文献詳細
今月の臨床 AIがもたらす産婦人科医療の変革
産科
文献概要
●帝王切開率は上昇しつつあり,緊急帝王切開リスクを予想し備えるシステムを構築することは重要である.
●現在,AIによる緊急帝王切開の予測精度は90%以上を超え,母体および胎児情報と胎児心拍モニタリングの情報を合わせることにより,より高い精度になる可能性がある.
●社会実装のために,AIは意思決定支援ツールであることを認識し,AIの特性を理解したうえで利用することが望ましい.
●現在,AIによる緊急帝王切開の予測精度は90%以上を超え,母体および胎児情報と胎児心拍モニタリングの情報を合わせることにより,より高い精度になる可能性がある.
●社会実装のために,AIは意思決定支援ツールであることを認識し,AIの特性を理解したうえで利用することが望ましい.
参考文献
1)WHO : Appropriate technology for birth. Lancet 2 : 436-437, 1985
2)厚生労働省 : 厚生労働統計一覧 医療施設調査.https://www. mhlw.go.jp/toukei/list/79-1.html
3)Darnal N, et al : Maternal and fetal outcome in emergency versus elective caesarean section. J Nepal Health Res Counc 18 : 186-189, 2020
4)Al Housseini A, et al : Prediction of risk for cesarean delivery in term nulliparas : a comparison of neural network and multiple logistic regression models. Am J Obstet Gynecol 201 : 113.e1-113.e6, 2009
5)Sufriyana H, et al : Comparison of multivariable logistic regression and other machine learning algorithms for prognostic prediction studies in pregnancy care : systematic review and mata-analysis. JMIR Med Inform 8 : e16503, 2020
6)Papoutsis D, et al : The SaTH risk-assessment tool for the prediction of emergency cesarean section in women having induction of labor for all indications : a large- cohort based study. Arch Gynecol Obstet 295 : 59-66, 2017
7)Campillo-Artero C, et al : Predictive modeling of emergency cesarean delivery. PLoS One 13 : e0191248, 2018
8)Ullah Z, et al : Reliable prediction models based on enriched data for identifying the mode of childbirth by using machine learning methods : development study. J Med Internet Res 23 : e28856, 2021
9)De Ramón Fernández A, et al : Prediction of the mode of delivery using artificial intelligence algorithms. Comput Methods Programs Biomed 219 : 106740, 2022
10)Nagayasu Y, et al : Use of an artificial intelligence-based rule extraction approach to predict an emergency cesarean section. Int J Gynaecol Obstet 157 : 654-662, 2022
11)Ayres-de-Campos D, et al : FIGO consensus guidelines on intrapartum fetal monitoring : cardiotocography. Int J Gynaecol Obstet 131 : 13-24, 2015
12)Costa MA, et al : Comparison of a computer system evaluation of intrapartum cardiotocographic events and a consensus of clinicians. J Perinat Med 38 : 191-195, 2010
13)Nunes I, et al : Central fetal monitoring with and without computer analysis. Obstet Gynecol 129 : 83-90, 2017
14)Campanile M, et al : Intrapartum cardiotocography with and without computer analysis : a systematic review and meta-analysis of randomized controlled trials. J Matern Fetal Neonatal Med 33:2284-2290, 2020
15)Balayla J, et al : Use of artificial intelligence(AI)in the interpretation of intrapartum fetal heart rate(FHR) tracings : a systematic review and meta-analysis. Arch Gynecol Obstet 300 : 7-14, 2019
16)Garcia-Canadilla P, et al : Machine learning in fetal cardiology : what to expect. Fetal Diagn Ther 47 : 363-372, 2020
17)O'Sullivan ME, et al : Challenges of developing robust AI for intrapartum fetal heart rate monitoring. Front Artif Intell 4 : 765210, 2021
18)O'Sullivan M, et al : Classification of fetal compromise during labour : signal processing and feature engineering of the cardiotocograph. Proceedigs of the 2021 29th European Signal Processing Conference(EUSIPCO). pp1331-1335, Dublin, Ireland, August 23-27, 2021
掲載誌情報