Objective. The study aimed to explore the efficacy of pulmonary surfactant (PS) combined with Mucosolvan in the diagnosis of meconium aspiration syndrome (MAS) in newborns through ultrasonic images of lung based on machine learning. Methods. 138 cases of infants with MAS were selected as the research subjects and randomly divided into PS group (n = 46), Mucosolvan group (n = 46), and combination group (n = 46). Then, ultrasonic images based on machine learning algorithm were used for examination. On the basis of conventional treatment, the PS group accepted intratracheal PS drip treatment with 100 mg/kg. For the Mucosolvan group, 7.5 mg/kg of Mucosolvan was added with 50 g/L glucose, which was diluted to 3 mL. Then, the mixture was injected intravenously with a micropump for more than 5 min. The combination group received combined treatment of PS and Mucosolvan. If there was no relief or the symptoms aggravated after 12 h of PS treatment, the patient should be treated again. 7.5 mg/kg/d of Mucosolvan was given for 7 days. Mechanical ventilation time, hospitalization time, oxygenation index (OI) before treatment, at 3 d and at 7 d after treatment, and arterial/alveolar oxygen ratio (a/APO₂) of the three groups were detected and compared. Besides, in-hospital mortality and complication rate of the three groups were statistically compared. Results. Ultrasonic image edge detection based on machine learning algorithm was more condensed and better than Sobel operator. Compared with the PS group and the Mucosolvan group, treatment efficiency, OI at 3 d and at 7 d after treatment, and a/APO₂ of combination group were increased. Mechanical ventilation time and hospitalization time of the combination group were shortened, and mortality rate of the combination group was reduced ( < 0.05). Compared with the situation before treatment, OI at 3 d and at 7 d after treatment and a/APO₂ of the combination group were increased, and OI at 7 d after treatment and a/APO₂ of the PS group and the Mucosolvan group were increased ( < 0.05). Curative effect, mechanical ventilation time, hospitalization time, OI before and after treatment, a/APO₂, and mortality rate during hospitalization of the PS group and the Mucosolvan group had no significant difference ( > 0.05). There was no significant difference in the complications rates in the three groups ( > 0.05). Conclusion. Pulmonary ultrasound based on machine learning algorithm can be used in the diagnosis of MAS in neonates. PS combined with Mucosolvan is feasible and safe in treating neonatal MAS and effectively improves the pulmonary oxygenation function. Therefore, it is worthy of clinical application.

中文翻译：

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基于机器学习算法的超声图像分析肺表面活性剂联合溶栓治疗新生儿胎粪吸入综合征的疗效

目标。本研究旨在通过基于机器学习的肺部超声图像，探讨肺表面活性物质（PS）联合 Mucosolvan 在新生儿胎粪吸入综合征（MAS）诊断中的疗效。方法。选取138例MAS婴儿作为研究对象，随机分为PS组（n  = 46）、Mucosolvan组（n  = 46）和联合组（n = 46）。然后，使用基于机器学习算法的超声图像进行检查。PS组在常规治疗的基础上接受100 mg/kg的气管内PS滴注治疗。对于 Mucosolvan 组，添加 7.5 mg/kg Mucosolvan 和 50 g/L 葡萄糖，将其稀释至 3 mL。然后，将混合物用微型泵静脉内注射超过 5 分钟。联合组接受PS和Mucosolvan联合治疗。如果PS治疗12小时后仍未缓解或症状加重，则应再次治疗。7.5 mg/kg/d 的 Mucosolvan 给药 7 天。机械通气时间、住院时间、治疗前、治疗后3 d、7 d氧合指数（OI）、动脉/肺泡氧比（a/APO ₂)对三组进行检测和比较。比较三组的住院死亡率和并发症发生率。结果。基于机器学习算法的超声图像边缘检测比Sobel算子更简洁、更优。与PS组和Mucosolvan组相比，联合组的治疗有效率、治疗后3 d和7 d的OI和a/APO ₂增加。联合组机械通气时间和住院时间缩短，联合组死亡率降低（ <  0.05）。与治疗前相比，治疗后3 d和7 d的OI和a/APO ₂联合组的OI升高，PS组和Mucosolvan组治疗后7 d的OI和a/APO ₂升高（ <  0.05）。PS组与Mucosolvan组的疗效、机械通气时间、住院时间、治疗前后OI、a/APO ₂、住院死亡率等均无显着差异（ >  0.05）。三组并发症发生率无显着差异（ >  0.05）。结论. 基于机器学习算法的肺部超声可用于新生儿 MAS 的诊断。PS联合Mucosolvan治疗新生儿MAS安全可行，有效改善肺氧合功能。因此，值得临床应用。