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Article
Open AccessFactors influencing U.S. women’s interest and preferences for breast cancer risk communication: a cross-sectional study from a large tertiary care breast imaging center
Breast imaging clinics in the United States (U.S.) are increasingly implementing breast cancer risk assessment (BCRA) to align with evolving guideline recommendations but with limited uptake of risk-reduction ...
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Article
Liver fibrosis classification from ultrasound using machine learning: a systematic literature review
Liver biopsy was considered the gold standard for diagnosing liver fibrosis; however, with advancements in medical technology and increasing awareness of potential complications, the reliance on liver biopsy h...
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Article
Open AccessOpportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach
Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events—the leading cause of global mortality—have known limitations and may be improved by imaging biomarkers. While ...
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Article
Synthetic dual-energy CT reconstruction from single-energy CT Using artificial intelligence
To develop and assess the utility of synthetic dual-energy CT (sDECT) images generated from single-energy CT (SECT) using two state-of-the-art generative adversarial network (GAN) architectures for artificial ...
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Article
Natural Language Processing Model for Identifying Critical Findings—A Multi-Institutional Study
Improving detection and follow-up of recommendations made in radiology reports is a critical unmet need. The long and unstructured nature of radiology reports limits the ability of clinicians to assimilate the...
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Protocol
Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research
Recurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models ca...
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Article
A Systematic Review of ‘Fair’ AI Model Development for Image Classification and Prediction
The new challenge in Artificial Intelligence (AI) is to understand the limitations of models to reduce potential harm. Particularly, unknown disparities based on demographic factors could encrypt currently exi...
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Article
Open AccessIn-sensor neural network for high energy efficiency analog-to-information conversion
This work presents an on-chip analog-to-information conversion technique that utilizes analog hyper-dimensional computing based on reservoir-computing paradigm to process electrocardiograph (ECG) signals local...
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Article
Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports
Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in sco...
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Article
Open AccessFusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset
The objective of this work is to develop a fusion artificial intelligence (AI) model that combines patient electronic medical record (EMR) and physiological sensor data to accurately predict early risk of seps...
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Article
Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for various imaging related tasks such as artificial image generation to support AI training. GANs are especially usefu...
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Article
Open AccessTransfer language space with similar domain adaptation: a case study with hepatocellular carcinoma
Transfer learning is a common practice in image classification with deep learning where the available data is often limited for training a complex model with millions of parameters. However, transferring langu...
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Chapter
Currently Available Artificial Intelligence Softwares for Cardiothoracic Imaging
There has been rapid development of commercial radiology AI software over the past several years, particularly for cardiothoracic imaging. These applications can be broadly classified into cardiac, pulmonary, ...
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Chapter
Ethical Considerations of Artificial Intelligence Applications in Healthcare
The recent advances in artificial intelligence (AI) and its expanding adoption in medicine demand new approaches to ethical consideration. This is because the intersection between AI and medicine is increasing...
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Chapter
Natural Language Processing for Cardiovascular Applications
Natural language processing (NLP) systems can turn unstructured clinic notes (e.g. progression notes, discharge summary, ECG reports) into query-able structured data to support on-demand knowledge discovery an...
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Chapter and Conference Paper
CVAD: An Anomaly Detector for Medical Images Based on Cascade VAE
Anomaly detection in medical imaging plays an important role to ensure AI generalization. However, existing out-of-distribution (OOD) detection approaches fail to account for OOD data granularity in medical im...
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Article
Optimizing risk-based breast cancer screening policies with reinforcement learning
Screening programs must balance the benefit of early detection with the cost of overscreening. Here, we introduce a novel reinforcement learning-based framework for personalized screening, Tempo, and demonstra...
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Article
Open AccessA DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images
Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient...
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Article
Open AccessPatient-specific COVID-19 resource utilization prediction using fusion AI model
The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine le...
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Article
Open AccessWeakly supervised temporal model for prediction of breast cancer distant recurrence
Efficient prediction of cancer recurrence in advance may help to recruit high risk breast cancer patients for clinical trial on-time and can guide a proper treatment plan. Several machine learning approaches h...
