Smartphone Ophthalmoscope of Lens Vascularity to Estimate Gestational Age
Smartphone Ophthalmoscope of Lens Vascularity to Estimate Gestational Age
The purpose of this study is to develop a novel, highly automated method of gestational age
 estimation at delivery combining anterior lens capsule vascularity (ALCV) and biophysical
 parameters appropriate for use in low income countries. The specific aims of the proposed
 study are: (1) To develop an algorithm to predict gestational age at delivery from 26 to 42
 weeks' gestation with ALCV and key biophysical parameters (2)To evaluate the performance of
 ALCV and biophysical parameter-based gestational age estimates. Specifically, we hypothesize
 that the accuracy of the predictive algorithm will be comparable to commonly used measures of
 gestational age dating (±2 days) and have better precision (±14 days) than commonly used
 measures of gestational age dating.
Postnatal gestational age dating methods are needed in low/middle income settings as
 ultrasound is often unavailable, last menstrual dates uncertain, and physical/neurological
 scoring complex. The disappearance of anterior lens capsule vascularity (ALCV), a normal
 embryological process, has a high correlation with gestational age at delivery among preterm
 neonates. We will use an observational method-comparison study to establish the validity of
 smartphone ophthalmoscope ALCV gestational age estimates among preterm infants at delivery
 compared to the referent standard of ultrasound gestational age estimates. Study objectives
 and methods include the following: Objective 1. Develop an ALCV biomarker dataset from
 smartphone ophthalmoscope images using automated image analysis software. The dataset will
 include image features including: vessel quantity, lens clearing, tortuosity, vessel
 thickness, and branching angles and coefficients. Methods: (1) capture ALCV images via
 smartphone ophthalmoscope within 24 hours of delivery, and (2) segment ALCV images using
 software developed for the assessment of retinal vasculature. Proposed software will convert
 images into graphs and automatically find the vasculature based on the Dijkstra's
 shortest-path algorithm. The proposed software is tested for video-indirect neonatal
 ophthalmoscope images. Objective 2. Develop an algorithm to predict gestational age at
 delivery from 26 to 42 weeks' gestation with the ALCV biomarker dataset combined with key
 neonatal biophysical measures. Methods: (1) conduct descriptive and univariate analyses of
 predictors and assess linear model assumptions, and (2) fit a constrained linear regression
 model by selecting and shrinking estimated model coefficients from a fully specified model in
 the original sample to optimize predictive accuracy and model parsimony. Objective 3. Assess
 the performance of ALCV gestational age estimates compared to referent standard ultrasound
 estimates. Methods: (1) compare distributions of gestational age and the mean difference in
 days between dating methods, (2) test agreement between dating methods using Lin's
 concordance correlation coefficient and Bland-Altman plots with limits of agreement (3)
 internally validate test agreement using a bootstrap procedure for optimism correction of the
 agreement statistics. All statistical analyses will be performed in SAS 9.4 (SAS, Cary, North
 Carolina, USA).
Inclusion Criteria:

 -  Gestational age 260/7 to 426/7 weeks (based on antenatal ultrasound <20 weeks
 gestation)

 -  Chronological age <24 hours

 -  Consent of the patient's parents or legal guardians

 Exclusion Criteria:

 -  Fused eyelids

 -  Corneal opacity or cataract

 -  Obstetrical ultrasound performed at <20 weeks' gestation not performed/not available

 -  Known genetic/congenital anomalies

 -  Known coagulopathies

 -  Patients who, in the opinion of the attending physician or clinical study staff, are
 too medically unstable to participate in the study safely