Predict Lung Cancer

Lung cancer, one of the most lethal types worldwide, is primarily characterized by late diagnosis and its association with risk factors such as smoking and exposure to environmental carcinogens. This disease is notorious for its aggressiveness and the speed with which it can progress without obvious symptoms, often leading to discoveries at advanced stages where treatment options are limited and less effective [1].

Size of the Problem

• Approximately 2.21 million new cases of lung cancer were diagnosed in 2020 [1].
• The five-year survival rate is only 15% worldwide, highlighting the lethality of the disease [2].
• Over 85% of cases are directly related to tobacco use [3].

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Lung cancer complications represent a significant burden for both patients and health systems. Reducing the prevalence of this disease would not only save lives but also decrease the medical costs associated with treatment and long-term care. Early detection is essential to improve outcomes and reduce mortality rates.

The implementation of artificial intelligence (AI) in detecting and managing lung cancer is increasingly promising:

1. Study by Armato et al.: This study used AI to analyze computed tomography data, improving early lung cancer detection with notable precision in identifying small pulmonary nodules [4].
2. Study by Tammemagi et al.: They developed an algorithm to assess cancer risk based on several critical lung cancer risk factors, including age, smoking history, and family cancer history. The AI algorithm focused on these factors to optimize screening recommendations, resulting in a more personalized and efficient protocol [1].
3. Study by Jacobs et al.: They integrated AI algorithms to analyze patterns in periodic examinations of smokers, identifying elevated lung cancer risks with 85% accuracy, allowing for more targeted preventive actions [2].
4. Research by Setio et al.: They demonstrated that AI could enhance the interpretation of medical images to detect early signs of lung cancer with up to 90% accuracy [3].
5. Development of a Predictive AI Solution: We have trained a solution using a database containing medical risk factors for lung cancer. By using predictive models and artificial intelligence technologies, this solution allows for early detection of lung cancer, enabling earlier and potentially life-saving interventions.

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• Medical imaging data: Essential for training AI models on detecting pulmonary anomalies.
• Electronic medical records: Provide valuable information about the patient's medical history.
• Epidemiological studies: Offer data on the incidence and distribution of the disease.
• Genomic databases: Useful for identifying genetic markers associated with lung cancer risk.
• Environmental data: Crucial for investigating the impact of pollutants on cancer incidence.