Masuda Yuto, Takeshita Ryo, Tsujimoto Akiko, Sahashi Yuki, Watanabe Takatomo, Fukuoka Daisuke, Hara Takeshi, Kanamori Hiromitsu, Okura Hiroyuki
Department of Cardiology, Gifu University Gradual School of Medicine, Gifu, Japan.
Graduate School of Natural Science and Technology, Gifu University, Gifu, Japan.
Int J Cardiol. 2025 Dec 15;441:133744. doi: 10.1016/j.ijcard.2025.133744. Epub 2025 Aug 8.
Intravascular ultrasound (IVUS)-based tissue characterization has been used to detect vulnerable plaque or lipid-rich plaque (LRP). Recently, advancements in artificial intelligence (AI) technology have enabled automated coronary arterial plaque segmentation and tissue characterization. The purpose of this study was to evaluate the feasibility and diagnostic accuracy of a deep learning model for plaque segmentation, tissue characterization and identification of LRP.
A total of 1,098 IVUS images from 67 patients who underwent IVUS-guided percutaneous coronary intervention were selected for the training group, while 1,100 IVUS images from 100 vessels (88 patients) were used for the validation group. A 7-layer U-Net ++ was applied for automated coronary artery segmentation and tissue characterization. Segmentation and quantification of the external elastic membrane (EEM), lumen and guidewire artifact were performed and compared with manual measurements. Plaque tissue characterization was conducted using integrated backscatter (IB)-IVUS as the gold standard. LRP was defined as %lipid area of ≥65 %.
The deep learning model accurately segmented EEM and lumen. AI-predicted %lipid area (R = 0.90, P < 0.001), % fibrosis area (R = 0.89, P < 0.001), %dense fibrosis area (R = 0.81, P < 0.001) and % calcification area (R = 0.89, P < 0.001), showed strong correlation with IB-IVUS measurements. The model predicted LRP with a sensitivity of 62 %, specificity of 94 %, positive predictive value of 69 %, negative predictive value of 92 % and an area under the receiver operating characteristic curve of 0.919 (95 % CI:0.902-0.934), respectively.
The deep-learning model demonstrated accurate automatic segmentation and tissue characterization of human coronary arteries, showing promise for identifying LRP.
基于血管内超声（IVUS）的组织特征分析已被用于检测易损斑块或富含脂质斑块（LRP）。近年来，人工智能（AI）技术的进步使得冠状动脉斑块的自动分割和组织特征分析成为可能。本研究的目的是评估深度学习模型在斑块分割、组织特征分析和LRP识别方面的可行性和诊断准确性。
选取67例行IVUS引导下经皮冠状动脉介入治疗患者的1098幅IVUS图像作为训练组，100例患者（88例）的1100幅IVUS图像作为验证组。采用7层U-Net ++进行冠状动脉自动分割和组织特征分析。对外部弹性膜（EEM）、管腔和导丝伪影进行分割和量化，并与手动测量结果进行比较。以背向散射积分（IB）-IVUS作为金标准进行斑块组织特征分析。LRP定义为脂质面积百分比≥65%。
深度学习模型准确分割了EEM和管腔。人工智能预测的脂质面积百分比（R = 0.90，P < 0.001）、纤维化面积百分比（R = 0.89，P < 0.001）、致密纤维化面积百分比（R = 0.81，P < 0.001）和钙化面积百分比（R = 0.89，P < 0.001）与IB-IVUS测量结果显示出强烈的相关性。该模型预测LRP的敏感性为62%，特异性为94%，阳性预测值为69%，阴性预测值为92%，受试者操作特征曲线下面积为0.919（95%CI：0.902 - 0.934）。
深度学习模型在人体冠状动脉自动分割和组织特征分析方面表现准确，在识别LRP方面显示出前景。
