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An algorithm for predicting Robin sequence from fetal MRI.

Cory M Resnick1,2, Tessa D Kooiman3, Carly E Calabrese4

  • 1Oral and Maxillofacial Surgery, Harvard School of Dental Medicine and Harvard Medical School, Boston, MA, USA.

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|February 21, 2018
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Summary
This summary is machine-generated.

Prenatal diagnosis of Robin sequence (RS) is improved by a new fetal MRI algorithm. This tool predicts RS with 98% accuracy using cleft palate, tongue shape, and jaw angle, aiding early intervention.

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Area of Science:

  • Medical imaging
  • Fetal medicine
  • Pediatric surgery

Background:

  • Robin sequence (RS) can cause airway compromise in newborns.
  • Prenatal diagnosis of RS is crucial for preparing delivery and postnatal care.
  • Fetal magnetic resonance imaging (MRI) offers potential for early RS detection.

Purpose of the Study:

  • To develop a predictive algorithm for Robin sequence (RS) using fetal MRI data.
  • To identify key fetal MRI indicators associated with RS.
  • To enhance prenatal detection and management of RS.

Main Methods:

  • Retrospective case-control study of fetal MRIs (2002-2017).
  • Subjects categorized into RS, micrognathia, cleft lip and palate (CLP), and controls.
  • Multivariable analysis of MRI variables including Veau classification, tongue shape index (TSI), and inferior facial angle (IFA).

Main Results:

  • 162 subjects included: 27 RS, 35 micrognathia, 46 CLP, 54 controls.
  • Independent predictors for RS were Veau I/II cleft palate (OR=38.8), TSI >80% (OR=8.7), and IFA <48° (OR=14.5).
  • The combination of these three factors achieved 98% predictive accuracy for RS.

Conclusions:

  • Fetal MRI findings of cleft palate, TSI >80%, and IFA <48° strongly predict Robin sequence.
  • Absence of these findings suggests a low probability (1%) of RS.
  • This algorithm can significantly improve prenatal identification and management of infants with RS.