Clinical Value of AI-Assisted Transvaginal Color Ultrasound Combined with Serum Markers in Diagnosing Heterotopic Pregnancy after IVF-ET
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摘要:
目的 探讨AI辅助阴道彩超联合血清标志物对IVF-ET后宫内外复合妊娠（HP）的早期诊断价值。 方法 本研究为回顾性病例对照研究，纳入2022年1月至2024年12月期间在河北省沧州中西医结合医院接受IVF-ET治疗后确诊为HP的86例患者。对照组包括同期86例正常宫内妊娠和86例单纯异位妊娠患者。所有患者均通过临床诊断、影像学检查及手术确认诊断。采用AI超声影像分析模型和血清学指标（hCG、PAPP～A、APA、孕酮、PlGF）联合评估HP的诊断效能。通过计算灵敏度、特异度、阳性预测值和阴性预测值，比较各组的诊断性能。 结果 AI超声影像分析模型的AUC值为0.835，显著优于传统超声（AUC=0.696，P < 0.01）。血清hCG和PAPP～A分别为（ 36210 ±9820 ） IU/L和（0.81±0.25）μg/mL。联合诊断的AUC值为0.909，敏感性为94.2%，特异性为88.4%，阳性预测值为91.6%，阴性预测值为92.8%。结论 AI辅助超声联合血清学指标显著提高HP早期诊断准确性，优于单一方法，为临床提供了可靠工具。 Abstract:Objective To investigate the early diagnostic value of AI-assisted transvaginal ultrasound combined with serum biomarkers for intrauterine and ectopic combined pregnancy （heterotopic pregnancy, HP） after IVF-ET. Methods This retrospective case-control study included 86 patients diagnosed with HP after IVF-ET at Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine between January 2022 and December 2024. Control groups consisted of 86 patients with normal intrauterine pregnancies and 86 patients with simple ectopic pregnancy. All patients were diagnosed through clinical diagnosis, imaging examinations, and surgical confirmation. An AI ultrasound image analysis model and serum markers （hCG, PAPP-A, APA, progesterone, PlGF） were used to jointly assess HP diagnostic efficiency. Diagnostic performance was compared by calculating sensitivity, specificity, positive predictive value, and negative predictive value. Results The AI ultrasound image analysis model's AUC was 0.835, significantly superior to traditional ultrasound （AUC=0.696）. Serum hCG and PAPP-A levels were （36, 210±9, 820） IU/L and （0.81±0.25） μg/mL, respectively. The joint diagnostic AUC was 0.909, with a sensitivity of 94.2%, specificity of 88.4%, positive predictive value of 91.6%, and negative predictive value of 92.8%. Conclusion AI-assisted ultrasound combined with serological markers significantly improves the early diagnostic accuracy of HP, outperforming single methods and providing a reliable clinical tool. -
图 2 ROC曲线分析
A：单独超声的ROC曲线；B：单独血清学指标的ROC曲线；C：AI超声的ROC曲线；D：联合诊断的ROC曲线。
Figure 2. ROC curve analysis
表 1 患者基本特征[（$\bar x \pm s $）/n （%）]
Table 1. Basic characteristics of patients[（$\bar x \pm s $）/n （%）]
指标 HP组
（n=86）正常宫内
妊娠组（n=86）单纯异位
妊娠组（n=86）F/χ2 P 平均年龄（岁） 33.2 ± 4.1 32.8 ± 3.9 32.5 ± 4.0 0.75 0.474 平均孕周（周） 6.8 ± 1.2 6.9 ± 1.1 7.0 ± 1.0 0.5 0.607 阴道出血 71 (82.6) 13 (15.1) 53 (61.6) 95.2 <0.001* 下腹痛 56 (65.1) 16 (18.6) 63 (73.3) 65.8 <0.001* *P < 0.05。 
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表 2 阴道彩色多普勒超声检查特征[（$\bar x \pm s $）/n （%）]
Table 2. Features of transvaginal color doppler ultrasound examination[（$\bar x \pm s $）/n （%）]
超声特征 数值结果 标准差 （SD） 95%置信区间 （CI） 宫内妊娠孕囊平均直径 （mm） 18.6 ± 4.2 4.2 17.2 ～ 19.9 胎心搏动检出率（%） 76（88.4） - - 输卵管妊娠发生率（%） 64（74.4） - - 异位妊娠平均包块直径 （mm） 24.3 ± 6.1 6.1 22.2 ～ 26.4 “火环征”阳性率（%） 55（64.0） - - 高流量血流信号出现率（%） 50（58.1） - - 输卵管妊娠包块位于输卵管中段 44（52.3） - - 异位妊娠出现血管影像征 36（41.8） - -
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表 3 人工智能影像分析模型评估指标
Table 3. Evaluation metrics of artificial intelligence imaging analysis model
评估指标 评估指标数值 95% 置信区间 （CI） 准确率 （%） 92.4 90.1 ～94.7 敏感性（%） 94.2 92.5 ～ 95.9 特异性 (%) 88.4 85.6 ～ 91.2 F1-score 93.2 91.5 ～ 94.9 AUC 0.835 0.801 ～ 0.869
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表 4 血清hCG与PAPP-A检测结果[（$\bar x \pm s $）/n （%）]
Table 4. Serum hCG and PAPP-A test results[（$\bar x \pm s $）/n （%）]
指标 测量值 95% 置信区间 （CI） P 血清hCG （IU/L） 36210 ± 9820 28000 ～45000 0.001** 血清PAPP-A（μg/mL） 0.81 ± 0.25 0.68 ～ 0.94 0.003** 血清APA阳性率 （%） 45（52.3） 41.5 ～ 63.1 0.021* 血清孕酮 （ng/mL） 12.5 ± 4.2 10.1 ～ 14.9 0.008** 血清 PlGF （pg/mL） 22.6 ± 8.1 18.4 ～ 26.8 0.004** *P < 0.05；**P < 0.01。
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表 5 不同诊断方法的敏感性与特异性
Table 5. Sensitivity and specificity of different diagnostic methods
诊断方法 敏感性 （%） 特异性 （%） 阳性预测值 （%） 阴性预测值 （%） 传统超声单独诊断 81.4 73.2 79.4 74.2 AI超声单独诊断 84.5 76.7 84.3 80.1 血清学指标联合诊断 78.2 72.1 81.5 75.9 联合诊断（超声 + 血清学指标） 94.2 88.4 91.6 92.8
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