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Article ; Online: Feature Importance Analysis for Compensatory Reserve to Predict Hemorrhagic Shock.

Gupta, Jay F / Telfer, Brian A / Convertino, Victor A

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 1747–1752

Abstract: Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological ... ...

Abstract	Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate on an arterial blood pressure (ABP) waveform acquired via photoplethysmography have been shown to provide an effective early indicator. However, these machine learning approaches lack physiological interpretability. In this paper, we evaluate the importance of nine ABP-derived features that provide physiological insight, using a database of 40 human subjects from a lower-body negative pressure model of progressive central hypovolemia. One feature was found to be considerably more important than any other. That feature, the half-rise to dicrotic notch (HRDN), measures an approximate time delay between the ABP ejected and reflected wave components. This delay is an indication of compensatory mechanisms such as reduced arterial compliance and vasoconstriction. For a scale of 0% to 100%, with 100% representing normovolemia and 0% representing decompensation, linear regression of the HRDN feature results in root-mean-squared error of 16.9%, R2 of 0.72, and an area under the receiver operating curve for detecting decompensation of 0.88. These results are comparable to previously reported results from the more complex black box machine learning models. Clinical Relevance- A single physiologically interpretable feature measured from an arterial blood pressure waveform is shown to be effective in monitoring for blood loss and impending hemorrhagic shock based on data from a human lower-body negative pressure model of progressive central hypolemia.
MeSH term(s)	Blood Pressure/physiology ; Cardiovascular Diseases/complications ; Hemorrhage ; Humans ; Hypovolemia/diagnosis ; Lower Body Negative Pressure/adverse effects ; Shock, Hemorrhagic/complications ; Shock, Hemorrhagic/diagnosis
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871661
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Article ; Online: Noninvasive Monitoring of Simulated Hemorrhage and Whole Blood Resuscitation.

Gupta, Jay F / Arshad, Saaid H / Telfer, Brian A / Snider, Eric J / Convertino, Victor A

Biosensors

2022 Volume 12, Issue 12

Abstract: Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in ... ...

Abstract	Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage
Language	English
Publishing date	2022-12-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662125-3
ISSN	2079-6374 ; 2079-6374
ISSN (online)	2079-6374
ISSN	2079-6374
DOI	10.3390/bios12121168
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Article ; Online: Transfer Learning for Automated COVID-19 B-Line Classification in Lung Ultrasound.

Pare, Joseph R / Gjesteby, Lars A / Telfer, Brian A / Tonelli, Melinda M / Leo, Megan M / Billatos, Ehab / Scalera, Jonathan / Brattain, Laura J

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 1675–1681

Abstract: Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to ... ...

Abstract	Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective was to develop automated methods to classify B-lines as pathologic vs. normal. We developed transfer learning models based on ResNet networks to classify B-lines as pathologic (at least 3 B-lines per lung field) vs. normal using COVID-19 LUS data. Assessment of B-line severity on a 0-4 multi-class scale was also explored. For binary B-line classification, at the frame-level, all ResNet models pretrained with ImageNet yielded higher performance than the baseline nonpretrained ResNet-18. Pretrained ResNet-18 has the best Equal Error Rate (EER) of 9.1% vs the baseline of 11.9%. At the clip-level, all pretrained network models resulted in better Cohen's kappa agreement (linear-weighted) and clip score accuracy, with the pretrained ResNet-18 having the best Cohen's kappa of 0.815 [95% CI: 0.804-0.826], and ResNet-101 the best clip scoring accuracy of 93.6%. Similar results were shown for multi-class scoring, where pretrained network models outperformed the baseline model. A class activation map is also presented to guide clinicians in interpreting LUS findings. Future work aims to further improve the multi-class assessment for severity of B-lines with a more diverse LUS dataset.
MeSH term(s)	COVID-19/diagnostic imaging ; Deep Learning ; Humans ; Lung/diagnostic imaging ; Thorax ; Ultrasonography
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871894
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Article ; Online: Predicting Influenza A Tropism with End-to-End Learning of Deep Networks.

Scarafoni, Dan / Telfer, Brian A / Ricke, Darrell O / Thornton, Jason R / Comolli, James

Health security

2020 Volume 17, Issue 6, Page(s) 468–476

Abstract: The type of host that a virus can infect, referred to as host specificity or tropism, influences infectivity and thus is important for disease diagnosis, epidemic response, and prevention. Advances in DNA sequencing technology have enabled rapid ... ...

Abstract	The type of host that a virus can infect, referred to as host specificity or tropism, influences infectivity and thus is important for disease diagnosis, epidemic response, and prevention. Advances in DNA sequencing technology have enabled rapid metagenomic analyses of viruses, but the prediction of virus phenotype from genome sequences is an active area of research. As such, automatic prediction of host tropism from analysis of genomic information is of considerable utility. Previous research has applied machine learning methods to accomplish this task, although deep learning (particularly deep convolutional neural network, CNN) techniques have not yet been applied. These techniques have the ability to learn how to recognize critical hierarchical structures within the genome in a data-driven manner. We designed deep CNN models to identify host tropism for human and avian influenza A viruses based on protein sequences and performed a detailed analysis of the results. Our findings show that deep CNN techniques work as well as existing approaches (with 99% mean accuracy on the binary prediction task) while performing end-to-end learning of the prediction model (without the need to specify handcrafted features). The findings also show that these models, combined with standard principal component analysis, can be used to quantify and visualize viral strain similarity.
MeSH term(s)	Animals ; Birds ; Computer Simulation ; Genotype ; Humans ; Influenza A virus/genetics ; Influenza A virus/physiology ; Influenza in Birds/virology ; Influenza, Human/virology ; Machine Learning ; Models, Biological ; Neural Networks, Computer ; Phenotype ; Viral Tropism
Language	English
Publishing date	2020-01-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	2823049-8
ISSN	2326-5108 ; 2326-5094
ISSN (online)	2326-5108
ISSN	2326-5094
DOI	10.1089/hs.2019.0055
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Article ; Online: Image Processing Pipeline for Liver Fibrosis Classification Using Ultrasound Shear Wave Elastography.

Brattain, Laura J / Ozturk, Arinc / Telfer, Brian A / Dhyani, Manish / Grajo, Joseph R / Samir, Anthony E

Ultrasound in medicine & biology

2020 Volume 46, Issue 10, Page(s) 2667–2676

Abstract: The purpose of this study was to develop an automated method for classifying liver fibrosis stage ≥F2 based on ultrasound shear wave elastography (SWE) and to assess the system's performance in comparison with a reference manual approach. The reference ... ...

Abstract	The purpose of this study was to develop an automated method for classifying liver fibrosis stage ≥F2 based on ultrasound shear wave elastography (SWE) and to assess the system's performance in comparison with a reference manual approach. The reference approach consists of manually selecting a region of interest from each of eight or more SWE images, computing the mean tissue stiffness within each of the regions of interest and computing a resulting stiffness value as the median of the means. The 527-subject database consisted of 5526 SWE images and pathologist-scored biopsies, with data collected from a single system at a single site. The automated method integrates three modules that assess SWE image quality, select a region of interest from each SWE measurement and perform machine learning-based, multi-image SWE classification for fibrosis stage ≥F2. Several classification methods were developed and tested using fivefold cross-validation with training, validation and test sets partitioned by subject. Performance metrics were area under receiver operating characteristic curve (AUROC), specificity at 95% sensitivity and number of SWE images required. The final automated method yielded an AUROC of 0.93 (95% confidence interval: 0.90-0.94) versus 0.69 (95% confidence interval: 0.65-0.72) for the reference method, 71% specificity with 95% sensitivity versus 5% and four images per decision versus eight or more. In conclusion, the automated method reported in this study significantly improved the accuracy for ≥F2 classification of SWE measurements as well as reduced the number of measurements needed, which has the potential to reduce clinical workflow.
MeSH term(s)	Adolescent ; Adult ; Aged ; Aged, 80 and over ; Elasticity Imaging Techniques/methods ; Female ; Humans ; Image Processing, Computer-Assisted ; Liver Cirrhosis/classification ; Liver Cirrhosis/diagnostic imaging ; Male ; Middle Aged ; Retrospective Studies ; Young Adult
Language	English
Publishing date	2020-07-02
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	186150-5
ISSN	1879-291X ; 0301-5629
ISSN (online)	1879-291X
ISSN	0301-5629
DOI	10.1016/j.ultrasmedbio.2020.05.016
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Article ; Online: Individualized Ultrasound-Guided Intervention Phantom Development, Fabrication, and Proof of Concept.

Pierce, Theodore T / Ottensmeyer, Mark P / Som, Avik / Brattain, Laura J / Werblin, Joshua S / Sutphin, Patrick D / Schoen, Scott / Johnson, Matthew R / Gjesteby, Lars / Telfer, Brian A / Samir, Anthony E

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2023 Volume 2023, Page(s) 1–4

Abstract: Commercial ultrasound vascular phantoms lack the anatomic diversity required for robust pre-clinical interventional device testing. We fabricated individualized phantoms to test an artificial intelligence enabled ultrasound-guided surgical robotic system ...

Abstract	Commercial ultrasound vascular phantoms lack the anatomic diversity required for robust pre-clinical interventional device testing. We fabricated individualized phantoms to test an artificial intelligence enabled ultrasound-guided surgical robotic system (AI-GUIDE) which allows novices to cannulate deep vessels. After segmenting vessels on computed tomography scans, vessel cores, bony anatomy, and a mold tailored to the skin contour were 3D-printed. Vessel cores were coated in silicone, surrounded in tissue-mimicking gel tailored for ultrasound and needle insertion, and dissolved with water. One upper arm and four inguinal phantoms were constructed. Operators used AI-GUIDE to deploy needles into phantom vessels. Two groin phantoms were tested due to imaging artifacts in the other two phantoms. Six operators (medical experience: none, 3; 1-5 years, 2; 5+ years, 1) inserted 27 inguinal needles with 81% (22/27) success in a median of 48 seconds. Seven operators performed 24 arm injections, without tuning the AI for arm anatomy, with 71% (17/24) success. After excluding failures due to motor malfunction and a defective needle, success rate was 100% (22/22) in the groin and 85% (17/20) in the arm. Individualized 3D-printed phantoms permit testing of surgical robotics across a large number of operators and different anatomic sites. AI-GUIDE operators rapidly and reliably inserted a needle into target vessels in the upper arm and groin, even without prior medical training. Virtual device trials in individualized 3-D printed phantoms may improve rigor of results and expedite translation.Clinical Relevance- Individualized phantoms enable rigorous and efficient evaluation of interventional devices and reduce the need for animal and human subject testing.
MeSH term(s)	Animals ; Humans ; Artificial Intelligence ; Ultrasonography ; Needles ; Phantoms, Imaging ; Ultrasonography, Interventional/methods
Language	English
Publishing date	2023-12-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC40787.2023.10340966
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Article ; Online: Deep Learning-Based Automatic Endometrium Segmentation and Thickness Measurement for 2D Transvaginal Ultrasound.

Hu, Szu-Yeu / Xu, Hong / Li, Qian / Telfer, Brian A / Brattain, Laura J / Samir, Anthony E

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2019 Volume 2019, Page(s) 993–997

Abstract: Endometrial thickness is closely related to gyneco-logical function and is an important biomarker in transvaginal ultrasound (TVUS) examinations for assessing female reproductive health. Manual measurement is time-consuming and subject to high inter- and ...

Abstract	Endometrial thickness is closely related to gyneco-logical function and is an important biomarker in transvaginal ultrasound (TVUS) examinations for assessing female reproductive health. Manual measurement is time-consuming and subject to high inter- and intra- observer variability. In this paper, we present a fully automated endometrial thickness measurement method using deep learning. Our pipeline consists of: 1) endometrium segmentation using a VGG-based U-Net, and 2) endometrial thickness estimation using medial axis transformation. We conducted experimental studies on 137 2D TVUS cases (74/63 secretory phase/proliferative phase). On a test set of 27 cases/277 images, the segmentation Dice score is 0.83. For thickness measurement, we achieved mean absolute error of 1.23/1.38 mm and root mean squared error of 1.79/1.85 mm on two different test sets. The results are considered well within the clinically acceptable range of ±2 mm. Furthermore, our phase-stratified analysis shows that the measurement variance from the secretory phase is higher than that from the proliferative phase, largely due to the high variability of the endometrium appearance in the secretory phase. Future work will extend our current algorithm toward different clinical outcomes for a broader spectrum of clinical applications.
MeSH term(s)	Algorithms ; Deep Learning ; Endometrium/diagnostic imaging ; Female ; Humans ; Observer Variation ; Ultrasonography
Language	English
Publishing date	2019-12-30
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC.2019.8856367
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Article ; Online: Oxidative stress from DGAT1 oncoprotein inhibition in melanoma suppresses tumor growth when ROS defenses are also breached.

Cell reports

2024 Volume 39, Issue 12, Page(s) 110995

Abstract: Dysregulated cellular metabolism is a cancer hallmark for which few druggable oncoprotein targets have been identified. Increased fatty acid (FA) acquisition allows cancer cells to meet their heightened membrane biogenesis, bioenergy, and signaling needs. ...

Abstract	Dysregulated cellular metabolism is a cancer hallmark for which few druggable oncoprotein targets have been identified. Increased fatty acid (FA) acquisition allows cancer cells to meet their heightened membrane biogenesis, bioenergy, and signaling needs. Excess FAs are toxic to non-transformed cells but surprisingly not to cancer cells. Molecules underlying this cancer adaptation may provide alternative drug targets. Here, we demonstrate that diacylglycerol O-acyltransferase 1 (DGAT1), an enzyme integral to triacylglyceride synthesis and lipid droplet formation, is frequently up-regulated in melanoma, allowing melanoma cells to tolerate excess FA. DGAT1 over-expression alone transforms p53-mutant zebrafish melanocytes and co-operates with oncogenic BRAF or NRAS for more rapid melanoma formation. Antagonism of DGAT1 induces oxidative stress in melanoma cells, which adapt by up-regulating cellular reactive oxygen species defenses. We show that inhibiting both DGAT1 and superoxide dismutase 1 profoundly suppress tumor growth through eliciting intolerable oxidative stress.
MeSH term(s)	Animals ; Diacylglycerol O-Acyltransferase/genetics ; Diacylglycerol O-Acyltransferase/metabolism ; Melanoma ; Oncogene Proteins/metabolism ; Oxidative Stress ; Reactive Oxygen Species ; Triglycerides ; Zebrafish/metabolism
Chemical Substances	Oncogene Proteins ; Reactive Oxygen Species ; Triglycerides ; Diacylglycerol O-Acyltransferase (EC 2.3.1.20)
Language	English
Publishing date	2024-02-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2649101-1
ISSN	2211-1247 ; 2211-1247
ISSN (online)	2211-1247
ISSN	2211-1247
DOI	10.1016/j.celrep.2022.110995
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Article ; Online: Estimating Sedentary Breathing Rate from Chest-Worn Accelerometry From Free-Living Data.

Telfer, Brian A / Williamson, James R / Weed, Lara / Bursey, Max / Frazee, Royce / Galer, Meghan / Moore, Charles / Buller, Mark / Friedl, Karl E

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2020 Volume 2020, Page(s) 4636–4639

Abstract: Breathing rate was estimated from chest-worn accelerometry collected from 1,522 servicemembers during training by a wearable physiological monitor. A total of 29,189 hours of training and sleep data were analyzed. The primary purpose of the monitor was ... ...

Abstract	Breathing rate was estimated from chest-worn accelerometry collected from 1,522 servicemembers during training by a wearable physiological monitor. A total of 29,189 hours of training and sleep data were analyzed. The primary purpose of the monitor was to assess thermal-work strain and avoid heat injuries. The monitor design was thus not optimized to estimate breathing rate. Since breathing rate cannot be accurately estimated during periods of high activity, a qualifier was applied to identify sedentary time periods, totaling 8,867 hours. Breathing rate was estimated for a total of 4,179 hours, or 14% of the total collection and 47% of the sedentary total, primarily during periods of sleep. The breathing rate estimation method was compared to an FDA 510(K)-cleared criterion breathing rate sensor (Zephyr, Annapolis MD, USA) in a controlled laboratory experiment, which showed good agreement between the two techniques. Contributions of this paper are to: 1) provide the first analysis of accelerometry-derived breathing rate on free-living data including periods of high activity as well as sleep, along with a qualifier that effectively identifies sedentary periods appropriate for estimating breathing rate; 2) test breathing rate estimation on a data set with a total duration that is more than 60 times longer than that of the largest previously reported study, 3) test breathing rate estimation on data from a physiological monitor that has not been expressly designed for that purpose.
MeSH term(s)	Accelerometry ; Humans ; Monitoring, Physiologic ; Respiratory Rate ; Sleep ; Thorax
Language	English
Publishing date	2020-09-25
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC44109.2020.9175669
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Article ; Online: Objective Liver Fibrosis Estimation from Shear Wave Elastography.

Brattain, Laura J / Telfer, Brian A / Dhyani, Manish / Grajo, Joseph R / Samir, Anthony E

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2018 Volume 2018, Page(s) 1–5

Abstract: Diffuse liver disease is common, primarily driven by high prevalence of non-alcoholic fatty liver disease (NAFLD). It is currently assessed by liver biopsy to determine fibrosis, often staged as F0 (normal) - F4 (cirrhosis). A noninvasive assessment ... ...

Abstract	Diffuse liver disease is common, primarily driven by high prevalence of non-alcoholic fatty liver disease (NAFLD). It is currently assessed by liver biopsy to determine fibrosis, often staged as F0 (normal) - F4 (cirrhosis). A noninvasive assessment method will allow a broader population to be monitored longitudinally, facilitating risk stratification and treatment efficacy assessment. Ultrasound shear wave elastography (SWE) is a promising noninvasive technique for measuring tissue stiffness that has been shown to correlate with fibrosis stage. However, this approach has been limited by variability in stiffness measurements. In this work, we developed and evaluated an automated framework, called SWE-Assist, that checks SWE image quality, selects a region of interest (ROI), and classifies the ROI to determine whether the fibrosis stage is at or exceeds F2, which is important for clinical decisionmaking. Our database consists of 3,392 images from 328 cases. Several classifiers, including random forest, support vector machine, and convolutional neural network (CNN)) were evaluated. The best approach utilized a CNN and yielded an area under the receiver operating curve (AUROC) of 0.89, compared to the conventional stiffness only based AUROC of 0.74. Moreover, the new method is based on single image per decision, vs. 10 images per decision for the baseline. A larger dataset is needed to further validate this approach, which has the potential to improve the accuracy and efficiency of non-invasive liver fibrosis staging.
MeSH term(s)	Adult ; Area Under Curve ; Elasticity Imaging Techniques/methods ; Female ; Humans ; Liver Cirrhosis/diagnostic imaging ; Male ; Middle Aged ; Neural Networks, Computer ; Support Vector Machine
Language	English
Publishing date	2018-09-17
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC.2018.8513011
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