Vol. 11, No 2: 42–55.

78th International Scientific Conference of Students and Young Scientists

2026

Scientific article

UDK 618.5-07

pdf-version

Ulyana E. Taeva
specialist degree, Petrozavodsk State University
(Petrozavodsk, Russia),
taevaulyana@gmail.com

Subjectivity in Visual Cardiotocography Interpretation and Prospects for Machine-Based Analysis

Scientific adviser:
Alexander A. Ivshin
Reviewer:
Julia Boldina
Paper submitted on: 05/07/2026;
Accepted on: 06/27/2026;
Published online on: 06/27/2026.
Abstract. Cardiotocography is the primary method of intrauterine foetal monitoring. However, its visual interpretation is subjective: inter-expert agreement on assessment does not exceed 48 %, and no guidelines provide both high sensitivity and specificity simultaneously. A review of 67 publications between 2015 and 2025 showed that ML algorithms outperform visual assessment in terms of reproducibility, achieving accuracy of 87–99 %, but their applicability is limited by training data imbalance and reduced accuracy on new samples. A promising direction is integrating AI into clinical decision support systems.
Keywords: cardiotocography, machine learning, inter-expert variability, clinical decision support system, CTG interpretation, foetal monitoring, artificial intelligence

For citation: Taeva, U. E. Subjectivity in Visual Cardiotocography Interpretation and Prospects for Machine-Based Analysis. StudArctic forum. 2026, 11 (2): 42–55.

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