Department of Anesthesiology, First Affiliated Hospital of Jiamusi University, Jiamusi 154003, Heilongjiang, China.
Department of Anesthesiology, Jiansanjiang Hospital of Beidahuang Group, Jiamusi 156399, Heilongjiang, China.
Contrast Media Mol Imaging. 2022 Feb 25;2022:1732915. doi: 10.1155/2022/1732915. eCollection 2022.
This study was aimed at exploring the efficacy of morphine combined with mechanical ventilation in the treatment of heart failure with artificial intelligence algorithms. The cardiac magnetic resonance imaging (MRI) under the watershed segmentation algorithm was proposed, and the local grayscale clustering watershed (LGCW) model was designed in this study. A total of 136 patients with acute left heart failure were taken as the research objects and randomly divided into the control group (conventional treatment) and the experimental group (morphine combined with mechanical ventilation), with 68 cases in each group. The left ventricular end-diastolic diameter (LVEDD), left ventricular end-systolic diameter (LVESD), left ventricular ejection fraction (LVEF), N-terminal pro-brain natriuretic peptide (NT-proBNP), arterial partial pressure of oxygen (PaO), and arterial partial pressure of carbon dioxide (PaCO) were observed. The results showed that the mean absolute deviation (MAD) and maximum mean absolute deviation (max-MAD) of the LGCW model were lower than those of the fuzzy k-nearest neighbor (FKNN) algorithm and local gray-scale clustering model (LGSCm). The Dice metric was also significantly higher than that of other algorithms with statistically significant differences ( < 0.05). After treatment, LVEDD, LVESD, and NT-proBNP of patients in the experimental group were significantly lower than those in the control group, and LVEF in the experimental group was higher than that in the control group ( < 0.05). PaO of patients in the experimental group was also significantly higher than that in the control group ( < 0.05). It suggested that the LGCW model had a better segmentation effect, and morphine combined with mechanical ventilation gave a better clinical efficacy in the treatment of acute left heart failure, improving the patients' cardiac function and arterial blood gas effectively.
本研究旨在探讨人工智能算法辅助下吗啡联合机械通气治疗心力衰竭的疗效。提出了基于分水岭分割算法的心脏磁共振成像（MRI），并在本研究中设计了局部灰度聚类分水岭（LGCW）模型。共纳入 136 例急性左心衰竭患者作为研究对象，随机分为对照组（常规治疗）和实验组（吗啡联合机械通气），每组 68 例。观察左心室舒张末期直径（LVEDD）、左心室收缩末期直径（LVESD）、左心室射血分数（LVEF）、N 末端脑利钠肽前体（NT-proBNP）、动脉血氧分压（PaO）和动脉血二氧化碳分压（PaCO）。结果显示，LGCW 模型的平均绝对偏差（MAD）和最大平均绝对偏差（max-MAD）均低于模糊 k-最近邻（FKNN）算法和局部灰度聚类模型（LGSCm）。Dice 度量值也明显高于其他算法，差异具有统计学意义（<0.05）。治疗后，实验组患者的 LVEDD、LVESD 和 NT-proBNP 明显低于对照组，实验组患者的 LVEF 高于对照组（<0.05）。实验组患者的 PaO 也明显高于对照组（<0.05）。提示 LGCW 模型具有更好的分割效果，吗啡联合机械通气治疗急性左心衰竭的临床疗效更好，能有效改善患者心功能和动脉血气。
