Artificial intelligence and deep learning have shown promising results in the early detection of tumors from radiological imaging and even in disease classification. AI refers to a machine's (or algorithm's) ability to perform tasks traditionally associated with human intelligence, such as learning. By using AI methods, we can develop learning algorithms that give a system designed for a specific application the ability to make observations, process data, and achieve specific goals.
In our research, CT-scan material from the Finnish Hepato-Pancreato-Biliary Cancers Cohort (FinHPB) is used to develop AI algorithms for the early detection and risk classification of pancreatic tumors. The FinHPB cohort includes all patients with liver, pancreatic, and biliary tract cancers who underwent surgery in Finland between 2000 and 2019, totaling 6,930 patients.
With the help of deep learning algorithms, we can train computer models to accurately detect tumors from CT-scan images and classify them based on their risk level. By improving the accuracy of tumor detection and risk classification, we hope to enable earlier interventions and improve patient outcomes.
Join us in our effort to harness the power of artificial intelligence and deep learning for medical applications. Contact us for more information on how to contribute to this exciting research field.
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