Fast selection of geologic models honoring CO2 plume monitoring data using Hausdorff distance and scaled connectivity analysis

Jeong, Hoonyoung; Srinivasan, Sanjay

doi:10.1016/j.ijggc.2017.02.005

Cited by 11 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To calculate the degree of resemblance between the reference spiral and cohort's drawing, we introduced features related to the Hausdorff distance, which quantify the extent to which each point in the reference spiral lies near the points in the cohort's drawing following procedures illustrated (9,(25)(26)(27). Similar to Figure 3 in Jeong and Srinivasan (27), a detailed example procedure to calculate the Hausdorff distance of the reference and cohort's drawing is presented in Supplementary Figure S1. We point out that the Hausdorff distance was calculated using the "metric.hausdorff " function in the fda.usc R software package (28).…”

Section: Feature Extractionsmentioning

confidence: 99%

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test

Messan

Pham

Harris

et al. 2022

Front. Med. Technol.

View full text Add to dashboard Cite

Technological advances, lack of medical professionals, high cost of face-to-face encounters, and disasters such as the COVID-19 pandemic fuel the telemedicine revolution. Numerous smartphone apps have been developed to measure neurological functions. However, their psychometric properties are seldom determined. It is unclear which designs underlie the eventual clinical utility of the smartphone tests. We have developed the smartphone Neurological Function Tests Suite (NeuFun-TS) and are systematically evaluating their psychometric properties against the gold standard of complete neurological examination digitalized into the NeurExTM app. This article examines the fifth and the most complex NeuFun-TS test, the “Spiral tracing.” We generated 40 features in the training cohort (22 healthy donors [HD] and 89 patients with multiple sclerosis [MS]) and compared their intraclass correlation coefficient, fold change between HD and MS, and correlations with relevant clinical and imaging outcomes. We assembled the best features into machine-learning models and examined their performance in the independent validation cohort (45 patients with MS). We show that by involving multiple neurological functions, complex tests such as spiral tracing are susceptible to intra-individual variations, decreasing their reproducibility and clinical utility. Simple tests, reproducibly measuring single function(s) that can be aggregated to increase sensitivity, are preferable in app design.

show abstract

Section: Feature Extractionsmentioning

confidence: 99%

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test

Messan

Pham

Harris

et al. 2022

Front. Med. Technol.

View full text Add to dashboard Cite

show abstract

“…P. Creagh et al, 2020; Dubuisson & Jain, 1994; Huttenlocher, Klanderman, & Rucklidge, 1993; Jeong & Srinivasan, 2017). Similar to Figure 3 in (Jeong & Srinivasan, 2017), a detail example procedure to calculate the Hausdorff distance of the reference and cohort’s drawing is presented in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

“…To calculate the degree of resemblance between the reference spiral and cohort's drawing, we introduced features related to the Hausdorff distance, which quantify the extent to which each point in the reference spiral lies near the points in the cohort's drawing following procedures illustrated in (A. P. Creagh et al, 2020;Dubuisson & Jain, 1994;Huttenlocher, Klanderman, & Rucklidge, 1993;Jeong & Srinivasan, 2017). Similar to Figure 3 in (Jeong & Srinivasan, 2017), a detail example procedure to calculate the Hausdorff distance of the reference and cohort's drawing is presented in Figure 2. Prior to calculating the Hausdorff distance, the x and y screen coordinate points of the reference spiral were interpolated to the length of the cohort's drawing's coordinates using cubic spline interpolation (Fritsch & Carlson, 1980;Hyman, 1983;McKinley & Levine, 1998).…”

Section: Clinical Assessments Of Motor Symptomsmentioning

confidence: 99%

Intra-individual reproducibility as essential determinant of clinical utility of smartphone-based neurological disability tests

Lkt

Harris

et al. 2021

Preprint

View full text Add to dashboard Cite

Technological advances, lack of medical professionals, high cost of face-to face encounters and disasters such as COVID19 pandemic, fuel the telemedicine revolution. Numerous smartphone apps have been developed to measure neurological functions. However, their psychometric properties are seldom determined. Lacking such data, it is unclear which designs underlie eventual clinical utility of the smartphone tests. We have developed the smartphone Neurological Function Tests Suite (NeuFun-TS) and are systematically evaluating their psychometric properties against the gold-standard of complete neurological examination digitalized into NeurExTM App. This paper examines the fifth, and thus far the most complex NeuFun-TS test, the "Spiral tracing". We generated 40 features in the training cohort (22 healthy donors [HD] and 105 multiple sclerosis [MS] patients) and compared their intraclass correlation coefficient, fold-change between HD and MS and correlations with relevant clinical and imaging outcomes. We assembled the best features into machine-learning models and examined their performance in the independent validation cohort (56 MS patients). We show that by aggregating multiple neurological functions, complex tests such as spiral tracing are susceptible to intra-individual variations, decreasing their reproducibility and thus, clinical utility. Simple tests, reproducibly measuring single function(s) that can be aggregated to increase sensitivity are preferable in app design.

show abstract

“…The computational costs associated with simulating these aspects of CCS can be prohibitive, necessitating the use of surrogate models. Although a large number of surrogate modeling studies have been conducted for CCS in the context of risk assessment, sensitivity analysis, UQ, and monitoring network design (Dai et al, ; Jeong & Srinivasan, , ; Keating et al, ; Oladyshkin et al, ; Pawar et al, ; Sun et al, , ), development of high‐fidelity surrogate models remains a challenging subject in the high‐dimensional decision space.…”

Section: Introductionmentioning

confidence: 99%

Predicting CO₂ Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

Zhi

Sun

Jeong

2019

Water Resources Research

Self Cite

148

View full text Add to dashboard Cite

Numerical simulation of flow and transport in heterogeneous formations has long been studied, especially for uncertainty quantification and risk assessment. The high computational cost associated with running large‐scale numerical simulations in a Monte Carlo sense has motivated the development of surrogate models, which aim to capture the important input‐output relations of physics‐based models but require only a fraction of the cost of full model runs. In this work, we formulate a conditional deep convolutional generative adversarial network (cDC‐GAN) surrogate model to learn the dynamic functional mappings in multiphase models. The cDC‐GAN belongs to a class of semisupervised learning methods that can be used to learn the data generation processes. Like the original GAN, a main strength of the cDC‐GAN is that it includes a self‐training scheme for improving the quality of generative modeling in a game theoretic framework, without requiring extensive statistical knowledge and assumptions on input data distributions. In particular, our cDC‐GAN model is designed to learn cross‐domain mappings between high‐dimensional input (e.g., permeability) and output (e.g., phase saturations) pairs, with the ability to incorporate conditioning information (e.g., prediction time). As a use case, we demonstrate the performance of cDC‐GAN for predicting the migration of carbon dioxide (CO2) plume in heterogeneous carbon storage reservoirs, which has both numerical and practical significance because of the safe storage requirements now mandated in many countries. Results show that cDC‐GAN achieves high accuracy in predicting the spatial and temporal evolution patterns of the injected CO2 plume, as compared to the original results obtained using a compositional reservoir simulator. The performance of cDC‐GAN models, trained using the same number of training samples, stays relatively robust when the level of spatial heterogeneity is increased. Our cDC‐GAN is pattern based and is not limited by the underlying physics. Thus, it provides a general framework for developing surrogate models, and for conducting uncertainty analyses for a wide range of physics‐based models used in both groundwater and subsurface energy exploration applications.

show abstract

Fast selection of geologic models honoring CO2 plume monitoring data using Hausdorff distance and scaled connectivity analysis

Cited by 11 publications

References 25 publications

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test

Intra-individual reproducibility as essential determinant of clinical utility of smartphone-based neurological disability tests

Predicting CO₂ Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

Contact Info

Product

Resources

About

Fast selection of geologic models honoring CO2 plume monitoring data using Hausdorff distance and scaled connectivity analysis

Cited by 11 publications

References 25 publications

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test

Intra-individual reproducibility as essential determinant of clinical utility of smartphone-based neurological disability tests

Predicting CO2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

Contact Info

Product

Resources

About

Predicting CO₂ Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network