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Review Articles

Prediction of preterm birth using artificial intelligence: a systematic review

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References

  • Acharya UR, Sudarshan VK, Rong SQ, Tan Z, Lim CM, Koh JEW, et al. 2017. Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Computers in Biology and Medicine 85:33–42.
  • Agustin CA, Roberto R. 2014. Prediction of preterm birth in twin gestations using biophysical and biochemical tests. American Journal of Obstetrics and Gynecology 211:583–595.
  • Allouche M, Huissoud C, Guyard-Boileau B, Rouzier R, Parant O. 2011. Development and validation of nomograms for predicting preterm delivery. American Journal of Obstetrics and Gynecology 204:242.e1–242.e8.
  • Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. 2019. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet (London, England) 394:861–867.
  • Bahado-Singh RO, Sonek J, McKenna D, Cool D, Aydas B, Turkoglu O, et al. 2019. Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix. Ultrasound in Obstetrics & Gynecology 54:110–118.
  • Borowska M, Brzozowska E, Kuć P, Oczeretko E, Mosdorf R, Laudański P. 2018. Identification of preterm birth based on RQA analysis of electrohysterograms. Computer Methods and Programs in Biomedicine 153:227–236.
  • Chen L, Hao Y. 2017. Feature extraction and classification of EHG between pregnancy and labour group using Hilbert-Huang transform and extreme learning machine. Computational and Mathematical Methods in Medicine 2017:7949507.
  • Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, et al. 2018. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet (London, England) 392:2388–2396.
  • Considine EC, Khashan AS, Kenny LC. 2019. Screening for preterm birth: Potential for a metabolomics biomarker panel. Metabolites 9:90.
  • Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, Marwick TH. 2019. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. Journal of the American College of Cardiology 73:1317–1335.
  • Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. 2019. Accuracy of computer-aided diagnosis of melanoma: a meta-analysis. JAMA Dermatology 155:1291.
  • Esplin MS, O'Brien E, Fraser A, Kerber RA, Clark E, Simonsen SE, et al. 2008. Estimating recurrence of spontaneous preterm delivery. Obstetrics and Gynecology 112:516–523.
  • Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118.
  • Fergus P, Montanez CC, Abdulaimma B, Lisboa P, Chalmers C, Pineles B. 2020. Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women. IEEE/ACM Transactions on Computational Biology and Bioinformatics 17:668–678.
  • Fiset S, Martel A, Glanc P, Barrett J, Melamed N. 2019. Prediction of spontaneous preterm birth among twin gestations using machine learning and texture analysis of cervical ultrasound images. University of Toronto Medical Journal 96:6–9.
  • Gao C, Osmundson S, Velez Edwards DR, Jackson GP, Malin BA, Chen Y. 2019. Deep learning predicts extreme preterm birth from electronic health records. Journal of Biomedical Informatics 100:103334.
  • Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402–2410.
  • Hallingström M, Barman M, Savolainen O, Viklund F, Kacerovsky M, Brunius C, Jacobsson B. 2020. Metabolomic profiles of mid-trimester amniotic fluid are not associated with subsequent spontaneous preterm delivery or gestational duration at delivery. The Journal of Maternal-Fetal & Neonatal Medicine 16:1–9.
  • Hezelgrave NL, Abbott DS, Radford SK, Seed PT, Girling JC, Filmer J, et al. 2016. Quantitative fetal fibronectin at 18 weeks of gestation to predict preterm birth in asymptomatic high-risk women. Obstetrics and Gynecology 127:255–263.
  • Hannah B, Simon C, Mikkel ZO, Doris C, Ann-Beth M, Rajesh N, et al. 2012. National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. Lancet 379:2162–2172.
  • Iams JD, Goldenberg RL, Meis PJ, Mercer BM, Moawad A, Das A, et al. 1996. The length of the cervix and the risk of spontaneous premature delivery. National Institute of Child Health and Human Development Maternal Fetal Medicine Unit Network. New England Journal of Medicine 334:567–572.
  • Idowu IO, Fergus P, Hussain A, Dobbins C, Al-Askar H. 2014. Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births 2014 Eighth International Conference on Complex Intelligent and Software Intensive Systems (CISIS). New York: IEEE.
  • Khatibi T, Kheyrikoochaksarayee N, Sepehri MM. 2019. Analysis of big data for prediction of provider-initiated preterm birth and spontaneous premature deliveries and ranking the predictive features. Archives of Gynecology and Obstetrics 300:1565–1582.
  • Koivu A, Sairanen M. 2020. Predicting risk of stillbirth and preterm pregnancies with machine learning. Health Information Science and Systems 8:14.
  • Kuhrt K, Smout E, Hezelgrave N, Seed PT, Carter J, Shennan AH. 2016. Development and validation of a tool incorporating cervical length and quantitative fetal fibronectin to predict spontaneous preterm birth in asymptomatic high-risk women. Ultrasound in Obstetrics & Gynecology : The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology 47:104–109.
  • Langerhuizen DWG, Janssen SJ, Mallee WH, van den Bekerom MPJ, Ring D, Kerkhoffs GMMJ, et al. 2019. What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? A systematic review. Clinical Orthopaedics and Related Research 477:2482–2491.
  • Lee KS, Ahn KH. 2019. Artificial neural network analysis of spontaneous preterm labor and birth and its major determinants. Journal of Korean Medical Science 34:e128.
  • Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. 2009. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Medicine 6:e1000100.
  • McKeating DR, Clifton VL, Hurst CP, Fisher JJ, Bennett WW, Perkins A. 2020. Elemental metabolomics for prediction of term gestational outcomes utilising 18-week maternal plasma and urine samples. Biological Trace Element Research 199:26–40.
  • Menon R, Bhat G, Saade GR, Spratt H. 2014. Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth. Acta Obstetricia et Gynecologica Scandinavica 93:382–391.
  • Rajkomar A, Dean J, Kohane I. 2019. Machine learning in medicine. The New England Journal of Medicine 380:1347–1358.
  • Ramalingam P, Sandhya M, Sankar S. 2019. Using an innovative stacked ensemble algorithm for the accurate prediction of preterm birth. Journal of the Turkish German Gynecological Association 20:70–78.
  • Rawashdeh H, Awawdeh S, Shannag F, Henawi E, Faris H, Obeid N, Hyett J. 2020. Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage. Computational Biology and Chemistry 85:107233.
  • Ren P, Yao S, Li J, Valdes-Sosa PA, Kendrick KM. 2015. Improved prediction of preterm delivery using empirical mode decomposition analysis of uterine electromyography signals. PLoS One 10:e0132116.
  • Rittenhouse KJ, Vwalika B, Keil A, Winston J, Stoner M, Price JT, et al. 2019. Improving preterm newborn identification in low-resource settings with machine learning. PLoS One 14:e0198919.
  • Sadi-Ahmed N, Kacha B, Taleb H, Kedir-Talha M. 2017. Relevant features selection for automatic prediction of preterm deliveries from pregnancy electrohysterograhic (EHG) records. Journal of Medical Systems 41:204.
  • Senders JT, Staples PC, Karhade AV, Zaki MM, Gormley WB, Broekman MLD, et al. 2018. Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurgery 109:476–486.
  • Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529:484–489.
  • Weber A, Darmstadt GL, Gruber S, Foeller ME, Carmichael SL, Stevenson DK, Shaw GM. 2018. Application of machine-learning to predict early spontaneous preterm birth among nulliparous non-Hispanic black and white women. Annals of Epidemiology 28:783–789.e1.
  • Weber CR, Schwarz RA, Neely Atkinson E, Cox DD, Macaulay C, Follen M, Richards-Kortum R. 2008. Model-based analysis of reflectance and fluorescence spectra for in vivo detection of cervical dysplasia and cancer. Journal of Biomedical Optics 13:064016.
  • Włodarczyk T, Płotka S, Trzciński T, Rokita P, Sochacki-Wójcicka N, Lipa M, Wójcicki J. 2019. Estimation of preterm birth markers with u-net segmentation network Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
  • Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, PROBAST Group, et al. 2019. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Annals of Internal Medicine 170:51–58.

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