Man versus Machine in Mesothelioma Diagnostics: Can AI Help?

ByHolly Zink
Man versus Machine in Mesothelioma Diagnostics: Can AI Help?

Diagnosing lung diseases can be hard because they have many causes and symptoms. Artificial Intelligence (AI) may enable faster and more accurate diagnosis. AI could also play a significant role in improving outcomes for mesothelioma patients and other lung diseases.

Using Artificial Intelligence (AI)

One common lung problem is pleural effusion. This can make breathing tough and cause coughing and tiredness. Doctors usually use a method called pleural fluid aspiration to diagnose pleural effusion.

But this method is not great at finding cancer and other diseases. Doctors often need more tests like CT scans and pleural biopsies, which can be uncomfortable and take time.

Researchers are looking at using AI to help diagnose pleural effusions. In a recent study, scientists made a machine learning model to sort pleural effusions into different types.

The study looked at over 2,000 patients and 49 different factors. It found that the AI model could sort pleural effusions accurately. Even when the model only used 18 features, it still worked well. This means AI could be useful for doctors to diagnose pleural effusions and mesothelioma.

Enhancing Mesothelioma Diagnosis

Mesothelioma, caused by exposure to asbestos, is a rare but aggressive cancer that affects the lining of the lungs. Early detection is crucial for improving outcomes in mesothelioma patients. AI has the potential to enhance early detection by assisting in the accurate classification of pleural effusions, which can be a symptom of mesothelioma.

AI could change how we diagnose pleural diseases. By helping doctors diagnose faster and more accurately, AI could improve patient care and make healthcare more efficient. As researchers keep improving AI models, they could become important tools in fighting pleural diseases.

Source:

Addala, Dinesh N., and Najib M. Rahman. “Man versus Machine in Pleural Diagnostics: Does Artificial Intelligence Provide the Solution?” Annals of the American Thoracic Society 21, no. 2 (February 2024): 202–3. https://doi.org/10.1513/AnnalsATS.202311-960ED.

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