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Assessing Intrauterine Retention according to Microscopic Stillbirth Features: A Cluster Analysis Approach.

Tess E K Cersonsky1, George R Saade2, Robert M Silver3

  • 1Warren Alpert Medical School of Brown University, Providence, RI, USA.

Fetal and Pediatric Pathology
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

Unsupervised machine learning identified objective histopathological features in stillbirths. These findings correlate with the time from fetal death to delivery, aiding clinical assessment.

Keywords:
perinatal pathologystillbirth/intrauterine fetal demisetime of deathunsupervised machine learning

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Area of Science:

  • Pathology
  • Medical Informatics
  • Perinatal Medicine

Background:

  • Previous studies relied on subjective clinical timing for stillbirth retention.
  • Microscopic changes in intrauterine stillbirths were previously documented.
  • Objective methods are needed to assess time from fetal death to delivery.

Purpose of the Study:

  • To apply unsupervised machine learning for identifying stillbirth features.
  • To objectively determine factors associated with intrauterine retention duration.
  • To correlate histopathological findings with time from fetal death to delivery.

Main Methods:

  • Utilized data from the Stillbirth Collaborative Research Network.
  • Employed hierarchical cluster analysis on pre-selected fetal and placental features.
  • Applied unsupervised machine learning, avoiding subjective clinical measures.

Main Results:

  • A four-cluster solution was identified, correlating with "no retention," "mild," "moderate," and "severe" retention.
  • Loss of nuclear basophilia in fetal organs varied across clusters.
  • Histopathological features objectively classified stillbirths based on retention intervals.

Conclusions:

  • Hierarchical cluster analysis effectively classifies stillbirths by retention duration.
  • Objective fetal and placental findings provide reliable indicators.
  • This method enhances clinical assessment of the interval from death to delivery.