Agreement between an Expert in Physiological Interpretation of Cardiotocographs (CTG) and the Tweris Mini CTG Artificial intelligence (AI) App in recognizing and managing different types of fetal hypoxic stress and abnormal CTG patterns
Main Article Content
Abstract
Introduction: Tweris Mini is a visual analysis Artificial Intelligence (AI) application designed to detect different types of fetal hypoxic stress, and common abnormal fetal heart rate patterns on Cardiotocograph (CTG) images. It is based on the classification system recommended by the International Expert Consensus Guidelines on Physiological Interpretation of Cardiotocograph published by over 50 CTG experts from more than 20 countries.
Objective: This study aims to evaluate the degree of agreement between the expert who pioneered the physiological interpretation of Cardiotocograph in 2006, and was on the editorial board of the international expert consensus guidelines, and the Tweris Mini App in classifying different types of fetal hypoxic stress and abnormal Cardiotocograph patterns, along with their proposed management.
Materials & Methods: A total of 100 anonymized CTG traces were randomly selected, representing no hypoxia (NH) and various types of fetal hypoxic stress: chronic (C), gradually evolving compensated (GC), gradually evolving decompensated (GD), subacute (S), and acute (A). Specific abnormal CTG patterns, including atypical sinusoidal (AS), typical sinusoidal (TS), and ZigZag (ZZ) patterns. The expert classified the traces and provided the following management recommendations: continue labour (CO), reduce stress (RS), or expedite birth (EB). Two independent obstetricians used the Tweris Mini App to classify the same CTG traces and the recommended management by the Tweris Mini AI App. The Cohen Kappa was used for statistical analysis to determine the level of agreement.
Results: The overall degree of agreement between the Tweris Mini App and the expert was 94%, with complete (100%) agreement relating to acute hypoxic stress, ZigZag and atypical sinusoidal (Poole Shark Teeth) patterns. The Cohen Kappa statistic for diagnostic agreement was 0.92 (95% CI: 0.83-0.99, p<.001). The agreement between the Tweris Mini App and the expert regarding the recommended management reached 98%. The Cohen Kappa statistic for management was 0.96 (95% CI: 0.94-0.98, p<.001.
Conclusion: There was excellent agreement (>90%) between the Tweris Mini App and the expert, who pioneered Physiological Interpretation of CTG in both diagnosing different types of fetal hypoxic stress and recommending optimum management. Our findings suggest that the Tweris AI Mini App has a high reliability to be used as diagnosis & decision support tool in clinical practice.
Keywords:
cardiotocograph (CTG); physiological CTG interpretation; artificial intelligence (AI); types of hypoxic stress Tweris Mini App (TMA); ZigZag pattern.
Article Details
How to Cite
GILLON, Mareva et al.
Agreement between an Expert in Physiological Interpretation of Cardiotocographs (CTG) and the Tweris Mini CTG Artificial intelligence (AI) App in recognizing and managing different types of fetal hypoxic stress and abnormal CTG patterns.
Medical Research Archives, [S.l.], v. 13, n. 4, apr. 2025.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/6484>. Date accessed: 15 may 2025.
doi: https://doi.org/10.18103/mra.v13i4.6484.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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6. Clark SL, Hankins GD. Temporal and demographic trends in cerebral palsy—fact and fiction. Am J Obstet Gynecol 2003;188:628-33.
7. Nelson KB, Dambrosia JM, Ting TY, Grether JK. Uncertain value of electronic fetal monitoring in predicting cerebral palsy. N Engl J Med 1996;334: 613-8.
8. Bernardes J, Costa-Pereira A, Ayres-de-Campos D, van Geijn HP, Pereira-Leite L. Evaluation of interobserver agreement of cardiotocograms. Int J Gynaecol Obstet 1997;57:33–7.
9. Blackwell SC, Grobman WA, Antoniewicz L, Hutchinson M, Gyamfi Bannerman C. Interobserver and intraobserver reliability of the NICHD 3-tier fetal heart rate interpretation system. Am J Obstet Gynecol 2011;205:378.e1–5.
10. Rei M, Tavares S, Pinto P, et al. Interobserver agreement in CTG interpretation using the 2015 FIGO guidelines for intrapartum fetal monitoring. Eur J Obstet Gynecol Reprod Biol 2016;205:27–31.
11. Beaulieu MD, Fabia J, Leduc B, et al. The reproducibility of intrapartum cardiotocogram assessments. Can Med Assoc J 1982;127: 214–6.
12. Santo S, Ayres-de-Campos D, Costa-Santos C, et al. Agreement and accuracy using the FIGO, ACOG and NICE cardiotocography interpretation guidelines. Acta Obstet Gynecol Scand 2017;96: 166–75.
13. Santo S, Ayres-de-Campos D. Human factors affecting the interpretation of fetal heart rate tracings: an update. Curr Opin Obstet Gynecol 2012;24:84–8.
14. Hruban L, Spilka J, Chudacek V, et al. Agreement on intrapartum cardiotocogram recordings between expert obstetricians. J Eval Clin Pract 2015; 21: 694–702.
15. Sabiani L, Le Dû R, Loundou A, et al. Intra- and interobserver agreement among obstetric experts in court regarding the review of abnormal fetal heart rate tracings and obstetrical management. Am J Obstet Gynecol 2015;213:856.e1-8.
16. Ayres-de-Campos D, Arteiro D, Costa-Santos C, Bernardes J. Knowledge of adverse neonatal outcome alters clinicians’ interpretation of the intrapartum cardiotocograph. BJOG 2011;118: 978-84.
17. Chandraharan E Intrapartum care: An urgent need to question historical practices and ‘non-evidence’-based, illogical foetal monitoring guidelines to avoid patient harm. Journal of Patient Safety and Risk Management. 2019; 24(5): 210-217.
18. Chandraharan E, Tahan ME, Pereira S. Each fetus matters: an urgent paradigm shift is needed to move away from the rigid “CTG Guideline Stickers” so as to individualize intrapartum fetal heart rate monitoring and to improve perinatal outcomes. Obstet Gynecol Int J 2016;5:00168.
19. Chandraharan E. Updated NICE Cardiotocograph (CTG) guideline: Is it suspicious or
pathological?. J Clin Med Surgery. 2023; 3(2): 1129.
20. Clark SL, Hamilton EF, Garite TJ, et al. The limits of electronic fetal heart rate monitoring in the prevention of neonatal metabolic acidemia. Am J Obstet Gynocol 2017; 216: 163.e1–163.e6.
21. Sartwelle TP, Johnston JC, Arda B, Zebenigus M. Cerebral palsy, cesarean sections, and electronic fetal monitoring: All the light we cannot see. Clinical Ethics. 2019;14(3):107-114.
22. Nelson KB, Sartwelle TP, Rouse DJ. Electronic fetal monitoring, cerebral palsy, and caesarean section: assumptions versus evidence. BMJ. 2016 Dec 1;355:i6405.
23. Chandraharan E. Physiological Interpretation of Cardiotocograph: Does the Emerging Scientific Evidence Suggest a Reversal in the “Thunder and Lightning” Phenomenon?. J Clin Med Surgery. 2023; 3(1): 1098.
24. Jia YJ, Ghi T, Pereira S, Gracia Perez-Bonfils A, Chandraharan E. Pathophysiological interpretation of fetal heart rate tracings in clinical practice. Am J Obstet Gynecol. 2023 Jun;228(6):622-644.
25. Chandraharan E. Handbook of CTG interpretation: from patterns to physiology. Cambridge, United Kingdom: Cambridge University Press; 2017.
26. Doria V, Papageorghiou AT, Gustafsson A, Ugwumadu A, Farrer K, Arulkumaran S. Review of the first 1502 cases of ECG-ST waveform analysis during labour in a teaching hospital. BJOG. 2007 Oct; 114(10): 1202-1207.
27. Chandraharan E. Fetal Electrocardiograph (ST-Analyser or STAN): Is it time for the Requiem?. J Clin Med Surgery. 2023; 3(2): 1111.
28. Chandraharan E, Arulkumaran S. Prevention of birth asphyxia: responding appropriately to cardiotocograph (CTG) traces. Best Pract Res Clin Obstet Gynaecol 2007;21:609–24.
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