Kantarcı Mecit, Kızılgöz Volkan, Terzi Ramazan, Kılıç Ahmet Enes, Kabalcı Halime, Durmaz Önder, Tokgöz Nil, Harman Mustafa, Sağır Kahraman Ayşegül, Avanaz Ali, Aydın Sonay, Elpek Gülsüm Özlem, Yazol Merve, Aydınlı Bülent
Erzincan Binali Yıldırım University Faculty of Medicine, Department of Radiology, Erzincan, Türkiye.
Atatürk University Faculty of Medicine, Department of Radiology, Erzurum, Türkiye.
Diagn Interv Radiol. 2025 Mar 26. doi: 10.4274/dir.2025.243095.
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.
本研究旨在评估人工智能（AI）利用磁共振成像（MRI）诊断肝脏局灶性结节性增生（FNH）的有效性，并将其性能与放射科医生的性能进行比较。
在研究的第一阶段，使用分割程序处理60例患者（30例FNH患者和30例无病变或有除FNH以外病变的患者）的MRI，并将其引入AI模型。在学习过程之后，将AI模型没有经验的42例不同患者的MRI引入该系统。此外，一名放射科住院医师和一名放射科专家对相同的MR序列患者进行评估。从所有三项评估中获得敏感性和特异性值。
发现AI模型的敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）分别为0.769、0.966、0.909和0.903。敏感性和特异性值高于放射科住院医师的，低于放射科专家的。专家与AI模型的结果显示出良好的一致性水平，kappa（κ）值为0.777。
对于FNH的诊断，AI设备的敏感性、特异性、PPV和NPV高于放射科住院医师的，低于放射科专家的。随着针对肝脏不同特定病变的进一步研究，预计AI模型未来能够高精度诊断每种肝脏病变。
研究AI以提供具有准确且可重复的影像诊断的放射影像辅助或自动解读。
