Validation of a computerized algorithm to quantify fetal heart rate deceleration area

Gyllencreutz, Erika; Lu, Ke; Lindecrantz, Kaj; Lindqvist, Pelle G.; Nordström, Lennart; Holzmann, Malin; Abtahi, Farhad

doi:10.1111/aogs.13370

Cited by 16 publications

(13 citation statements)

References 30 publications

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“…A possible weakness is that although the novel modified algorithm created to calculate STV has been validated in another study from our group, it has not been tested in other settings or compared with the original one in the Sonicaid Fetal Care™ system. Our study population is also a high‐risk group, all of whom had CTG changes indicating FBS, which might limit the generalizability of our results.…”

Section: Discussionmentioning

confidence: 99%

“…In the project, a modified algorithm to identify FHR baseline was created, described in detail in another article of this issue of AOGS . The aim was to exclude decelerations appropriately and thereafter calculate STV using the algorithm published by Dawes and Redman where the signal is averaged over 3.75‐second epochs and decelerations were excluded.…”

Section: Methodsmentioning

confidence: 99%

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Fetal heart rate short term variation during labor in relation to scalp blood lactate concentration

Holzmann

Abtahi

et al. 2018

Acta Obstet Gynecol Scand

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Fetal heart rate short term variation during labor in relation to scalp blood lactate concentration

Holzmann

Abtahi

et al. 2018

Acta Obstet Gynecol Scand

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“…It shows that cCTG is insufficient to evaluate fetal hypoxia, therefore, some analyses involve drawing blood from a fetal scalp which is an invasive procedure. It was believed that cCTG could have been gold standard assessment [11].…”

Section: Discussionmentioning

confidence: 99%

The relevance of Short-Term Variation (STV) value measured within 1 hour before delivery in predicting adverse neonatal outcome

et al. 2021

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Objectives: Computer CTG analysis (cCTG) included short-term variation (STV) is one of the methods of monitoring fetal condition during delivery. The aim of our study was to define appropriability of STV measured within 1 hour before delivery in prediction of neonatal outcomes. Material and methods:The retrospective study included 1014 pregnant women, who gave birth in the Department of Obstetrics and Perinatology. Participants were divided into two groups: group 1term pregnancies (37-41 weeks) and group 2preterm pregnancies (lower than 37 weeks). In each of them, two subgroups have been separated: control (STV ≥ 3 ms) and study group (STV < 3 ms).Results: In both groups 1 and 2, there were no statistically significant differences related to Apgar scores in 1 st , 3 rd and 5 th minute between group with STV < 3 ms and group with STV > 3 ms Moreover, for 37-41 weeks the sensitivity, specificity, positive predictive value and negative predictive value were: 22.7%, 83.9%, 3.3% and 97.8% and for lower than 37: 45.7%, 65.4%, 47.1%, 64.2% in 1 th minute after delivery. In group 1 the area under curve (AUC) measurements were 0.45 (95% CI: 0.32-0.58) for 1 st minute and 0.55 (95% CI: 0.35-0.74) for 5 th minute and in group 2: 0.58 (95% CI: 0.45-0.71) for 1th minute and 0.57 (95% CI: 0.42-0.72) for 5 th minute.Conclusions: High specificity and negative predictive value of STV indicates a good Apgar score of newborns in term pregnancies. Analysis of STV in preterm pregnancy is not clear.Fetal well-being in preterm pregnancy should include STV and other non-invasive and invasive tools.

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“…Prospective randomised data from the INFANT study group did not demonstrate any advantage over conventional visual assessment by medical staff present during delivery, neither in neonatal short-term outcomes nor in outcomes at two years 87 . The question of how far methodological weaknesses in the design of the study contributed to these non-significant differences between the study arms (Hawthorne effect) remains open 88 , 89 , especially since other computer-based approaches delivered clearly promising data 90 .…”

Section: Ai and Other Clinical Applications In Obstetric Monitoringmentioning

confidence: 99%

“…Die prospektiv-randomisiert erhobenen Daten der INFANT-Studiengruppe konnten tatsächlich keinen Vorteil gegenüber der konventionellen visuellen Beurteilung durch das Kreißsaalteam zeigen, weder im neonatalen Kurzzeitoutcome noch bei der Betrachtung des Outcomes nach 2 Jahren 87 . Die Frage, inwieweit methodologische Schwächen im Studiendesign diese nicht signifikanten Unterschiede zwischen den Studienarmen mit verursacht haben (Hawthorne-Effekt), bleibt offen 88 , 89 , zumal andere computerbasierte Ansätze deutlich erfolgversprechende Daten liefern konnten 90 .…”

Section: Ki Und Weitere Klinische Anwendungen In Der Geburtshilflichen üBerwachungunclassified

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

Weichert

Welp

Scharf

et al. 2021

Geburtshilfe Frauenheilkd

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The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter – at least in part – into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.

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Validation of a computerized algorithm to quantify fetal heart rate deceleration area

Abstract: A novel computerized algorithm for intrapartum cardiotocography analysis is as accurate as gold standard visual assessment, with high correlation and low bias.

Cited by 16 publications

References 30 publications

Fetal heart rate short term variation during labor in relation to scalp blood lactate concentration

Fetal heart rate short term variation during labor in relation to scalp blood lactate concentration

The relevance of Short-Term Variation (STV) value measured within 1 hour before delivery in predicting adverse neonatal outcome

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics – an Assessment of the State of Play

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