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3. 3D Augmented Reality-Assisted CT-Guided Interventions: System Design and Preclinical Trial on an Abdominal Phantom using HoloLens 2

Author

Park, Brian J., Hunt, Stephen J., Nadolski, Gregory J., and Gade, Terence P.

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction
Abstract

Background: Out-of-plane lesions pose challenges for CT-guided interventions. Augmented reality (AR) headset devices have evolved and are readily capable to provide virtual 3D guidance to improve CT-guided targeting. Purpose: To describe the design of a three-dimensional (3D) AR-assisted navigation system using HoloLens 2 and evaluate its performance through CT-guided simulations. Materials and Methods: A prospective trial was performed assessing CT-guided needle targeting on an abdominal phantom with and without AR guidance. A total of 8 operators with varying clinical experience were enrolled and performed a total of 86 needle passes. Procedure efficiency, radiation dose, and complication rates were compared with and without AR guidance. Vector analysis of the first needle pass was also performed. Results: Average total number of needle passes to reach the target reduced from 7.4 passes without AR to 3.4 passes with AR (54.2% decrease, p=0.011). Average dose-length product (DLP) decreased from 538 mGy-cm without AR to 318 mGy-cm with AR (41.0% decrease, p=0.009). Complication rate of hitting a non-targeted lesion decreased from 11.9% without AR (7/59 needle passes) to 0% with AR (0/27 needle passes). First needle passes were more nearly aligned with the ideal target trajectory with AR versus without AR (4.6{\deg} vs 8.0{\deg} offset, respectively, p=0.018). Medical students, residents, and attendings all performed at the same level with AR guidance. Conclusions: 3D AR guidance can provide significant improvements in procedural efficiency and radiation dose savings for targeting challenging, out-of-plane lesions. AR guidance elevated the performance of all operators to the same level irrespective of prior clinical experience., Comment: 16 pages, 6 figures, 2 tables

Published
2020
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21. Activity-induced gene expression in the human brain

Author

Chatterjee, Snehajyoti, primary, Park, Brian J., additional, Vanrobaeys, Yann, additional, Heiney, Shane A., additional, Rhone, Ariane E., additional, Nourski, Kirill V., additional, Langmack, Lucy, additional, Mukherjee, Utsav, additional, Kovach, Christopher K., additional, Kocsis, Zsuzsanna, additional, Kikuchi, Yukiko, additional, Petkov, Christopher I., additional, Hefti, Marco M., additional, Bahl, Ethan, additional, Michaelson, Jacob J, additional, Kawasaki, Hiroto, additional, Oya, Hiroyuki, additional, Howard, Matthew A., additional, Nickl-Jockschat, Thomas, additional, Lin, Li-Chun, additional, and Abel, Ted, additional

Published
2023
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27. Respiration extraction and atrial fibrillation detection from clinical data based on single RGB camera

Author

Que, Shuhao, van Esch, Rik J.C., Cramer, Iris C., Verstappen, Cindy, Kloeze, Carla, van 't Veer, Marcel, Dierick, Angelique, Hommerson, Susan, van Meulen, Fokke, Overeem, Sebastiaan, Montenij, Leon, Dekker, Lukas R.C., Bouwman, R. Arthur, Korsten, Erik, Bergmans, Jan, Zinger, Svitlana, Stuijk, Sander, Park, Brian J., Yoshida, Hiroyuki, Efficient Stream Processing Lab, Electronic Systems, Eindhoven MedTech Innovation Center, Cardiovascular Biomechanics, Biomedical Engineering, Signal Processing Systems, Center for Care & Cure Technology Eindhoven, Biomedical Diagnostics Lab, EAISI Foundational, EAISI Health, NeuroPlatform, and EAISI High Tech Systems

Subjects
optical flow, RGB, remote PPG, AI, atrial fibrillation, respiration, camera
Abstract

In this work, we investigated the feasibility of extracting continuous respiratory parameters from a single RGB camera stationed in a short-stay ward. Based on the extracted respiration parameters, we further investigated the feasibility of using respiratory features to aid in the detection of atrial fibrillation (AF). To extract respiration, we implemented two algorithms: chest optical flow (COF) and energy variance maximization (EVM). We used COF to extract respiration from the patient’s thoracic area and EVM from the patient’s facial area. Using capnography as the reference, for average breath-to-breath rate estimation (i.e., 15-second sliding windows with 50% overlap), we achieved errors within 3 breaths per minute with COF and within 3.5 breaths per minute with EVM. To detect the presence of AF in the respiratory signal, we extracted three respiratory features from the derived COF measurements. We fed these features to a logistic regression model and achieved an average AUC value of 0.64. This result showcases the potential of using camera-based respiratory parameters as predictors for AF, or as surrogate predictors when there is no sufficient facial area in the camera’s field of view for the extraction of cardiac measurements.

Published
2023

28. Video health monitoring for cardiac arrhythmia detection in a real hospital scenario

Author

van Esch, Rik J.C., Cramer, Iris, Li, Xian, Verstappen, Cindy, Kloeze, Carla, van 't Veer, Marcel, Dierick, Angelique, Hommerson, Susan, Montenij, Leon, Dekker, Lukas, Bouwman, R. Arthur, Korsten, Erik, Bergmans, Jan, Stuijk, Sander, Zinger, Sveta, Park, Brian J., Yoshida, Hiroyuki, Electronic Systems, Eindhoven MedTech Innovation Center, Electrical Engineering, Cardiovascular Biomechanics, Biomedical Engineering, Center for Care & Cure Technology Eindhoven, Biomedical Diagnostics Lab, Signal Processing Systems, EAISI Foundational, EAISI Health, NeuroPlatform, EAISI High Tech Systems, and Efficient Stream Processing Lab

Subjects
remote PPG, machine learning, Atrial Flutter, Atrial Fibrillation, Arrhythmia (heart rhythm disorders), RGB color model, Video, Atrial Fibrillation (AF), Cameras, Data modeling, Cardiac Arrhythmia
Abstract

Remote Photoplethysmography (remote PPG) enables contactless monitoring of the cardiac rhythm using video cameras. Prior research has shown the feasibility of video-based atrial fibrillation (AF) and/or flutter (Aflutter) detection in some scenarios, but most exclude patient movement. In this work, we investigate the feasibility of detecting these two cardiac arrhythmias in a regular hospital environment using an RGB camera, where patients were not limited in movement during the recording process. Data of 56 patients was collected before and after a scheduled cardioversion treatment. Using the data and machine learning models, we developed three models: First, a model to detect only AF from the data excluding any Aflutter cases. Here we report a sensitivity of 94.5% and a specificity of 89.3% with an AUC of 0.966. Second, a model to classify if a cardiac arrhythmia (AF or Aflutter) is present or not. There we report there a sensitivity of 95.6% and a specificity of 91.2% with an AUC of 0.975. Finally, we develop a multi rhythm model, where we classify the data in AF, Aflutter and sinus rhythm separately. The performance of arrhythmia detection is close to the second model, but we note that the distinction between AF and Aflutter is still a challenge. Here we theorize that remote PPG is more sensitive to noise during Aflutter, which will lead to features in Aflutter which are closer to those of AF. To confirm this, we will extensively review the reason of misclassification of Aflutter as AF in future work.

Published
2023

35. Utilization of Cancer Cell Line Screening to Elucidate the Anticancer Activity and Biological Pathways Related to the Ruthenium-Based Therapeutic BOLD-100.

Author

Park, Brian J., Raha, Paromita, Pankovich, Jim, and Bazett, Mark

Subjects
*THERAPEUTIC use of metals, *THERAPEUTIC use of antineoplastic agents, *PHENOMENOLOGICAL biology, *ANTINEOPLASTIC agents, *MACHINE learning, *METALS, *GENE expression, *RESEARCH funding, *TUMORS, *CELL lines, *PHARMACODYNAMICS
Abstract

Simple Summary: There is an unmet need for novel anticancer therapeutics that work differently to current standard-of-care therapies. BOLD-100 is a unique clinical-stage anticancer compound that is based on the rare metal, ruthenium. Understanding the bioactivity of BOLD-100 can accelerate its development towards approval and into clinical practice. The aim of this study was to use a large panel of cancer cell lines to formulate a sensitivity profile of BOLD-100 across various cancer types. BOLD-100 demonstrated increased activity in cell lines from esophageal cancer, blood cancers, and bladder cancer. These indications are in addition to the gastrointestinal cancers currently in clinical development, thus opening new opportunities. Using the sensitivity profile for downstream bioinformatics and pathway analysis revealed associations between cancer cell lines' sensitivity to BOLD-100 and ribosomal gene expression, including several genes coding for large- and small-ribosomal subunits. These findings provide evidence that ribosomal processes may be a critical component of BOLD-100's mechanism. BOLD-100 (sodium trans-[tetrachlorobis(1H indazole)ruthenate(III)]) is a ruthenium-based anticancer compound currently in clinical development. The identification of cancer types that show increased sensitivity towards BOLD-100 can lead to improved developmental strategies. Sensitivity profiling can also identify mechanisms of action that are pertinent for the bioactivity of complex therapeutics. Sensitivity to BOLD-100 was measured in a 319-cancer-cell line panel spanning 24 tissues. BOLD-100's sensitivity profile showed variation across the tissue lineages, including increased response in esophageal, bladder, and hematologic cancers. Multiple cancers, including esophageal, bile duct and colon cancer, had higher relative response to BOLD-100 than to cisplatin. Response to BOLD-100 showed only moderate correlation to anticancer compounds in the Genomics of Drug Sensitivity in Cancer (GDSC) database, as well as no clear theme in bioactivity of correlated hits, suggesting that BOLD-100 may have a differentiated therapeutic profile. The genomic modalities of cancer cell lines were modeled against the BOLD-100 sensitivity profile, which revealed that genes related to ribosomal processes were associated with sensitivity to BOLD-100. Machine learning modeling of the sensitivity profile to BOLD-100 and gene expression data provided moderative predictive value. These findings provide further mechanistic understanding around BOLD-100 and support its development for additional cancer types. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Risk Factors for Delirium in Elderly Patients After Lumbar Spinal Fusion

Author

Gold, Colin, primary, Ray, Emanuel, additional, Christianson, David, additional, Park, Brian J, additional, Kournoutas, Ioannis, additional, Khan, Taimur, additional, Perez, Eli A, additional, Berger, Joel I, additional, Sander, Katie, additional, Igram, Cassim A, additional, Pugely, Andrew, additional, Olinger, Catherine R, additional, Carnahan, Ryan, additional, Chen, Pei-fu, additional, Mueller, Rashmi, additional, Hitchon, Patrick W., additional, Howard, Matthew A, additional, Banks, Matthew, additional, Sanders, Robert D, additional, and Woodroffe, Royce W, additional

Published
2022
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