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Cross-Sectional Observational Study of Typical in utero Fetal Movements Using Machine Learning.
Vasung, Lana; Xu, Junshen; Abaci-Turk, Esra; Zhou, Cindy; Holland, Elizabeth; Barth, William H; Barnewolt, Carol; Connolly, Susan; Estroff, Judy; Golland, Polina; Feldman, Henry A; Adalsteinsson, Elfar; Grant, P Ellen.
Afiliação
  • Vasung L; Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
  • Xu J; Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA.
  • Abaci-Turk E; Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
  • Zhou C; Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
  • Holland E; Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
  • Barth WH; Department of Obstetrics and Gynecology, Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
  • Barnewolt C; Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
  • Connolly S; Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
  • Estroff J; Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
  • Golland P; Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA.
  • Feldman HA; Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, USA.
  • Adalsteinsson E; Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts, USA.
  • Grant PE; Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA.
Dev Neurosci ; 45(3): 105-114, 2023.
Article em En | MEDLINE | ID: mdl-36538911
ABSTRACT
Early variations of fetal movements are the hallmark of a healthy developing central nervous system. However, there are no automatic methods to quantify the complex 3D motion of the developing fetus in utero. The aim of this prospective study was to use machine learning (ML) on in utero MRI to perform quantitative kinematic analysis of fetal limb movement, assessing the impact of maternal, placental, and fetal factors. In this cross-sectional, observational study, we used 76 sets of fetal (24-40 gestational weeks [GW]) blood oxygenation level-dependent (BOLD) MRI scans of 52 women (18-45 years old) during typical pregnancies. Pregnant women were scanned for 5-10 min while breathing room air (21% O2) and for 5-10 min while breathing 100% FiO2 in supine and/or lateral position. BOLD acquisition time was 20 min in total with effective temporal resolution approximately 3 s. To quantify upper and lower limb kinematics, we used a 3D convolutional neural network previously trained to track fetal key points (wrists, elbows, shoulders, ankles, knees, hips) on similar BOLD time series. Tracking was visually assessed, errors were manually corrected, and the absolute movement time (AMT) for each joint was calculated. To identify variables that had a significant association with AMT, we constructed a mixed-model ANOVA with interaction terms. Fetuses showed significantly longer duration of limb movements during maternal hyperoxia. We also found a significant centrifugal increase of AMT across limbs and significantly longer AMT of upper extremities <31 GW and longer AMT of lower extremities >35 GW. In conclusion, using ML we successfully quantified complex 3D fetal limb motion in utero and across gestation, showing maternal factors (hyperoxia) and fetal factors (gestational age, joint) that impact movement. Quantification of fetal motion on MRI is a potential new biomarker of fetal health and neuromuscular development.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Placenta / Hiperóxia Tipo de estudo: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Female / Humans / Middle aged / Pregnancy Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Placenta / Hiperóxia Tipo de estudo: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Female / Humans / Middle aged / Pregnancy Idioma: En Ano de publicação: 2023 Tipo de documento: Article