Development of a clinical decision support system for the diagnosis of space-occupying liver lesions using artificial intelligence methods

Abstract

Aim. To develop an artificial intelligence-based system for the diagnosis of focal liver lesions aimed at supporting clinical decision-making in surgical hepatology. Materials and methods. An artificial intelligence-based technological service was developed for the automatic segmentation and classification of contrast-enhanced computed tomography (CT) images of four types of liver lesions: focal nodular hyperplasia, carcinoma, hemangioma, and simple cyst. The service was trained and tested on datasets comprising 725 CT images using the nnU-Net architecture. Diagnostic performance was evaluated by calculating the AUC-ROC, sensitivity, specificity, and accuracy. Results. The service achieved high performance metrics. The AUC-ROC ranged from 0.847 to 0.928, with a maximum sensitivity of 0.940 for carcinoma and a specificity of 0.900 for focal nodular hyperplasia. Accuracy ranged from 0.883 to 0.922, which demonstrates the algorithm's ability to clearly differentiate between malignant and benign lesions. Conclusion. The machine learning-based service demonstrated high diagnostic performance and shows promise for integration into clinical practice, offering improved detection and classification of liver lesions.

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