Repeatability and reproducibility of artificial intelligence-acquired fetal brain measurements (SonoCNS) in the second and third trimesters of pregnancy.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
23 10 2024
23 10 2024
Historique:
received:
12
01
2024
accepted:
21
10
2024
medline:
24
10
2024
pubmed:
24
10
2024
entrez:
24
10
2024
Statut:
epublish
Résumé
Artificial Intelligence (AI)-based algorithms are increasingly entering clinical practice, aiding in the assessment of fetal anatomy and biometry. One such tool for evaluating the fetal head and central nervous system structures is SonoCNS™, which delineates appropriate planes for measuring head circumference (HC), biparietal diameter (BPD), occipitofrontal diameter (OFD), transcerebellar diameter (TCD), width of the posterior horn of the lateral ventricle (Vp), and cisterna magna (CM) based on a 3D volume acquired at the level of the fetal head's thalamic plane. This study aimed to evaluate the intra- and interobserver variability of measurements obtained using this software. The study included 381 patients, 270 in their second trimester of pregnancy (70%) and 111 in the third trimester. Each patient underwent manual biometric measurements of the aforementioned structures and twice using the SonoCNS software. We calculated the intraobserver variability between the manual measurements and the average of the automated measurements, as well as the interobserver variability for automated measurements. We also compared the median examination time for manual and automated measurements. The interclass correlation coefficients (ICC) for interobserver and intraobserver variability for parameters BPD, HC, and OFD ranged from good to excellent reproducibility in the general population and subgroups (> 0.75). CM and Vp measurements, both in the general population and subgroups, fell into the category of moderate (0.5-0.75) and poor reproducibility (< 0.5). TCD measurements showed moderate (> 0.5) to good reproducibility (0.75-0.9), and OFD showed good and excellent reproducibility. The assessment of the biometry of fetal head structures using SonoCNS took an average of 63 s compared to 14 s for manual measurement (p < 0.001). The SonoCNS™ software is characterized by good to excellent reproducibility and repeatability in the measurement of fetal skull biometry (BPD, HC, and OFD), with poorer performance in measurements of intracranial structures (CM, Vp, TCD). Apart from biometric parameters, the software is useful in clinical practice for delineating appropriate planes from the acquired volume of the fetal head and shortening examination time.
Identifiants
pubmed: 39443660
doi: 10.1038/s41598-024-77313-w
pii: 10.1038/s41598-024-77313-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25076Informations de copyright
© 2024. The Author(s).
Références
He, F., Wang, Y., Xiu, Y., Zhang, Y. & Chen, L. Artificial Intelligence in prenatal Ultrasound diagnosis. Front. Med. 8, 729978. https://doi.org/10.3389/fmed.2021.729978 (2021).
doi: 10.3389/fmed.2021.729978
Drukker, L., Noble, J. A. & Papageorghiou, A. T. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet. Gynecol. 56, 498–505. https://doi.org/10.1002/uog.22122 (2020).
doi: 10.1002/uog.22122
pubmed: 32530098
pmcid: 7702141
Horgan, R., Nehme, L. & Abuhamad, A. Artificial intelligence in obstetric ultrasound: a scoping review. Prenat Diagn. 43 (9), 1176–1219. https://doi.org/10.1002/pd.6411 (2023). Epub 2023 Jul 28. PMID: 37503802.
doi: 10.1002/pd.6411
pubmed: 37503802
Xie, H. N. et al. Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal. Ultrasound Obstet. Gynecol., 56: 579–587. https://doi.org/10.1002/uog.21967
Koo, T. K. & Li, M. Y. A Guideline of selecting and reporting Intraclass correlation coefficients for Reliability Research. J. Chiropr. Med. 15 (2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012 (June 2016). PMC 4913118. PMID 27330520.
Pluym, I. D. et al. Accuracy of automated three-dimensional ultrasound imaging technique for fetal head biometry. Ultrasound Obstet. Gynecol., 57: 798–803. https://doi.org/10.1002/uog.22171
Pluym, I. D. et al. Accuracy of automated three-dimensional ultrasound imaging technique for fetal head biometry. Ultrasound Obstet Gynecol. ;57(5):798–803. doi: (2021). https://doi.org/10.1002/uog.22171 . PMID: 32770786.
Adam, B. M. & Baharum, N. A simplified guide to determination of sample size requirements for estimating the value of intraclass correlation coefficient: a review. Arch. Orofac. Sci. 12 (1), 1–11 (2017).
Gembicki, M., Welp, A., Scharf, J. L., Dracopoulos, C. & Weichert, J. A Clinical Approach to Semiautomated three-dimensional fetal brain biometry-comparing the strengths and weaknesses of two diagnostic tools: 5DCNS + TM and SonoCNSTM. J. Clin. Med. 12 (16), 53 (2023).
doi: 10.3390/jcm12165334
Nagayasu, Y., Fujita, D. & Omichi, M. VP47.05: automating the process of measuring the fetal brain using three-dimensional fetal ultrasonography. Ultrasound Obstet. Gynecol. 58, 300–300. https://doi.org/10.1002/uog.24701 (2021).
doi: 10.1002/uog.24701
Gynecology International Society of Ultrasoung in Obstetrics and. https://www.isuog.org/static/uploaded/340263e4-07ef-4921-ab461bd9b97cd79c.pdf . [Online].
Malinger, G. et al. ISUOG Practice guidelines (updated): sonographic examination of the fetal central nervous system. Part 1: performance of screening examination and indications for targeted neurosoneurosonography. Ultrasound Obstet. Gynecol. 56 (3), 476–484. https://doi.org/10.1002/uog.22145 (2020). Erratum in: Ultrasound Obstet Gynecol. 2022;60(4):591. PMID: 32870591.
doi: 10.1002/uog.22145
pubmed: 32870591
Lin, M. et al. Use of real-time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations. Ultrasound Obstet. Gynecol. 59, 304–316. https://doi.org/10.1002/uog.24843 (2022).
doi: 10.1002/uog.24843
pubmed: 34940999
Company General Electric. https://intimex.com.pl/wp-content/uploads/2022/09/2022_Voluson_Expert_22_NPI_Artificial_Intelligence_capabilities_JB18773XX.pdf . [Online].
Gofer, S., Haik, O., Bardin, R., Gilboa, Y. & Perlman, S. Machine learning algorithms for classification of first-trimester fetal brain ultrasound images. J. Ultrasound Med. 41 (7), 1773–1779. https://doi.org/10.1002/jum.15860 (2022). Epub 2021 Oct 28. PMID: 34710247.
doi: 10.1002/jum.15860
pubmed: 34710247
Arjunan, Sridhar, P. & Thomas Mary Christeena and Deep learning measurement model to Segment the Nuchal Translucency Region for the early identification of Down Syndrome. Meas. Sci. Rev. 22 (4), 187–192. https://doi.org/10.2478/ (2022).
doi: 10.2478/msr-2022-0023
Zhou, Y. et al. (eds) in Q,,,. How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10–14 in ultrasound using deep learning. Comput Methods Programs Biomed. ;226:107170. doi: (2022). https://doi.org/10.1016/j.cmpb.2022.107170 . Epub 2022 Oct 2. PMID: 36272307.
Burgos-Artizzu, X. P. et al. Analysis of maturation features in fetal brain ultrasound via artificial intelligence for the estimation of gestational age. Am. J. Obstet. Gynecol. MFM. 3 (6), 100462. https://doi.org/10.1016/j.ajogmf.2021.100462 (2021). Epub 2021 Aug 14. PMID: 34403820.
doi: 10.1016/j.ajogmf.2021.100462
pubmed: 34403820
Namburete, A. I. et al. Learning-based prediction of gestational age from ultrasound images of the fetal brain. Med. Image Anal. 21 (1), 72–86. https://doi.org/10.1016/j.media.2014.12.006 (2015). Epub 2015 Jan 3. PMID: 25624045; PMCID: PMC4339204.
doi: 10.1016/j.media.2014.12.006
pubmed: 25624045
pmcid: 4339204
Miyagi, Y., Hata, T., Bouno, S., Koyanagi, A. & Miyake, T. Recognition of facial expression of fetuses by artificial intelligence (AI). J Perinat Med. ;49(5):596–603. doi: (2021). https://doi.org/10.1515/jpm-2020-0537 . PMID: 33548168.