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.

1. Enhanced Detection with AI: A study by Armato et al. demonstrated the use of AI in analyzing computed tomography data, which significantly improves the early detection of lung cancer by precisely identifying small pulmonary nodules [4].‍
2. Risk Assessment Algorithm: Tammemagi et al. developed an AI algorithm that assesses lung cancer risk based on critical factors such as age, smoking history, and family cancer history. This algorithm optimizes screening recommendations, offering a more personalized and efficient screening protocol [1].‍
3. Pattern Analysis for Prevention: Jacobs et al. implemented AI algorithms to analyze patterns in periodic examinations of smokers, identifying elevated lung cancer risks with 85% accuracy. This allows for more targeted preventive actions [2].‍
4. Medical Image Interpretation Enhancement: Setio et al. showcased how AI could significantly improve the interpretation of medical images to detect early signs of lung cancer with up to 90% accuracy [3].‍
5. Predictive AI Solution for Lung Cancer Detection: We have developed a predictive AI solution trained on a database containing medical risk factors for lung cancer. Utilizing predictive models and AI technologies, this solution facilitates the early detection of lung cancer, enabling earlier and potentially life-saving interventions.

• 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.