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This prospective study evaluated the relationship between laser speckle contrast imaging (LSCI) ocular blood flow velocity (BFV) and five birth parameters: gestational age (GA), postmenstrual age (PMA) and chronological age (CA) at the time of measurement, birth weight (BW), and current weight (CW) in preterm neonates at risk for retinopathy of prematurity (ROP). 38 Neonates with BW < 2 kg, GA < 32 weeks, and PMA between 27 and 47 weeks underwent 91 LSCI sessions. Correlation tests and regression analysis were performed to quantify relationships between birth parameters and ocular BFV. Mean ocular BFV index in this cohort was 8.8 +/- 4.0 IU. BFV positively correlated with PMA (r = 0.3, p = 0.01), CA (r = 0.3, p = 0.005), and CW (r = 0.3, p = 0.02). BFV did not correlate with GA nor BW (r = - 0.2 and r = - 0.05, p > 0.05). Regression analysis with mixed models demonstrated that BFV increased by 1.2 for every kilogram of CW, by 0.34 for every week of CA, and by 0.36 for every week of PMA (p = 0.03, 0.004, 0.007, respectively). Our findings indicate that increased age and weight are associated with increased ocular BFV measured using LSCI in premature infants. Future studies investigating the associations between ocular BFV and ROP clinical severity must control for age and/or weight of the infant.
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Peso al Nacer , Edad Gestacional , Retinopatía de la Prematuridad , Humanos , Recién Nacido , Femenino , Masculino , Estudios Prospectivos , Recien Nacido Prematuro , Velocidad del Flujo Sanguíneo , Vasos Retinianos/diagnóstico por imagen , Vasos Retinianos/fisiopatología , Retina/fisiopatología , Retina/diagnóstico por imagen , Factores de Riesgo , Flujo Sanguíneo RegionalRESUMEN
Purpose: To determine the correlation between blood flow metrics measured by intravenous fluorescein angiography (IVFA) and the blood flow velocity index (BFVi) obtained by laser speckle contrast imaging (LSCI) in infants with retinopathy of prematurity (ROP). Design: Prospective comparative pilot study. Subjects: Seven eyes from 7 subjects with ROP. Methods: Unilateral LSCI and IVFA data were obtained from each subject in the neonatal intensive care unit. Five LSCI-based metrics and 5 IVFA-based metrics were extracted from images to quantify blood flow patterns in the same region of interest. Correlation between LSCI-based and IVFA-based blood flow metrics was compared between 2 subgroups of ROP severity: moderate ROP (defined as stage ≤ 2 without Plus disease) and severe ROP (defined as stage ≥3 or Plus disease). Main Outcome Measures: Pearson and Kendall rank correlation coefficients between IVFA and LSCI metrics; Student t test P values comparing LSCI metrics between "severe" and "moderate" ROP groups. Results: Pearson correlations between IVFA and LSCI included arterial-venous transit time (AVTT) and peak BFVi (pBFVi; r = -0.917; P = 0.004), AVTT and dip BFVi (dBFVi; r = -0.920; P = 0.003), AVTT and mean BFVi (r = -0.927- P = 0.003), and AVTT and volumetric rise index (r = -0.779; P = 0.039). Kendall rank correlation between AVTT and dBFVi was r = -0.619 (P = 0.051). pBFVi was higher in severe ROP than in moderate ROP (8.4 ± 0.6 and 4.4 ± 1.8, respectively; P = 0.0045 using the 2-sample t test with pooled variance and P = 0.0952 using the Wilcoxon rank-sum test). Conclusions: Correlation was found between blood flow metrics obtained by IVFA and noninvasive LSCI techniques. We demonstrate the feasibility of obtaining quantitative metrics using LSCI in infants with ROP in this pilot study; however, further investigation is needed to evaluate its potential use in clinical assessment of ROP severity. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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This article evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI/machine learning (ML) algorithms are analyzed, as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models. As ANN and DNN analyses can be applied to data with an unknown clinical diagnosis, these algorithms are evaluated according to Bayesian statistical analyses. Bayesian neural network analyses are incorporated, as these algorithms indicate that the predictive accuracy and model performance are dependent upon accurate configurations of the model's hyperparameters and neural inputs. Thus, mathematical evaluations of AI algorithms are comprehensively explored to examine their clinical utility, as underperformance of AI/ML models may have deleterious consequences that affect patient outcomes due to misdiagnosis and false-negative test results.
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Inteligencia Artificial , Aprendizaje Automático , Humanos , Redes Neurales de la Computación , Algoritmos , Teorema de BayesRESUMEN
This prospective study evaluated the relationship between laser speckle contrast imaging (LSCI) ocular blood flow velocity (BFV) and five birth parameters: gestational age (GA), postmenstrual age (PMA), and chronological age (CA) at the time of measurement, birth weight (BW), and current weight (CW) in preterm neonates at risk for retinopathy of prematurity (ROP).38 Neonates with BW < 2 kg, GA < 32 weeks, and PMA between 27-47 weeks underwent 91 LSCI sessions. Correlation tests and regression analysis were performed to quantify relationships between birth parameters and ocular BFV. Mean ocular BFV index in this cohort was 8.8 +/- 4.0 IU. BFV positively correlated with PMA (r = 0.3, p = 0.01), CA (r = 0.3, p = 0.005), and CW (r = 0.3, p = 0.02). BFV did not correlate with GA nor BW (r=-0.2 and r=-0.05, p > 0.05). Regression analysis with mixed models demonstrated that BFV increased by 1.2 for every kilogram of CW, by 0.34 for every week of CA, and by 0.36 for every week of PMA (p = 0.03, 0.004, 0.007, respectively). Our findings indicate that increased age and weight are associated with increased ocular BFV measured using LSCI in premature infants. Future studies investigating the associations between ocular BFV and ROP clinical severity must control for age and/or weight of the infant.