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    Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings
    (Turkish Soc Radiology, 2025) Kantarci, Mecit; Kizilgoz, Volkan; Terzi, Ramazan; Kilic, Ahmet Enes; Kabalci, Halime; Durmaz, Onder; Tokgoz, Nil
    PURPOSE This study aimed to evaluate the effectiveness of artificial intelligence (AI) in diagnosing focal nod-ular hyperplasia (FNH) of the liver using magnetic resonance imaging (MRI) and compare its perfor-mance with that of radiologists. METHODS In the first phase of the study, the MRIs of 60 patients (30 patients with FNH and 30 patients with no lesions or lesions other than FNH) were processed using a segmentation program and introduced to an AI model. After the learning process, the MRIs of 42 different patients that the AI model had no experience with were introduced to the system. In addition, a radiology resident and a radiology specialist evaluated patients with the same MR sequences. The sensitivity and specificity values were obtained from all three reviews. RESULTS The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the AI model were found to be 0.769, 0.966, 0.909, and 0.903, respectively. The sensitivity and speci-ficity values were higher than those of the radiology resident and lower than those of the radiology specialist. The results of the specialist versus the AI model revealed a good agreement level, with a kappa (kappa) value of 0.777. CONCLUSION For the diagnosis of FNH, the sensitivity, specificity, PPV, and NPV of the AI device were higher than those of the radiology resident and lower than those of the radiology specialist. With additional studies focused on different specific lesions of the liver, AI models are expected to be able to diag-nose each liver lesion with high accuracy in the future. CLINICAL SIGNIFICANCE AI is studied to provide assisted or automated interpretation of radiological images with an accu-rate and reproducible imaging diagnosis.