Evaluating artificial intelligence for a focal nodular hyperplasia diagnosis using magnetic resonance imaging: preliminary findings.
Authors
Affiliations (9)
Affiliations (9)
- Erzincan Binali Yıldırım University Faculty of Medicine, Department of Radiology, Erzincan, Türkiye.
- Atatürk University Faculty of Medicine, Department of Radiology, Erzurum, Türkiye.
- Digital Transformation Office, Presidency of the Republic of Türkiye, Ankara, Türkiye.
- Erzincan Binali Yıldırım University Faculty of Medicine, Department of Anatomy, Erzincan, Türkiye.
- Gazi University Faculty of Medicine, Department of Radiology, Ankara, Türkiye.
- Ege University Faculty of Medicine, Department of Radiology, İzmir, Türkiye.
- İnönü University Faculty of Medicine, Department of Radiology, Malatya, Türkiye.
- Akdeniz University Faculty of Medicine, Department of General Surgery, Antalya, Türkiye.
- Akdeniz University Faculty of Medicine, Department of Pathology, Antalya, Türkiye.
Abstract
This study aimed to evaluate the effectiveness of artificial intelligence (AI) in diagnosing focal nodular hyperplasia (FNH) of the liver using magnetic resonance imaging (MRI) and compare its performance with that of radiologists. 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. 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 specificity 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 (κ) value of 0.777. 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 diagnose each liver lesion with high accuracy in the future. AI is studied to provide assisted or automated interpretation of radiological images with an accurate and reproducible imaging diagnosis.