCNM), a 67-year-old male smoker with no symptoms had a suspicious shadow in his upper left lung detected by an AI-enabled chest X-ray. Even though he had no symptoms, this early detection and diagnosis could have been missed by human doctors alone.

Medical professionals can trust AI’s diagnostic capabilities through a combination of validation, transparency, and regulatory oversight. AI tools undergo rigorous validation and peer review to ensure their accuracy and consistency. Additionally, some AI systems utilise explainable AI (XAI), which provides insights into how the system arrived at a particular diagnosis, increasing trust and transparency in the diagnostic process. AI can complement the skills of healthcare professionals, but it cannot replace their judgement and experience.

Treatment

AI is increasingly being used not just for the prediction and diagnosis of lung diseases but also for treatment management.

Personalized Treatment Plans

If a disease is diagnosed, AI can assist doctors in creating personalized treatment plans. AI can analyze a patient’s genetic data, medical history, and other factors to identify specific mutations associated with lung diseases. This information helps provide customized treatment for individual patients. By analyzing historical and genetic data, AI can predict how a patient will respond to particular medicines, enabling doctors to choose the most appropriate treatment options.

Patient Monitoring and Remote Care

AI tools can continuously monitor a patient’s lung function and condition, allowing doctors to adjust treatment dynamically. Real-time monitoring updates the patient’s condition to doctors. For instance, in patients with COPD, AI can track oxygen levels and lung function in real-time, adjusting ventilator settings or oxygen therapy as needed. AI also facilitates remote consultations between patients and medical professionals, improving patient outcomes.

Robotic Surgery Assistance

AI is also being used to assist surgeons in planning and performing lung surgeries. It provides surgeons with 3D models, reducing the complexity and risks involved in surgery. This helps minimize the chances of damaging surrounding tissues during the procedure, making surgeries safer and more precise.

So far, we’ve explored AI’s potential in disease prediction, diagnosis, and treatment. But how is an AI system trained? What kind of data is used to teach these systems to make accurate decisions? In the next section, we’ll discuss how AI is trained and the role of training data in shaping its capabilities.

The Role of Medical Data Labeling

In the above, we discussed how AI can predict, diagnose, and even treat diseases. But how did AI gain this ability? How can it identify an unhealthy cell or detect abnormalities? How does an AI model handle different types of data? The answer lies in the fact that AI is pretrained with millions of data samples! We’ve already mentioned training data in the earlier sections, but now, let’s understand how AI’s training data is created and works.

Training data is a set of labeled medical data used to teach AI models how to recognize patterns and make decisions. If AI can accomplish anything, it is because it has been pretrained on similar data to what it will encounter in future applications. This data can include X-rays, CT scan images, MRI scans, body sounds (such as coughing or breathing), and clinical notes, just as human medical professionals use these resources to identify diseases or abnormalities. Let’s discuss types of medical data labeling and how this labeled data is used to train AI.

Image Labeling and Medical Image Analysis

Medical images of the lungs include chest X-rays, MRIs, CT scans, etc. In these images, medical data labelers identify and label abnormal spots, lung nodules, tissue growth, and other markers that could indicate diseases. Bounding boxes, semantic segmentation, and polygon are some of the most commonly used image labeling techniques.

AI uses machine learning (ML) models, specifically Convolutional Neural Networks (CNNs), which are highly effective at analyzing medical images. These models are trained on accurately labeled data and can recognize similar patterns associated with diseases and lung conditions in future images. For example, the AI prediction model mentioned in the previous section was trained on nearly 47,000 CT images. These models can also analyze 3D and high-resolution images from CT scans.

Text Labeling and Natural Language Processing

Medical text labeling refers to the process of tagging text data from clinical reports, research papers, or any other medical-related documentation. This enables AI to analyze specific information from every medical text document.

AI uses Natural Language Processing (NLP) to interpret information from large amounts of medical text data. NLP algorithms analyze and extract textual information from medical records, such as patient history, radiology reports, and medication descriptions. For example, if a patient’s record states “60-year-old male, smoker for 20 years, chronic cough,” AI can flag him as high risk for lung cancer and COPD. NLP algorithms can also automatically extract findings from clinical reports, alerting physicians to possible lung abnormalities that need further investigation.

Audio Data Labeling and audio analysis

Audio data labeling involves assigning labels to specific audio signals such as coughs, breathing sounds, or other respiratory sounds. Every sound produced by our body, like coughing, breathing, or speaking, can provide important insights into lung health.

Recently, researchers from Google developed an AI model capable of analyzing human sounds to detect early signs of diseases. Nearly 300 million audio samples were used to train this model. For instance, while labeling, a wheezing sound can be labeled as “asthma.” Then, in the future, when the AI model detects a wheezing sound, it can identify it as asthma. This is the basic principle behind audio data labeling for lung-related AI.

Who Creates the Training Data?

Medical data labeling is crucial for creating training data for AI models, but who performs this task? AI development companies can either handle the labeling internally or outsource it to specialized medical data labeling companies. There are three primary approaches to labeling: automated labeling, manual labeling, and hybrid labeling (a combination of automation and manual labeling).

However, for medical data labeling, outsourcing with manual data labeling is often preferred. Medical data is highly sensitive and confidential, requiring strong privacy and security measures. Additionally, medical data labeling demands deep domain knowledge and guidance from medical professionals due to its complexity. Outsourcing allows AI developers to focus on their core work while trusting that the labeling process is handled securely and professionally.

Summing Up

Lungs, the reason for our breath, must be protected throughout our lives. While AI has the ability to support lung health, it all works best when combined with proper medical consultation. AI can assist in diagnosing and treating lung conditions, but it’s essential for us to avoid harmful habits like smoking, and have regular medical checkups. If any disease or abnormality is detected, take the correct treatment and adopt a healthy lifestyle.

Medrays, as a medical data labeling company, are proud to be part of many healthcare revolutions involving AI. Our team of experienced medical data labelers, along with the support of qualified and experienced medical professionals, provides high-quality datasets. We have contributed to many successful AI projects in the healthcare industry, and we’re committed to continuing this in the future too.