{"title":"应用神经网络数据分析预测临床上骨盆狭窄","authors":"A. M. Ziganshin, G. Dikke, V. Mudrov","doi":"10.17749/2313-7347/ob.gyn.rep.2023.382","DOIUrl":null,"url":null,"abstract":"Aim: to improve the efficiency of predicting a clinically narrow pelvis (СNP) using neural network data analysis and to evaluate its prognostic characteristics.Materials and Мethods. The study was designed as a retrospective non-randomized clinical trial. An analysis of 184 born neonates was carried out: group 1 included 135 female patients whose delivery occurred through the natural birth canal, group 2 – 49 patients whose delivery was complicated by СNP development and ended up with emergency caesarean section. Examination of patients was carried out on the eve of childbirth (1–2 days) and included anamnesis, general and special obstetric examination, including pelvimetry, a clinical assessment of cephalopelvic disproportion was carried out during childbirth. The condition of newborns was assessed using the Apgar scale, height and body weight were measured. Neural network analysis was performed using the built-in Neural Networks module of SPSS Statistics Version 25.0 (IBM, USA).Results. Despite hypothetically important role of anatomically narrowed pelvis in development of cephalopelvic disproportion, no significant inter-group differences were found. Significant parameters (abdominal circumference, uterine fundus height and woman’s weight, fetal head circumference, as well as data on the presence or absence of oligohydramnios and fetal macrosomia) were determined, which were included in the test database to create the basis for training the multilayer perceptron. Out of 135 patients of group 1, the prognosis was negative in 131 (97.0 %), positive in 4 (3.0 %); out of 49 patients in group 2, negative in 0 (0.0 %), positive in 49 (100.0 %). The forecast accuracy of the developed model was 98 % (sensitivity – 100 %, specificity –97 %). The information content of neural network data analysis in СNP predicting is presented in ROC analysis: area under the curve (AUC) = 0.99 (95 % confidence interval = 0.97–1.00). Neonatal anthropometric parameters were significantly higher in group 2 vs. group 1, and the Apgar score at 1 minute was correspondingly lower.Conclusion. The use of neural network analysis of clinical data obtained on the eve of childbirth allows to predict СNP development at sufficient degree of accuracy (98.0 %), which, in the future, after being introduced into clinical practice, will optimize a choice of delivery method in patients at risk (anatomically narrow pelvis, large fetus), reduce emergency caesarean sections and improve birth outcomes.","PeriodicalId":36521,"journal":{"name":"Obstetrics, Gynecology and Reproduction","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting a clinically narrow pelvis using neural network data analysis\",\"authors\":\"A. M. Ziganshin, G. Dikke, V. Mudrov\",\"doi\":\"10.17749/2313-7347/ob.gyn.rep.2023.382\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aim: to improve the efficiency of predicting a clinically narrow pelvis (СNP) using neural network data analysis and to evaluate its prognostic characteristics.Materials and Мethods. The study was designed as a retrospective non-randomized clinical trial. An analysis of 184 born neonates was carried out: group 1 included 135 female patients whose delivery occurred through the natural birth canal, group 2 – 49 patients whose delivery was complicated by СNP development and ended up with emergency caesarean section. Examination of patients was carried out on the eve of childbirth (1–2 days) and included anamnesis, general and special obstetric examination, including pelvimetry, a clinical assessment of cephalopelvic disproportion was carried out during childbirth. The condition of newborns was assessed using the Apgar scale, height and body weight were measured. Neural network analysis was performed using the built-in Neural Networks module of SPSS Statistics Version 25.0 (IBM, USA).Results. Despite hypothetically important role of anatomically narrowed pelvis in development of cephalopelvic disproportion, no significant inter-group differences were found. Significant parameters (abdominal circumference, uterine fundus height and woman’s weight, fetal head circumference, as well as data on the presence or absence of oligohydramnios and fetal macrosomia) were determined, which were included in the test database to create the basis for training the multilayer perceptron. Out of 135 patients of group 1, the prognosis was negative in 131 (97.0 %), positive in 4 (3.0 %); out of 49 patients in group 2, negative in 0 (0.0 %), positive in 49 (100.0 %). The forecast accuracy of the developed model was 98 % (sensitivity – 100 %, specificity –97 %). The information content of neural network data analysis in СNP predicting is presented in ROC analysis: area under the curve (AUC) = 0.99 (95 % confidence interval = 0.97–1.00). Neonatal anthropometric parameters were significantly higher in group 2 vs. group 1, and the Apgar score at 1 minute was correspondingly lower.Conclusion. The use of neural network analysis of clinical data obtained on the eve of childbirth allows to predict СNP development at sufficient degree of accuracy (98.0 %), which, in the future, after being introduced into clinical practice, will optimize a choice of delivery method in patients at risk (anatomically narrow pelvis, large fetus), reduce emergency caesarean sections and improve birth outcomes.\",\"PeriodicalId\":36521,\"journal\":{\"name\":\"Obstetrics, Gynecology and Reproduction\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Obstetrics, Gynecology and Reproduction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17749/2313-7347/ob.gyn.rep.2023.382\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Obstetrics, Gynecology and Reproduction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17749/2313-7347/ob.gyn.rep.2023.382","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
摘要
目的:提高应用神经网络数据分析预测临床上骨盆狭窄(СNP)的效率,并评价其预后特点。材料和Мethods。本研究设计为回顾性非随机临床试验。对184例新生儿进行分析:1组135例经自然产道分娩的女性患者,2组49例因СNP发育导致分娩并发症,最终采取紧急剖腹产。在分娩前夕(1-2天)对患者进行检查,包括记忆、一般和特殊产科检查,包括盆腔测量,分娩时进行头骨盆比例失调的临床评估。采用阿普加量表(Apgar scale)评估新生儿状况,测量身高、体重。使用SPSS Statistics Version 25.0 (IBM, USA)内置的神经网络模块进行神经网络分析。尽管假设解剖狭窄的骨盆在头骨盆失衡的发展中起重要作用,但没有发现显著的组间差异。确定重要参数(腹围、子宫底高度和女性体重、胎儿头围以及羊水过少和胎儿巨大症的存在与否),并将其纳入测试数据库,为多层感知器的训练奠定基础。1组135例患者中,预后阴性131例(97.0%),阳性4例(3.0%);2组49例患者中,阴性0例(0.0%),阳性49例(100.0%)。建立的模型预测准确率为98%(敏感性为100%,特异性为97%)。神经网络数据分析在СNP预测中的信息量用ROC分析表示:曲线下面积(AUC) = 0.99(95%置信区间= 0.97-1.00)。2组新生儿的人体测量参数明显高于1组,1分钟Apgar评分相应较低。使用神经网络对分娩前夕获得的临床数据进行分析,可以以足够的准确度(98.0%)预测СNP的发育,将来,在引入临床实践后,将优化高危患者(解剖上狭窄的骨盆,较大的胎儿)的分娩方法选择,减少紧急剖腹产并改善分娩结果。
Predicting a clinically narrow pelvis using neural network data analysis
Aim: to improve the efficiency of predicting a clinically narrow pelvis (СNP) using neural network data analysis and to evaluate its prognostic characteristics.Materials and Мethods. The study was designed as a retrospective non-randomized clinical trial. An analysis of 184 born neonates was carried out: group 1 included 135 female patients whose delivery occurred through the natural birth canal, group 2 – 49 patients whose delivery was complicated by СNP development and ended up with emergency caesarean section. Examination of patients was carried out on the eve of childbirth (1–2 days) and included anamnesis, general and special obstetric examination, including pelvimetry, a clinical assessment of cephalopelvic disproportion was carried out during childbirth. The condition of newborns was assessed using the Apgar scale, height and body weight were measured. Neural network analysis was performed using the built-in Neural Networks module of SPSS Statistics Version 25.0 (IBM, USA).Results. Despite hypothetically important role of anatomically narrowed pelvis in development of cephalopelvic disproportion, no significant inter-group differences were found. Significant parameters (abdominal circumference, uterine fundus height and woman’s weight, fetal head circumference, as well as data on the presence or absence of oligohydramnios and fetal macrosomia) were determined, which were included in the test database to create the basis for training the multilayer perceptron. Out of 135 patients of group 1, the prognosis was negative in 131 (97.0 %), positive in 4 (3.0 %); out of 49 patients in group 2, negative in 0 (0.0 %), positive in 49 (100.0 %). The forecast accuracy of the developed model was 98 % (sensitivity – 100 %, specificity –97 %). The information content of neural network data analysis in СNP predicting is presented in ROC analysis: area under the curve (AUC) = 0.99 (95 % confidence interval = 0.97–1.00). Neonatal anthropometric parameters were significantly higher in group 2 vs. group 1, and the Apgar score at 1 minute was correspondingly lower.Conclusion. The use of neural network analysis of clinical data obtained on the eve of childbirth allows to predict СNP development at sufficient degree of accuracy (98.0 %), which, in the future, after being introduced into clinical practice, will optimize a choice of delivery method in patients at risk (anatomically narrow pelvis, large fetus), reduce emergency caesarean sections and improve birth outcomes.