Advertisement

Artificial intelligence and machine learning in cardiotocography: A scoping review

Published:December 08, 2022DOI:https://doi.org/10.1016/j.ejogrb.2022.12.008

      Abstract

      Introduction

      Artificial intelligence (AI) is gaining more interest in the field of medicine due to its capacity to learn patterns directly from data. This becomes interesting for the field of cardiotocography (CTG) interpretation, since it promises to remove existing biases and improve the well-known issues of inter- and intra-observer variability.

      Material and methods

      The objective of this study was to map current knowledge in AI-assisted interpretation of CTG tracings and thus, to present different approaches with their strengths, gaps, and limitations. The search was performed on Ovid Medline and PubMed databases. The Preferred Reporting Items for Systematic Reviews and meta-Analysis for Scoping Reviews (PRISMA-ScR) guidelines were followed.

      Results

      We summarized 40 different studies investigating at least one algorithm or system to classify CTG tracings. In addition, the Oxford Sonicaid system is presented because of its wide use in clinical practice.

      Conclusions

      There are several promising approaches in this area, but none of them has gained big acceptance in clinical practice. Further investigation and refinement of the algorithms and features are needed to achieve a validated decision-support system. For this purpose, larger quantities of curated and labeled data may be necessary.

      Abbreviations:

      ANN (artificial neural network), AI (artificial intelligence), AO (adverse outcome), CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), CNN (convolutional neural network), CTG (cardiotocography), DEC (decelerations), DSSAE (deep stacked sparse auto-encoder), DT (decision tree), GLCM (gray level co-occurrence matrix), IAGA (improved adaptive genetic algorithm), FHR (fetal heart rate), FLDA (fishers linear discriminant analysis), LNN (legendre neural network), LTV (long-term variability), ML (machine learning), MLA-ANFIS (multi-layer architecture of an adaptive neuro fuzzy inference system), NN (neural network), RF (Random Forest), RFE (recursive feature elimination), SMOTE (Synthetic Minority Over-sampling Technique), STFT (short time Fourier transform), STV (short-term variability), SVM (support vector machine), UC (uterine contractions), VNN (volterra neural networks), kNN (kappa-nearest neighbor)

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      References

      1. Dick S. Artificial Intelligence. Harvard Data Science Review [Internet] 2019 [cited 2021 Apr 13];Available from: https://hdsr.mitpress.mit.edu/pub/0aytgrau.

        • Beam A.L.
        • Kohane I.S.
        Big Data and Machine Learning in Health Care.
        JAMA. 2018; 319: 1317
        • Ravi D.
        • Wong C.
        • Deligianni F.
        • et al.
        Deep Learning for Health Informatics.
        IEEE J Biomed Health Inform. 2017; 21: 4-21
        • Robertson L.
        • Knight H.
        • Prosser Snelling E.
        • et al.
        Each baby counts: National quality improvement programme to reduce intrapartum-related deaths and brain injuries in term babies.
        Semin Fetal Neonatal Med. 2017; 22: 193-198
      2. Alfirevic Z, Devane D, Gyte G, Cuthbert A. Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. The Cochrane Database of Systematic Reviews [Internet] 2017;Available from: https://doi.org/10.1002/14651858.cd006066.pub3.

        • Balayla J.
        • Shrem G.
        Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis.
        Arch Gynecol Obstet. 2019; 300: 7-14
        • Johnson K.W.
        • Torres Soto J.
        • Glicksberg B.S.
        • et al.
        Artificial Intelligence in Cardiology.
        J Am Coll Cardiol. 2018; 71: 2668-2679
        • Choy G.
        • Khalilzadeh O.
        • Michalski M.
        • et al.
        Current Applications and Future Impact of Machine Learning in Radiology.
        Radiology. 2018; 288: 318-328
        • Rodriguez-Ruiz A.
        • Lång K.
        • Gubern-Merida A.
        • et al.
        Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.
        JNCI: J Natl Cancer Institute. 2019; 111: 916-922
        • Moher D.
        • Liberati A.
        • Tetzlaff J.
        • Altman D.G.
        The PRISMA Group. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement.
        PLoS Med. 2009; 6: e1000097
        • Whiting P.F.
        QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies.
        Ann Intern Med. 2011; 155: 529
        • McGuinness L.A.
        • Higgins J.P.T.
        Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments.
        Res Syn Meth. 2021; 12: 55-61
      3. K2s Medical Systems. Full, Contemporaneous Data Capture During Labour | K2 GuardianTM [Internet]. K2ms.com. 2020;Available from: https://www.k2ms.com/infant-guardian/guardian.aspx.

        • Brocklehurst P.
        • Field D.
        • Greene K.
        • et al.
        Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial.
        Lancet. 2017; 389: 1719-1729
        • Steer P.
        • Kovar I.
        • McKenzie C.
        • Griffin M.
        • Linsell L.
        Computerised analysis of intrapartum fetal heart rate patterns and adverse outcomes in the INFANT trial.
        BJOG: Int J Obstet Gy. 2019; 126: 1354-1361
        • Hamilton E.
        • Kimanani E.K.
        Intrapartum prediction of fetal status and assessment of labour progress.
        Baillière’s Clin Obstetr Gynaecol. 1994; 8: 567-581
        • Parer J.T.
        • Hamilton E.F.
        Comparison of 5 experts and computer analysis in rule-based fetal heart rate interpretation.
        Am J Obstet Gynecol. 2010; 203: 451.e1-451.e7
        • Elliott C.
        • Warrick P.A.
        • Graham E.
        • Hamilton E.F.
        Graded classification of fetal heart rate tracings: association with neonatal metabolic acidosis and neurologic morbidity.
        Am J Obstet Gynecol. 2010; 202: 258.e1-258.e8
        • Hamilton E.
        • Warrick P.
        • O’Keeffe D.
        Variable decelerations: do size and shape matter?.
        J Matern Fetal Neonatal Med. 2012; 25: 648-653
      4. National Institute of Child Health and Human Development NICHD [Internet]. 2022;Available from: https://www.nichd.nih.gov.

        • Ayres-de-Campos D.
        • Sousa P.
        • Costa A.
        • Bernardes J.
        Omniview-SisPorto® 3.5 – a central fetal monitoring station with online alerts based on computerized cardiotocogram+ST event analysis.
        J Perinat Med [Internet]. 2008; ([cited 2020 Nov 13];36(3). Available from:)
        • Ayres-de-Campos D.
        • Bernardes J.
        Comparison of fetal heart rate baseline estimation by SisPorto® 2.01 and a consensus of clinicians.
        Europ J Obstet Gynecol Reproduct Biol. 2004; 117: 174-178
        • Costa M.A.
        • Ayres-de-Campos D.
        • Machado A.P.
        • Santos C.C.
        • Bernardes J.
        Comparison of a computer system evaluation of intrapartum cardiotocographic events and a consensus of clinicians.
        J Perinat Med [Internet]. 2010; ([cited 2020 Aug 12];38(2). Available from:)
        • Ayres-de-Campos D.
        • Costa-Santos C.
        • Bernardes J.
        Prediction of neonatal state by computer analysis of fetal heart rate tracings: the antepartum arm of the SisPorto® multicentre validation study.
        Europ J Obstet Gynecol Reproduct Biol. 2005; 118: 52-60
        • Costa A.
        • Santos C.
        • Ayres-de-Campos D.
        • Costa C.
        • Bernardes J.
        Access to computerised analysis of intrapartum cardiotocographs improves clinicians’ prediction of newborn umbilical artery blood pH: Computerised CTG analysis improves umbilical artery pH prediction.
        BJOG. 2010; 117: 1288-1293
        • Gonçalves H.
        • Bernardes J.
        • Paula Rocha A.
        • Ayres-de-Campos D.
        Linear and nonlinear analysis of heart rate patterns associated with fetal behavioral states in the antepartum period.
        Early Hum Dev. 2007; 83: 585-591
        • Bernardes J.
        • Gonçalves H.
        • Ayres-de-Campos D.
        • Rocha A.P.
        Sex differences in linear and complex fetal heart rate dynamics of normal and acidemic fetuses in the minutes preceding delivery.
        J Perinat Med [Internet]. 2009; ([cited 2020 Nov 13];37(2). Available from:)
      5. Nunes I, Ayres-de-Campos D, Ugwumadu A, et al. FM-ALERT: a randomised clinical trial of intrapartum fetal monitoring with computer analysis and alerts versus previously available monitoring. [Internet]. Porto: 2015. Available from: http://www.omniview.eu/Cache/binImagens/2015_UK_7730patient_RCT-647.pdf.

      6. Amaral J, Costa A, Santos C, Ayres-de-Campos D, Bernardes J. Impact of the introduction of central fetal monitoring with computerised analysis and real-time alerts on the rates of caesarean section and adverse neonatal outcome. 2009.

        • Ayres-de-Campos D.
        • Rei M.
        • Nunes I.
        • Sousa P.
        • Bernardes J.
        SisPorto 4.0 – computer analysis following the 2015 FIGO Guidelines for intrapartum fetal monitoring.
        J Matern Fetal Neonatal Med. 2017; 30: 62-67
        • Nunes I.
        • Ayres-de-Campos D.
        • Ugwumadu A.
        • et al.
        Central Fetal Monitoring With and Without Computer Analysis: A Randomized Controlled Trial.
        Obstet Gynecol. 2017; 129: 83-90
        • Lopes-Pereira J.
        • Costa A.
        • Ayres-De-Campos D.
        • Costa-Santos C.
        • Amaral J.
        • Bernardes J.
        Computerized analysis of cardiotocograms and ST signals is associated with significant reductions in hypoxic-ischemic encephalopathy and cesarean delivery: an observational study in 38,466 deliveries.
        Am J Obstet Gynecol. 2019; 220: 269.e1-269.e8
        • Ayres-de-Campos D.
        • Spong C.Y.
        • Chandraharan E.
        FIGO Intrapartum Fetal Monitoring Expert Consensus Panel. FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography.
        Int J Gynecol Obstet. 2015; 131: 13-24
        • Dawes G.S.
        • Redman C.W.G.
        • Smith J.H.
        Improvements in the registration and analysis of fetal heart rate records at the bedside.
        BJOG: An Int J O&G. 1985; 92: 317-325
        • Schiermeier S.
        • Pildner von Steinburg S.
        • Thieme A.
        • et al.
        Sensitivity and specificity of intrapartum computerised FIGO criteria for cardiotocography and fetal scalp pH during labour: multicentre, observational study.
        BJOG. 2008; 115: 1557-1563
        • Schiermeier S.
        • Hatzmann H.
        • Reinhard J.
        Die Wertigkeit der computergestützten CTG-Analyse in den letzten 70 Minuten vor der Entbindung.
        Z Geburtshilfe Neonatol. 2008; 212: 189-193
        • Devoe L.
        • Golde S.
        • Kilman Y.
        • Morton D.
        • Shea K.
        • Waller J.
        A comparison of visual analyses of intrapartum fetal heart rate tracings according to the new National Institute of Child Health and Human Development guidelines with computer analyses by an automated fetal heart rate monitoring system.
        Am J Obstet Gynecol. 2000; 183: 361-366
        • McCartney P.R.
        Computer Analysis of the Fetal Heart Rate.
        J Obstet Gynecol Neonatal Nurs. 2000; 29: 527-536
        • Dawes G.S.
        • Moulden M.
        • Redman C.W.G.
        System 8000: Computerized antenatal FHR analysis.
        J Perinat Med. 1991; 19: 47-51
        • Ribbert L.S.M.
        • Fidler V.
        • Visser G.H.A.
        Computer-assisted analysis of normal second trimester fetal heart rate patterns.
        J Perinat Med. 1991; 19: 53-59
        • Bartnicki J.
        • Ratanasiri T.
        • Meyenburg M.
        • Saling E.
        Postterm pregnancy: computer analysis of the antepartum fetal heart rate patterns.
        Int J Gynecol Obstet. 1992; 37: 243-246
        • Tincello D.G.
        • El-Sapagh K.M.
        • Walkinshaw S.A.
        Computerised analysis of fetal heart rate recordings in patients with diabetes mellitus: the Dawes-Redman criteria may not be valid indicators of fetal well-being.
        J Perinat Med. 1998; 26: 102-106
        • Roberts D.
        • Kumar B.
        • Tincello D.G.
        • Walkinshaw S.A.
        Computerised antenatal fetal heart rate recordings between 24 and 28 weeks of gestation.
        BJOG: An Int J Obs Gyn. 2001; 108: 858-862
      7. Bracero LA, Roshanfekr D, Byrne DW. Analysis of antepartum fetal heart rate tracing by physician and computer. 2000;5.

        • Agrawal S.
        Intrapartum computerized fetal heart rate parameters and metabolic acidosis at birth.
        Obstet Gynecol. 2003; 102: 731-738
        • Georgoulas G.
        • Stylios D.
        • Groumpos P.
        Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines.
        IEEE Trans Biomed Eng. 2006; 53: 875-884
        • Ravindran S.
        • Jambek A.B.
        • Muthusamy H.
        • Neoh S.-C.
        A Novel Clinical Decision Support System Using Improved Adaptive Genetic Algorithm for the Assessment of Fetal Well-Being.
        Comput Math Methods Med. 2015; 2015: 1-11
      8. Dua D, Graff C. UCI Machine Learning Repository [Internet]. 2019;Available from: http://archive.ics.uci.edu/ml.

      9. Liang Xu, Georgieva A, Redman CWG, Payne SJ. Feature selection for computerized fetal heart rate analysis using genetic algorithms [Internet]. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka: IEEE; 2013 [cited 2021 Aug 13]. p. 445–8.Available from: http://ieeexplore.ieee.org/document/6609532/.

        • Romano M.
        • Bifulco P.
        • Ruffo M.
        • Improta G.
        • Clemente F.
        • Cesarelli M.
        Software for computerised analysis of cardiotocographic traces.
        Comput Methods Programs Biomed. 2016; 124: 121-137
        • Zhao Z.
        • Zhang Y.
        • Deng Y.
        A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State.
        JCM. 2018; 7: 223
        • Quinlan J.R.
        C4.5: programs for machine learning.
        Morgan Kaufmann Publishers, San Mateo, Calif1993
      10. Ukil A. Support Vector Machine [Internet]. In: Intelligent Systems and Signal Processing in Power Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg; 2007 [cited 2020 Aug 12]. p. 161–226.Available from: http://link.springer.com/10.1007/978-3-540-73170-2_4.

      11. Margineantu D, Dietterich T. Pruning Adaptive Boosting. 1997. p. 211–8.

        • Sbrollini A.
        • Carnicelli A.
        • Massacci A.
        • et al.
        Automatic Identification and Classification of Fetal Heart-Rate Decelerations from Cardiotocographic Recordings [Internet].
        in: In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Honolulu, HI2018 [cited 2020 Aug 12].: 474-477 (Available from:)
      12. The MathWorks, Inc. MATLAB - Mathworks [Internet]. Ch.mathworks.com. 1994;Available from: https://ch.mathworks.com/de/products/matlab.html.

        • Griffin D.
        • Lim J.
        Signal estimation from modified short-time Fourier transform.
        IEEE Trans Acoust, Speech Signal Process. 1984; 32: 236-243
      13. Cömert Z, Kocamaz AF. A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses [Internet]. Malatya: 2016. p. 569–73.Available from: https://www.researchgate.net/publication/308684397_A_Study_Based_on_Gray_Level_Co-Occurrence_Matrix_and_Neural_Network_Community_for_Determination_of_Hypoxic_Fetuses.

        • Cömert Z.
        • Kocamaz A.F.
        • Subha V.
        Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment.
        Comput Biol Med. 2018; 99: 85-97
        • Fergus P.
        • Selvaraj M.
        • Chalmers C.
        Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using Cardiotocography traces.
        Comput Biol Med. 2018; 93: 7-16
      14. 34. NI for health and CEN. Intrapartum Care For Healthy Women And Babies | Guidance. [Internet]. nice.org.uk. 2014;Available from: https://www.nice.org.uk/guidance/CG190.

        • Granitto P.M.
        • Burgos A.
        Feature selection on wide multiclass problems using OVA-RFE.
        Int Artif. 2010; 13: 621
        • Taft L.M.
        • Evans R.S.
        • Shyu C.R.
        • et al.
        Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery.
        J Biomed Inform. 2009; 42: 356-364
      15. Peterek T, Gajdoš P, Dohnálek P, Krohová J. Human Fetus Health Classification on Cardiotocographic Data Using Random Forests [Internet]. In: Pan J-S, Snasel V, Corchado ES, Abraham A, Wang S-L, editors. Intelligent Data analysis and its Applications, Volume II. Cham: Springer International Publishing; 2014 [cited 2020 Aug 12]. p. 189–98.Available from: http://link.springer.com/10.1007/978-3-319-07773-4_19.

        • Gyllencreutz E.
        • Lu K.
        • Lindecrantz K.
        • et al.
        Validation of a computerized algorithm to quantify fetal heart rate deceleration area.
        Acta Obstet Gynecol Scand. 2018; 97: 1137-1147
        • Zhao Z.
        • Deng Y.
        • Zhang Y.
        • Zhang Y.
        • Zhang X.
        • Shao L.
        DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural network.
        BMC Med Inform Decis Mak. 2019; 19: 286
        • Zhao Z.
        • Zhang Y.
        • Comert Z.
        • Deng Y.
        Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network.
        Front Physiol. 2019; 10: 255
      16. Haweel TI, Bangash JI. Volterra neural analysis of fetal cardiotocographic signals [Internet]. In: 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA). Sharjah: IEEE; 2013 [cited 2020 Aug 12]. p. 1–5.Available from: http://ieeexplore.ieee.org/document/6487321/.

        • Alsayyari A.
        Fetal cardiotocography monitoring using Legendre neural networks.
        Biomed Eng/Biomedizinische Technik. 2019; 64: 669-675
        • Chen C.-Y.
        • Yu C.
        • Chang C.-C.
        • Lin C.-W.
        Comparison of a Novel Computerized Analysis Program and Visual Interpretation of Cardiotocography.
        PLoS One. 2014; 9: e112296
        • Romagnoli S.
        • Sbrollini A.
        • Burattini L.
        • Marcantoni I.
        • Morettini M.
        • Burattini L.
        Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”.
        Data Brief. 2020; 31105690
      17. Fuentealba P, Illanes A, Ortmeier F. Cardiotocograph Data Classification Improvement by Using Empirical Mode Decomposition * [Internet]. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin, Germany: IEEE; 2019 [cited 2021 Aug 13]. p. 5646–9.Available from: https://ieeexplore.ieee.org/document/8856673/.

        • Iraji M.S.
        Prediction of fetal state from the cardiotocogram recordings using neural network models.
        Artif Intell Med. 2019; 96: 33-44
        • Huang M.-L.
        • Hsu Y.-Y.
        Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network.
        JBiSE. 2012; 05: 526-533
        • Aha D.W.
        • Kibler D.
        • Albert M.K.
        Instance-based learning algorithms.
        Mach Learn. 1991; 6: 37-66
        • Safavian S.R.
        • Landgrebe D.
        A survey of decision tree classifier methodology.
        IEEE Trans Syst, Man, Cybern. 1991; 21: 660-674
        • Cömert Z.
        • Şengür A.
        • Budak Ü.
        • Kocamaz A.F.
        Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models.
        Health Inf Sci Syst. 2019; 7: 17
        • Hoodbhoy Z.
        • Noman M.
        • Shafique A.
        • Nasim A.
        • Chowdhury D.
        • Hasan B.
        Use of machine learning algorithms for prediction of fetal risk using cardiotocographic data.
        Int J App Basic Med Res. 2019; 9: 226
        • Ayres-de-campos D.
        • Bernardes J.
        • Garrido A.
        • Marques-de-sá J.
        • Pereira-leite L.
        SisPorto 2.0: A Program for Automated Analysis of Cardiotocograms.
        J Matern Fetal Neonatal Med. 2000; 9: 311-318