Dataset Information

Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis.

ABSTRACT: The objective of this study was to evaluate the performance of a new version of quantusFLM®, a software tool for prediction of neonatal respiratory morbidity (NRM) by ultrasound, which incorporates a fully automated fetal lung delineation based on Deep Learning techniques. A set of 790 fetal lung ultrasound images obtained at 24?+?0-38?+?6 weeks' gestation was evaluated. Perinatal outcomes and the occurrence of NRM were recorded. quantusFLM® version 3.0 was applied to all images to automatically delineate the fetal lung and predict NRM risk. The test was compared with the same technology but using a manual delineation of the fetal lung, and with a scenario where only gestational age was available. The software predicted NRM with a sensitivity, specificity, and positive and negative predictive value of 71.0%, 94.7%, 67.9%, and 95.4%, respectively, with an accuracy of 91.5%. The accuracy for predicting NRM obtained with the same texture analysis but using a manual delineation of the lung was 90.3%, and using only gestational age was 75.6%. To sum up, automated and non-invasive software predicted NRM with a performance similar to that reported for tests based on amniotic fluid analysis and much greater than that of gestational age alone.

SUBMITTER: Burgos-Artizzu XP

PROVIDER: S-EPMC6374419 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis.

Burgos-Artizzu Xavier P XP Perez-Moreno Álvaro Á Coronado-Gutierrez David D Gratacos Eduard E Palacio Montse M

Scientific reports 20190213 1

The objective of this study was to evaluate the performance of a new version of quantusFLM®, a software tool for prediction of neonatal respiratory morbidity (NRM) by ultrasound, which incorporates a fully automated fetal lung delineation based on Deep Learning techniques. A set of 790 fetal lung ultrasound images obtained at 24 + 0-38 + 6 weeks' gestation was evaluated. Perinatal outcomes and the occurrence of NRM were recorded. quantusFLM® version 3.0 was applied to all images to automatically ...[more]

PMID: 30760806

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Project description:IntroductionPodocyte depletion is a histomorphologic indicator of glomerular injury and predicts clinical outcomes. Podocyte estimation methods or podometrics are semiquantitative, technically involved, and laborious. Implementation of high-throughput podometrics in experimental and clinical workflows necessitates an automated podometrics pipeline. Recognizing that computational image analysis offers a robust approach to study cell and tissue structure, we developed and validated PodoCount (a computational tool for automated podocyte quantification in immunohistochemically labeled tissues) using a diverse data set.MethodsWhole-slide images (WSIs) of tissues immunostained with a podocyte nuclear marker and periodic acid-Schiff counterstain were acquired. The data set consisted of murine whole kidney sections (n = 135) from 6 disease models and human kidney biopsy specimens from patients with diabetic nephropathy (DN) (n = 45). Within segmented glomeruli, podocytes were extracted and image analysis was applied to compute measures of podocyte depletion and nuclear morphometry. Computational performance evaluation and statistical testing were performed to validate podometric and associated image features. PodoCount was disbursed as an open-source, cloud-based computational tool.ResultsPodoCount produced highly accurate podocyte quantification when benchmarked against existing methods. Podocyte nuclear profiles were identified with 0.98 accuracy and segmented with 0.85 sensitivity and 0.99 specificity. Errors in podocyte count were bounded by 1 podocyte per glomerulus. Podocyte-specific image features were found to be significant predictors of disease state, proteinuria, and clinical outcome.ConclusionPodoCount offers high-performance podocyte quantitation in diverse murine disease models and in human kidney biopsy specimens. Resultant features offer significant correlation with associated metadata and outcome. Our cloud-based tool will provide end users with a standardized approach for automated podometrics from gigapixel-sized WSIs.

Dataset Information

Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis.

Publications

Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis.

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