Assisted Reproduction Unit, Third Department of Obstetrics and Gynecology, Medical School, "Attikon" University Hospital, National and Kapodistrian University of Athens, 1 Rimini Street, Chaidari, 12642, Athens, Greece.
Second Department of Pathology, Medical School, "Attikon" University Hospital, National and Kapodistrian University of Athens, 1 Rimini Street, Chaidari, 12642, Athens, Greece.
J Assist Reprod Genet. 2019 Jul;36(7):1441-1448. doi: 10.1007/s10815-019-01498-7. Epub 2019 Jun 19.
To construct and validate an efficient artificial neural network (ANN) based on parameters with statistical correlation to live birth, to be used as a comprehensive tool for the prediction of the clinical outcome for patients undergoing ART.
Data from 257 infertile couples that underwent a total of 426 IVF/ICSI cycles from 2010 to 2017 was collected on an ensemble of 118 parameters for each cycle. Statistical correlation of the parameters with the outcome of live birth was performed, using either t test or χ test, and the parameters that demonstrated statistical significance were used to construct the ANN. Cross-validation was performed by random separation of data and repeating the training-testing procedure by 10 times.
12 statistically significant parameters out of the initial ensemble were used for the ANN construction, which exhibited a cumulative sensitivity and specificity of 76.7% and 73.4%, respectively. During cross-validation, the system exhibited the following: sensitivity 69.2% ± 2.36%, specificity 69.19% ± 2.8% (OR 5.21 ± 1.27), PPV 36.96 ± 3.44, NPV 89.61 ± 1.09, and OA 69.19% ± 2.69%. A rather small standard deviation in the performance indices between the training and test sets throughout the validation process indicated a stable performance of the constructed ANN.
The constructed ANN is based on statistically significant variables with the outcome of live birth and represents a stable and efficient system with increased performance indices. Validation of the system allowed an insight of its clinical value as a supportive tool in medical decisions, and overall provides a reliable approach in the routine practice of IVF units in a user-friendly environment.
构建并验证一个基于与活产具有统计学相关性的参数的高效人工神经网络（ANN），作为预测接受辅助生殖技术（ART）治疗患者临床结局的综合工具。
收集了 2010 年至 2017 年期间 257 对不孕夫妇共 426 个 IVF/ICSI 周期的数据，每个周期有 118 个参数的集合。使用 t 检验或 χ 检验对参数与活产结局的相关性进行统计学分析，选择具有统计学意义的参数构建 ANN。通过数据随机分离，重复 10 次训练-测试过程进行交叉验证。
从初始集合中选择了 12 个具有统计学意义的参数用于 ANN 构建，其累积灵敏度和特异性分别为 76.7%和 73.4%。在交叉验证中，该系统表现如下：灵敏度 69.2%±2.36%，特异性 69.19%±2.8%（OR 5.21±1.27），PPV 36.96±3.44，NPV 89.61±1.09，OA 69.19%±2.69%。在整个验证过程中，训练集和测试集之间的性能指标标准差较小，表明构建的 ANN 性能稳定。
所构建的 ANN 基于与活产结局具有统计学意义的变量，是一个具有较高性能指标的稳定有效的系统。该系统的验证使其成为辅助医疗决策的有价值工具，为 IVF 单位的常规实践提供了可靠的方法，且易于用户使用。
