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Articles 91 - 120 of 260

Full-Text Articles in Biomedical Engineering and Bioengineering

Improving Detection And Classification Of Steady-State Visual Evoked Potentials In Brain-Computer Interfaces, Hadi Habibzadeh Dec 2022

Improving Detection And Classification Of Steady-State Visual Evoked Potentials In Brain-Computer Interfaces, Hadi Habibzadeh

Legacy Theses & Dissertations (2009 - 2024)

Brain-computer interfaces (BCIs) provide an alternative communication channel to send computer commands without requiring any voluntary muscle control. They benefit people with limited communication capabilities due to motor function deficits, spinal cord injuries, and neurological disorders. They may also have applications for healthy individuals.

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The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah Dec 2022

The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah

Electronic Theses and Dissertations

Computational technologies can contribute to the modeling and simulation of the biological environments and activities towards achieving better interpretations, analysis, and understanding. With the emergence of digital pathology, we can observe an increasing demand for more innovative, effective, and efficient computational models. Under the umbrella of artificial intelligence, deep learning mimics the brain’s way in learn complex relationships through data and experiences. In the field of bioimage analysis, models usually comprise discriminative approaches such as classification and segmentation tasks. In this thesis, we study how we can use generative AI models to improve bioimage analysis tasks using Generative Adversarial Networks …

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Morton-Ordered Gpu Lattice Boltzmann Cfd Simulations With Application To Blood Flow, Gerald Gallagher, Fergal J. Boyle Sep 2022

Morton-Ordered Gpu Lattice Boltzmann Cfd Simulations With Application To Blood Flow, Gerald Gallagher, Fergal J. Boyle

Conference Papers

Computational fluid dynamics (CFD) is routinely used for numerically predicting cardiovascular-system medical device fluid flows. Most CFD simulations ignore the suspended cellular phases of blood due to computational constraints, which negatively affects simulation accuracy. A graphics processing unit (GPU) lattice Boltzmann-immersed boundary (LB-IB) CFD software package capable of accurately modelling blood flow is in development by the authors, focusing on the behaviour of plasma and stomatocyte, discocyte and echinocyte red blood cells during flow. Optimised memory ordering and layout schemes yield significant efficiency improvements for LB GPU simulations. In this work, comparisons of row-major-ordered Structure of Arrays (SoA) and Collected …

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Machine Learning And Scalable Informatics Methods To Predict Disease Status From Multimodal Biomedical Data, Hossein Mohammadian Foroushani Aug 2022

Machine Learning And Scalable Informatics Methods To Predict Disease Status From Multimodal Biomedical Data, Hossein Mohammadian Foroushani

McKelvey School of Engineering Graduate Student Theses & Dissertations

Biological understanding of complex diseases such as stroke and obesity is critical for the advancement of medicine. Further knowledge discovery can provide effective biomarkers to improve disease diagnosis and prognosis, identify driver mutations, predict individual genetic susceptibility for early prevention and effective disease management, and facilitate development of personalized drugs. Stroke is the second leading cause of death and long-term disability in the world. Thus, stroke management is a time-sensitive emergency. The initial hours after stroke onset map the trajectory of subsequent neurologic complications. Cerebral edema develops hours to days after acute ischemic stroke and may result in midline shift …

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Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu Aug 2022

Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and …

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Role Of Deep Learning Techniques In Non-Invasive Diagnosis Of Human Diseases., Hisham Abouelseoud Elsayem Abdeltawab Aug 2022

Role Of Deep Learning Techniques In Non-Invasive Diagnosis Of Human Diseases., Hisham Abouelseoud Elsayem Abdeltawab

Electronic Theses and Dissertations

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than …

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Positive Rate-Dependent Action Potential Prolongation By Modulating Potassium Ion Channels, Candido Cabo Jun 2022

Positive Rate-Dependent Action Potential Prolongation By Modulating Potassium Ion Channels, Candido Cabo

Publications and Research

Pharmacological agents that prolong action potential duration (APD) to a larger extent at slow rates than at the fast excitation rates typical of ventricular tachycardia exhibit reverse rate dependence. Reverse rate dependence has been linked to the lack of efficacy of class III agents at preventing arrhythmias because the doses required to have an anti-arrhythmic effect at fast rates may have pro-arrhythmic effects at slow rates due to an excessive APD prolongation. In this report we show that, in computer models of the ventricular action potential, APD prolongation by accelerating phase 2 repolarization (by increasing IKs) and decelerating …

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A Versatile Python Package For Simulating Dna Nanostructures With Oxdna, Kira Threlfall May 2022

A Versatile Python Package For Simulating Dna Nanostructures With Oxdna, Kira Threlfall

Computer Science and Computer Engineering Undergraduate Honors Theses

The ability to synthesize custom DNA molecules has led to the feasibility of DNA nanotechnology. Synthesis is time-consuming and expensive, so simulations of proposed DNA designs are necessary. Open-source simulators, such as oxDNA, are available but often difficult to configure and interface with. Packages such as oxdna-tile-binding pro- vide an interface for oxDNA which allows for the ability to create scripts that automate the configuration process. This project works to improve the scripts in oxdna-tile-binding to improve integration with job scheduling systems commonly used in high-performance computing environments, improve ease-of-use and consistency within the scripts compos- ing oxdna-tile-binding, and move …

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Radiomic Features To Predict Overall Survival Time For Patients With Glioblastoma Brain Tumors Based On Machine Learning And Deep Learning Methods, Lina Chato May 2022

Radiomic Features To Predict Overall Survival Time For Patients With Glioblastoma Brain Tumors Based On Machine Learning And Deep Learning Methods, Lina Chato

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for …

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Analysis Of An Existing Method In Refinement Of Protein Structure Predictions Using Cryo-Em Images, Maytha Alshammari, Jing He, Willy Wriggers, Jiangwen Sun Apr 2022

Analysis Of An Existing Method In Refinement Of Protein Structure Predictions Using Cryo-Em Images, Maytha Alshammari, Jing He, Willy Wriggers, Jiangwen Sun

College of Sciences Posters

Protein structure prediction produces atomic models from its amino acid sequence. Three-dimensional structures are important for understanding the function mechanism of proteins. Knowing the structure of a given protein is crucial in drug development design of novel enzymes. AlphaFold2 is a protein structure prediction tool with good performance in recent CASP competitions. Phenix is a tool for determination of a protein structure from a high-resolution 3D molecular image. Recent development of Phenix shows that it is capable to refine predicted models from AlphaFold2, specifically the poorly predicted regions, by incorporating information from the 3D image of the protein. The goal …

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A Meshless Approach To Computational Pharmacokinetics, Anthony Matthew Khoury Apr 2022

A Meshless Approach To Computational Pharmacokinetics, Anthony Matthew Khoury

Doctoral Dissertations and Master's Theses

The meshless method is an incredibly powerful technique for solving a variety of problems with unparalleled accuracy and efficiency. The pharmacokinetic problem of transdermal drug delivery (TDDD) is one such topic and is of significant complexity. The locally collocated meshless method (LCMM) is developed in solution to this topic. First, the meshless method is formulated to model this transport phenomenon and is then validated against an analytical solution of a pharmacokinetic problem set, to demonstrate this accuracy and efficiency. The analytical solution provides a locus by which convergence behavior are evaluated, demonstrating the super convergence of the locally collocated meshless …

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Volume Introduction, I. Glenn Cohen, Timo Minssen, W. Nicholson Price Ii, Christopher Robertson, Carmel Shachar Mar 2022

Volume Introduction, I. Glenn Cohen, Timo Minssen, W. Nicholson Price Ii, Christopher Robertson, Carmel Shachar

Other Publications

Medical devices have historically been less regulated than their drug and biologic counterparts. A benefit of this less demanding regulatory regime is facilitating innovation by making new devices available to consumers in a timely fashion. Nevertheless, there is increasing concern that this approach raises serious public health and safety concerns. The Institute of Medicine in 2011 published a critique of the American pathway allowing moderate-risk devices to be brought to the market through the less-rigorous 501(k) pathway,1 flagging a need for increased postmarket review and surveillance. High-profile recalls of medical devices, such as vaginal mesh products, along with reports globally …

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Two-Stage Transfer Learning For Facial Expression Classification In Children, Gregory Hubbard, Megan Witherow, Khan Iftekharuddin Mar 2022

Two-Stage Transfer Learning For Facial Expression Classification In Children, Gregory Hubbard, Megan Witherow, Khan Iftekharuddin

Undergraduate Research Symposium

Studying facial expressions can provide insight into the development of social skills in children and provide support to individuals with developmental disorders. In afflicted individuals, such as children with Autism Spectrum Disorder (ASD), atypical interpretations of facial expressions are well-documented. In computer vision, many popular and state-of-the-art deep learning architectures (VGG16, EfficientNet, ResNet, etc.) are readily available with pre-trained weights for general object recognition. Transfer learning utilizes these pre-trained models to improve generalization on a new task. In this project, transfer learning is implemented to leverage the pretrained model (general object recognition) on facial expression classification. Though this method, the …

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Reducing Loading On The Contralateral Limb Using Human-In-The-Loop Optimization, Siena Senatore Mar 2022

Reducing Loading On The Contralateral Limb Using Human-In-The-Loop Optimization, Siena Senatore

UNO Student Research and Creative Activity Fair

In most everyday activities, we head towards a specific goal by updating our choices for a more direct path. However, there are specific clinical tasks where taking the direct path is more challenging. Clinical investigations of optimizing a prosthesis involve the assessment of multiple parameter settings through trial and error rather than goal-directed optimization. We investigate if a human-in-the-loop optimization algorithm can guide manual alterations to a prosthesis-simulating device to reduce the ground reaction force on the contralateral limb. In most participants, the optimal condition reduced the loading rate on the contralateral limb compared to the initial condition tested. These …

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Design And Development Of Software With A Graphical User Interface To Display And Convert Multiple Microscopic Histology Images, Sayed Ahmadreza Razian, Majid Jadidi, Alexey Kamenskiy Mar 2022

Design And Development Of Software With A Graphical User Interface To Display And Convert Multiple Microscopic Histology Images, Sayed Ahmadreza Razian, Majid Jadidi, Alexey Kamenskiy

UNO Student Research and Creative Activity Fair

Histological images are widely used to assess the microscopic anatomy of biological tissues. Recent advancements in image analysis allow the identification of structural features on histological sections that can help advance medical device development, brain and cancer research, drug discovery, vascular mechanobiology, and many other fields. Histological slide scanners create images in SVS and TIFF formats that were designed to archive image blocks and high-resolution textual information. Because these formats were primarily intended for storage, they are often not compatible with conventional image analysis software and require conversion before they can be used in research. We have developed a user-friendly …

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Advancing Ubiquitous Collaboration For Telehealth - A Framework To Evaluate Technology-Mediated Collaborative Workflow For Telehealth, Hypertension Exam Workflow Study, Christopher Bondy Ph.D., Linlin Chen Ph.D, Pamela Grover Md, Pengcheng Shi Ph.D Feb 2022

Advancing Ubiquitous Collaboration For Telehealth - A Framework To Evaluate Technology-Mediated Collaborative Workflow For Telehealth, Hypertension Exam Workflow Study, Christopher Bondy Ph.D., Linlin Chen Ph.D, Pamela Grover Md, Pengcheng Shi Ph.D

Articles

Healthcare systems are under siege globally regarding technology adoption; the recent pandemic has only magnified the issues. Providers and patients alike look to new enabling technologies to establish real-time connectivity and capability for a growing range of remote telehealth solutions. The migration to new technology is not as seamless as clinicians and patients would like since the new workflows pose new responsibilities and barriers to adoption across the telehealth ecosystem. Technology-mediated workflows (integrated software and personal medical devices) are increasingly important in patient-centered healthcare; software-intense systems will become integral in prescribed treatment plans [1]. My research explored the path to …

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An Ensemble Approach For Patient Prognosis Of Head And Neck Tumor Using Multimodal Data, Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub Feb 2022

An Ensemble Approach For Patient Prognosis Of Head And Neck Tumor Using Multimodal Data, Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub

Computer Vision Faculty Publications

Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Tradi-tional machine learning algorithms have been eminently useful in crafting prognostic models in the last few decades. Recently, deep learning algorithms have shown significant improvement when developing diag-nosis and prognosis solutions to different healthcare problems. However, most of these solutions rely solely on either imaging or clinical data. Utilizing patient tabular data such as demographics and patient med-ical history alongside imaging data in a multimodal approach to solve a prognosis task has started to gain more interest recently and …

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Deep-Precognitive Diagnosis: Preventing Future Pandemics By Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Chao Cheng, Jing Zhang, Tianyang Wang, Min Xu Feb 2022

Deep-Precognitive Diagnosis: Preventing Future Pandemics By Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Chao Cheng, Jing Zhang, Tianyang Wang, Min Xu

Computer Vision Faculty Publications

Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them …

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Subomiembed: Self-Supervised Representation Learning Of Multi-Omics Data For Cancer Type Classification, Sayed Hashim, Muhammad Ali, Karthik Nandakumar, Mohammad Yaqub Feb 2022

Subomiembed: Self-Supervised Representation Learning Of Multi-Omics Data For Cancer Type Classification, Sayed Hashim, Muhammad Ali, Karthik Nandakumar, Mohammad Yaqub

Computer Vision Faculty Publications

For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features and small number of available samples. Different types of omics data show various aspects of samples. Integration and analysis of multi-omics data give us a broad view of tumours, which can improve clinical decision making. Omics data, mainly DNA methylation and gene expression profiles are usually high dimensional data with a lot of molecular features. In recent years, variational autoencoders (VAE) [13] have been extensively used in embedding image and text data into …

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Representation Learning For Chemical Activity Predictions, Mohamed S. Ayed Feb 2022

Representation Learning For Chemical Activity Predictions, Mohamed S. Ayed

Dissertations, Theses, and Capstone Projects

Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery and many other applications. Chemical fingerprints derived from chemical structures are a widely used feature to build machine learning models. However, the fingerprints ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in …

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Hyperparameter Optimization For Covid-19 Chest X-Ray Classification, Ibraheem Hamdi, Muhammad Ridzuan, Mohammad Yaqub Jan 2022

Hyperparameter Optimization For Covid-19 Chest X-Ray Classification, Ibraheem Hamdi, Muhammad Ridzuan, Mohammad Yaqub

Computer Vision Faculty Publications

Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains a worldwide dilemma, continuously developing new variants such as Delta and the recent Omicron. The current standard for testing is through polymerase chain reaction (PCR). However, PCRs can be expensive, slow, and/or inaccessible to many people. X-rays on the other hand have been readily used since the early 20th century and are relatively cheaper, quicker to obtain, and typically covered by health insurance. With a careful selection of model, hyperparameters, and augmentations, we show that it is possible to develop models with 83% accuracy in binary classification and 64% in multi-class …

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Transformers In Medical Imaging: A Survey, Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu Jan 2022

Transformers In Medical Imaging: A Survey, Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu

Computer Vision Faculty Publications

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved …

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Deep Learning-Based Quality Assessment Of Clinical Protocol Adherence In Fetal Ultrasound Dating Scans, Sevim Cengiz, Mohammad Yaqub Jan 2022

Deep Learning-Based Quality Assessment Of Clinical Protocol Adherence In Fetal Ultrasound Dating Scans, Sevim Cengiz, Mohammad Yaqub

Computer Vision Faculty Publications

To assess fetal health during pregnancy, doctors use the gestational age (GA) calculation based on the Crown Rump Length (CRL) measurement in order to check for fetal size and growth trajectory. However, GA estimation based on CRL, requires proper positioning of calipers on the fetal crown and rump view, which is not always an easy plane to find, especially for an inexperienced sonographer. Finding a slightly oblique view from the true CRL view could lead to a different CRL value and therefore incorrect estimation of GA. This study presents an AI-based method for a quality assessment of the CRL view …

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Automatic Segmentation Of Head And Neck Tumor: How Powerful Transformers Are?, Ikboljon Sobirov, Otabek Nazarov, Hussain Alasmawi, Mohammad Yaqub Jan 2022

Automatic Segmentation Of Head And Neck Tumor: How Powerful Transformers Are?, Ikboljon Sobirov, Otabek Nazarov, Hussain Alasmawi, Mohammad Yaqub

Computer Vision Faculty Publications

Cancer is one of the leading causes of death worldwide, and head and neck (H&N) cancer is amongst the most prevalent types. Positron emission tomography and computed tomography are used to detect and segment the tumor region. Clinically, tumor segmentation is extensively time-consuming and prone to error. Machine learning, and deep learning in particular, can assist to automate this process, yielding results as accurate as the results of a clinician. In this research study, we develop a vision transformers-based method to automatically delineate H&N tumor, and compare its results to leading convolutional neural network (CNN)-based models. We use multi-modal data …

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Is Contrastive Learning Suitable For Left Ventricular Segmentation In Echocardiographic Images?, Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub Jan 2022

Is Contrastive Learning Suitable For Left Ventricular Segmentation In Echocardiographic Images?, Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub

Computer Vision Faculty Publications

Contrastive learning has proven useful in many applications where access to labelled data is limited. The lack of annotated data is particularly problematic in medical image segmenta-tion as it is difficult to have clinical experts manually annotate large volumes of data. One such task is the segmentation of cardiac structures in ultrasound images of the heart. In this paper, we argue whether or not contrastive pretraining is helpful for the segmentation of the left ventricle in echocardiography images. Furthermore, we study the effect of this on two segmentation networks, DeepLabV3, as well as the commonly used segmentation net-work, UNet. Our …

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Is It Possible To Predict Mgmt Promoter Methylation From Brain Tumor Mri Scans Using Deep Learning Models?, Numan Saeed, Shahad Hardan, Kudaibergen Abutalip, Mohammad Yaqub Jan 2022

Is It Possible To Predict Mgmt Promoter Methylation From Brain Tumor Mri Scans Using Deep Learning Models?, Numan Saeed, Shahad Hardan, Kudaibergen Abutalip, Mohammad Yaqub

Computer Vision Faculty Publications

Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most cancer types, can be improved if a particular genetic sequence in the tumor known as MGMT promoter is methylated. However, to identify the state of the MGMT promoter, the conventional approach is to perform a biopsy for genetic analysis, which is time and effort consuming. A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use of deep …

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Challenges In Covid-19 Chest X-Ray Classification: Problematic Data Or Ineffective Approaches?, Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub Jan 2022

Challenges In Covid-19 Chest X-Ray Classification: Problematic Data Or Ineffective Approaches?, Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub

Computer Vision Faculty Publications

The value of quick, accurate, and confident diagnoses cannot be undermined to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we carry out extensive experiments on a large COVID-19 chest X-ray dataset to investigate the challenges faced with creating reliable solutions from both the data and machine learning perspectives. Accordingly, we offer an in-depth discussion into the challenges faced …

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Human Interaction With Fake News, Autumn Woodson, Sampath Jayarathna (Mentor) Jan 2022

Human Interaction With Fake News, Autumn Woodson, Sampath Jayarathna (Mentor)

Computer & Information Science: Research Experiences for Undergraduates in Disinformation Detection and Analytics

No abstract provided.

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Segmentation Of Intracranial Structures From Noncontrast Ct Images With Deep Learning, Evan Porter Jan 2022

Segmentation Of Intracranial Structures From Noncontrast Ct Images With Deep Learning, Evan Porter

Wayne State University Dissertations

Presented in this work is an investigation of the application of artificially intelligent algorithms, namely deep learning, to generate segmentations for the application in functional avoidance radiotherapy treatment planning. Specific applications of deep learning for functional avoidance include generating hippocampus segmentations from computed tomography (CT) images and generating synthetic pulmonary perfusion images from four-dimensional CT (4DCT).A single institution dataset of 390 patients treated with Gamma Knife stereotactic radiosurgery was created. From these patients, the hippocampus was manually segmented on the high-resolution MR image and used for the development of the data processing methodology and model testing. It was determined that …

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Part I - Ai And Data As Medical Devices, W. Nicholson Price Ii Jan 2022

Part I - Ai And Data As Medical Devices, W. Nicholson Price Ii

Other Publications

It may seem counterintuitive to open a book on medical devices with chapters on software and data, but these are the frontiers of new medical device regulation and law. Physical devices are still crucial to medicine, but they – and medical practice as a whole – are embedded in and permeated by networks of software and caches of data. Those software systems are often mindbogglingly complex and largely inscrutable, involving artificial intelligence and machine learning. Ensuring that such software works effectively and safely remains a substantial challenge for regulators and policymakers. Each of the three chapters in this part examines …

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