摘要目的:建立超声多切面融合识别模型并评估该模型诊断胎儿圆锥动脉干畸形（conotruncal defect，CTD）的临床价值。方法:前瞻性收集2022年9月至2024年5月在东莞市妇幼保健院行产前超声检查的孕20～32周胎儿的心脏超声图像，其中诊断为CTD的胎儿为病例组，同期按1∶2收集心脏结构正常胎儿作为对照组。2组各自以3∶1分为建模训练集与验证集。每例胎儿的四腔心切面、左心室流出道切面、右心室流出道切面和三血管气管切面各切面取一幅最优质的标准切面图像纳入研究，通过深度学习算法建立胎儿心脏超声多切面融合识别模型判断正常圆锥动脉干和CTD，判断结果与引产后病理或生后超声心动图诊断结果对比，验证模型诊断胎儿CTD的灵敏度与特异度。采用SAS软件进行统计分析，计算多切面融合识别模型中3种融合模型（建立任意2、3和4个切面阳性的3种融合识别模型分别作为融合模型一、融合模型二和融合模型三），在CTD胎儿诊断中的灵敏度和特异度，并依据最大约登指数确定最优模型。同时请高、中、低年资产前超声检查医师采用盲法分别对验证集病例进行诊断，与最优模型诊断作对比，采用配对设计的 χ2检验（Cochran's Q检验）比较高、中、低年资医师诊断正确率与最优模型诊断灵敏度之间的差异，分析多切面融合识别模型的辅助诊断价值。 结果:研究纳入病例组CTD共88例，剔除6例（引产后病理或生后超声心动图诊断为非CTD和超声图像质量不佳病例），分为建模训练集60例，验证集22例（包括12例法洛四联症、4例右心室双出口、3例大动脉转位和3例永存动脉干）；对照组176例，剔除15例（新生儿期证实合并其他心脏结构异常及复核后图像质量不佳），分为建模训练集120例，验证集41例。3个融合识别模型的灵敏度分别为0.86、0.64和0.27，特异度分别为0.76、0.95和1.00。融合模型一约登指数最大（0.62），为最优模型；融合模型一的诊断灵敏度与高年资医师诊断正确率［91%（20/22）］差异无统计学意义（Bonferroni校正， P>0.999），但高于中、低年资医师诊断正确率［分别为55%（12/22）和32%（7/22）］（Bonferroni校正， P值分别为0.049和0.003）。 结论:应用超声多切面融合的深度学习识别模型能够获得与高年资医师相仿的诊断效能，在CTD的辅助诊断和培训中、低年资医师及科研教学方面可能具有重要的应用前景。

abstractsObjective:To develop an ultrasound multi-view fusion recognition model and evaluate its clinical value in diagnosing fetal conotruncal defects (CTD).Methods:This prospective study collected cardiac ultrasound images from fetuses at 20-32 weeks of gestation undergoing prenatal ultrasound at Dongguan Maternal and Child Health Hospital between September 2022 and May 2024. The case group comprised fetuses diagnosed with CTD, while controls with normal cardiac structures were collected at a 1∶2 ratio. Both groups were divided into modeling training and validation sets at a 3∶1 ratio. One optimal standard image each from the four-chamber view, left ventricular outflow tract view, right ventricular outflow tract view, and three vessels and trachea view was included per fetus. A deep learning-based multi-view fusion recognition model was developed to differentiate normal conotruncal anatomy from CTD. Model performance was validated against post-abortion pathology or postnatal echocardiography results. SAS software was used for statistical analysis to calculate the sensitivity and specificity of three fusion models (based on positivity in any two, three, or four views, and were designated as Fusion Model 1, Fusion Model 2, and Fusion Model 3, respectively), with the optimal model determined by the maximum Youden index. Senior, intermediate, and junior prenatal sonologists independently diagnosed cases in the validation set under blinding conditions. Their diagnostic results were compared with those of the optimal model. Paired Chi-square test (Cochran's Q test) was employed to compare the differences between the diagnostic accuracy rates of sonologists at different experience levels and the sensitivity of the optimal model, thereby analyzing the auxiliary diagnostic value of the multi-view fusion recognition model. Results:The study included 88 CTD cases, excluding six cases (non-CTD diagnosed by post-abortion pathology or postnatal echocardiography or poor image quality), divided into 60 training and 22 validation cases (12 tetralogy of Fallot, four double outlet right ventricle, three transposition of great arteries, three persistent truncus arteriosus). The control group included 176 cases, excluding 15 cases (other cardiac abnormalities confirmed postnatally or poor image quality after re-evaluation), divided into 120 training and 41 validation cases. The sensitivities of Fusion Model 1, Fusion Model 2, and Fusion Mudel 3 were 0.86, 0.64, and 0.27, while their specificities were 0.76, 0.95, and 1.00, respectively. Fusion Model 1 demonstrated the highest Youden index (0.62) and was selected as optimal. Its diagnostic sensitivity showed no significant difference from senior sonologists [86% vs. 91% (20/22), Bonferroni-corrected P>0.999], but was significantly higher than intermediate [55% (12/22), Bonferroni-corrected P=0.049] and junior sonologists [32% (7/22), Bonferroni-corrected P=0.003]. Conclusion:The deep learning multi-view fusion model achieved diagnostic performance comparable to senior sonologists, demonstrating potential value in assisting CTD diagnosis, training less experienced sonologists, and supporting research and education.