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@article{ADVANCING OBSTETRIC ASSESSMENT: PROOF-OF-CONCEPT EVALUATION OF A LIDAR-BASED CERVICAL MEASUREMENT SYSTEM USING OVINE REPRODUCTIVE ORGANS_2026, volume={45}, url={https://americanjournal.org/index.php/ajtas/article/view/3363}, abstractNote={
Accurate assessment of cervical dilation is fundamental to obstetric decision-making during labor, yet current manual examination techniques are inherently subjective and associated with substantial inter-observer variability (Nixon et al., 2015; Sheiner et al., 2017). CerviLite is a novel cervical assessment device that utilizes light detection and ranging (LiDAR) technology to generate high-resolution three-dimensional point-cloud reconstructions of cervical anatomy, enabling objective and reproducible measurement of cervical dilation (Chen et al., 2017).
This proof-of-concept study evaluated CerviLite using ex vivo sheep reproductive organs, selected for their anatomical and biomechanical similarity to the human cervix (Smith et al., 2018). Cervical dilation measurements obtained with CerviLite were compared with standard manual measurements using medical measuring tape. Measurement accuracy, precision, and agreement were assessed using mean absolute error (MAE), mean percentage error (MPE), coefficient of variation (CV), and Bland–Altman analysis.
CerviLite demonstrated a mean absolute error of 0.9 mm, a mean percentage error of 3.2%, and a coefficient of variation of 3.2%, indicating high measurement accuracy and repeatability. Bland–Altman analysis showed a mean bias of 0.6 mm with 95% limits of agreement ranging from −1.4 mm to 2.6 mm, suggesting good concordance with manual measurements and no clinically significant systematic bias.
These findings support the feasibility of LiDAR-based cervical measurement and highlight CerviLite’s potential to reduce subjectivity in obstetric examinations. Further validation in live animal models and clinical settings is warranted to assess performance under physiological conditions and during active labor.
}, journal={American Journal of Technology and Applied Sciences}, year={2026}, month={Feb.}, pages={1–6} }