Introduction: Artificial intelligence has made great progress and is increasingly being used in medical diagnostics. It plays an important role in the analysis of medical images. In chest CT, early detection of lung nodules is crucial, as timely diagnosis can significantly improve the outcome of lung cancer treatment. The integration of Artificial intelligence into this process can enhance accuracy in nodule identification, reduce image processing time, and lighten the workload for medical staff. Purpose: The main objective of this thesis is to explore the possibilities of using Artificial intelligence in the analysis of chest CT images, particularly in the automatic detection of lung nodules and lung segmentation. We focus on the open-source tools Monai Label and 3D Slicer, which enable the development and testing of Artificial intelligence models for medical image analysis. Additionally, we examine the practical challenges in using these tools and consider the possibilities of their implementation in clinical practice. Methods: First, we conducted a review of the existing literature on Artificial intelligence in radiology, focusing on foreign databases. We then carried out a case study using publicly available CT images from the TCIA database. We tested the Artificial intelligence model Lung Nodule CT Detection, which was trained on the LUNA16 dataset. Using the 3D Slicer program and the Monai Label extension, we performed automatic detection of lung nodules and compared the results with publicly available radiologist assessments. Additionally, we used the Auto3DSeg algorithm to perform lung segmentation on the same cases. Results: In the results, we found that Artificial intelligence successfully identifies lung nodules, but in some cases, discrepancies were observed when compared to radiologist assessments. This supports the idea that Artificial intelligence systems can be useful in CT image analysis, but further improvements are needed before they can be incorporated into routine clinical diagnostics. Discussion and conclusion: The use of Artificial intelligence in medical image analysis opens up new possibilities for faster and more accurate diagnosis of lung diseases. Despite the positive results offered by open-source tools such as Monai Label and 3D Slicer, challenges remain, including false positive results and the need for additional verification by experts. The integration of Artificial intelligence into clinical practice also requires proper training of medical staff and adjustments to existing workflows.
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