Miida Suguru, Kamimura Hiroteru, Fujiki Shinya, Kobayashi Taichi, Endo Saori, Maruyama Hiroki, Yoshida Tomoaki, Watanabe Yusuke, Kimura Naruhiro, Abe Hiroyuki, Sakamaki Akira, Yokoo Takeshi, Tsukada Masanori, Numano Fujito, Kashimura Takeshi, Inomata Takayuki, Fuzawa Yuma, Hirata Tetsuhiro, Horii Yosuke, Ishikawa Hiroyuki, Nonaka Hirofumi, Kamimura Kenya, Terai Shuji
Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata 951-8520, Japan.
Department of Cardiovascular Medicine, Niigata University Medical and Dental Hospital, Niigata 951-8510, Japan.
World J Gastroenterol. 2025 Sep 14;31(34):108807. doi: 10.3748/wjg.v31.i34.108807.
Congestive hepatopathy, also known as nutmeg liver, is liver damage secondary to chronic heart failure (HF). Its morphological characteristics in terms of medical imaging are not defined and remain unclear.
To leverage machine learning to capture imaging features of congestive hepatopathy using incidentally acquired computed tomography (CT) scans.
We retrospectively analyzed 179 chronic HF patients who underwent echocardiography and CT within one year. Right HF severity was classified into three grades. Liver CT images at the paraumbilical vein level were used to develop a ResNet-based machine learning model to predict tricuspid regurgitation (TR) severity. Model accuracy was compared with that of six gastroenterology and four radiology experts.
In the included patients, 120 were male (mean age: 73.1 ± 14.4 years). The accuracy of the results predicting TR severity from a single CT image for the machine learning model was significantly higher than the average accuracy of the experts. The model was found to be exceptionally reliable for predicting severe TR.
Deep learning models, particularly those using ResNet architectures, can help identify morphological changes associated with TR severity, aiding in early liver dysfunction detection in patients with HF, thereby improving outcomes.
充血性肝病，也称为槟榔肝，是继发于慢性心力衰竭（HF）的肝损伤。其在医学成像方面的形态学特征尚未明确界定且仍不清楚。
利用机器学习，通过偶然获得的计算机断层扫描（CT）扫描来捕捉充血性肝病的成像特征。
我们回顾性分析了179例在一年内接受超声心动图和CT检查的慢性HF患者。右心衰竭严重程度分为三个等级。使用脐旁静脉水平的肝脏CT图像开发基于ResNet的机器学习模型，以预测三尖瓣反流（TR）的严重程度。将模型准确性与六位胃肠病学专家和四位放射学专家的准确性进行比较。
纳入的患者中，120例为男性（平均年龄：73.1±14.4岁）。机器学习模型从单个CT图像预测TR严重程度的结果准确性显著高于专家的平均准确性。发现该模型在预测严重TR方面异常可靠。
深度学习模型，特别是那些使用ResNet架构的模型，可以帮助识别与TR严重程度相关的形态学变化，有助于早期检测HF患者的肝功能障碍，从而改善预后。
