Current State of Community-Driven Radiological AI Deployment in Medical Imaging.

Artificial intelligence (AI) has become commonplace in solving routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. AI has been shown to improve efficiency in medical image generation, processing, and interpretation, and various such AI models have been developed across research laboratories worldwide.

Greater burden of white matter lesions and silent infarcts ipsilateral to carotid stenosis.

Objectives: Carotid stenosis may cause silent cerebrovascular disease (CVD) through atheroembolism and hypoperfusion. If so, revascularization may slow progression of silent CVD. We aimed to compare the presence and severity of silent CVD to the degree of carotid bifurcation stenosis by cerebral hemisphere.

Pre-deployment assessment of an AI model to assist radiologists in chest X-ray detection and identification of lead-less implanted electronic devices for pre-MRI safety screening: realized implementation needs and proposed operational solutions.

Purpose: Chest X-ray (CXR) use in pre-MRI safety screening, such as for lead-less implanted electronic device (LLIED) recognition, is common. To assist CXR interpretation, we "pre-deployed" an artificial intelligence (AI) model to assess (1) accuracies in LLIED-type (and consequently safety-level) identification, (2) safety implications of LLIED nondetections or misidentifications, (3) infrastructural or workflow requirements, and (4) demands related to model adaptation to real-world conditions.

Approach: A two-tier cascading methodology for LLIED detection/localization and identification on a frontal CXR was applied to evaluate the performance of the original nine-class AI model.

Trends and Predictors of Imaging Utilization by Modality within an Academic Health System's Active Patient Population.

Rationale And Objectives: Evaluate trends and demographic predictors of imaging utilization at a university-affiliated health system.

Materials And Methods: In this single-institution retrospective study, per capita estimates of imaging utilization among patients active in the health system were computed by cross-referencing all clinical encounters (2004-2016) for 1,628,980 unique patients with a listing of 6,157,303 diagnostic radiology encounters. Time trends in imaging utilization and effects of gender, race/ethnicity, and age were assessed, with subgroup analyses performed by imaging modality.

A Pilot Randomized Trial of Transdermal Nicotine for Pulmonary Sarcoidosis.

Background: Tobacco smoking is associated with a reduced risk of developing sarcoidosis, and we previously reported that nicotine normalizes immune responses to environmental antigens in patients with active pulmonary sarcoidosis. The effects of nicotine on the progression of pulmonary sarcoidosis are unknown.

Research Question: Is nicotine treatment well tolerated, and will it improve lung function in patients with active pulmonary sarcoidosis?

Study Design And Methods: With local institutional review board approval, a randomized, double-blind, controlled pilot trial was conducted of daily nicotine transdermal patch treatment (21 mg daily) or placebo patch use for 24 weeks.

Constrained generative adversarial network ensembles for sharable synthetic medical images.

Sharing medical images between institutions, or even inside the same institution, is restricted by various laws and regulations; research projects requiring large datasets may suffer as a result. These limitations might be addressed by an abundant supply of synthetic data that (1) are representative (i.e.

Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis During CCTA Evaluation of Chest Pain in the Emergency Department: Preparing an Application for Real-world Use.

Coronary computed tomography angiography (CCTA) evaluation of chest pain patients in an emergency department (ED) is considered appropriate. While a "negative" CCTA interpretation supports direct patient discharge from an ED, labor-intensive analyses are required, with accuracy in jeopardy from distractions. We describe the development of an artificial intelligence (AI) algorithm and workflow for assisting qualified interpreting physicians in CCTA screening for total absence of coronary atherosclerosis.

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses).

Predicting Mental Decline Rates in Mild Cognitive Impairment From Baseline MRI Volumetric Data.

Purpose: In mild cognitive impairment (MCI), identifying individuals at high risk for progressive cognitive deterioration can be useful for prognostication and intervention. This study quantitatively characterizes cognitive decline rates in MCI and tests whether volumetric data from baseline magnetic resonance imaging (MRI) can predict accelerated cognitive decline.

Methods: The authors retrospectively examined Alzheimer Disease Neuroimaging Initiative data to obtain serial Mini-Mental Status Exam (MMSE) scores, diagnoses, and the following baseline MRI volumes: total intracranial volume, whole-brain and ventricular volumes, and volumes of the hippocampus, entorhinal cortex, fusiform gyrus, and medial temporal lobe.

Predicting rate of cognitive decline at baseline using a deep neural network with multidata analysis.

Our study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired patients by processing only the clinical and imaging data collected at the initial visit. We built a predictive model based on a supervised hybrid neural network utilizing a three-dimensional convolutional neural network to perform volume analysis of magnetic resonance imaging (MRI) and integration of nonimaging clinical data at the fully connected layer of the architecture. The experiments are conducted on the Alzheimer's Disease Neuroimaging Initiative dataset.

Bone grafting history affects soft tissue healing following implant placement.

Background: This study aimed to determine and compare soft tissue healing outcomes following implant placement in grafted (GG) and non-grafted bone (NGG).

Methods: Patients receiving single implant in a tooth-bound maxillary non-molar site were recruited. Clinical healing was documented.

Automated coronary artery atherosclerosis detection and weakly supervised localization on coronary CT angiography with a deep 3-dimensional convolutional neural network.

We propose a fully automated algorithm based on a deep learning framework enabling screening of a coronary computed tomography angiography (CCTA) examination for confident detection of the presence or absence of coronary artery atherosclerosis. The system starts with extracting the coronary arteries and their branches from CCTA datasets and representing them with multi-planar reformatted volumes; pre-processing and augmentation techniques are then applied to increase the robustness and generalization ability of the system. A 3-dimensional convolutional neural network (3D-CNN) is utilized to model pathological changes (e.

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI.

Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection remains challenging partly due to their structural similarities to normal structures (e.

Integrating AI into radiology workflow: levels of research, production, and feedback maturity.

We present a roadmap for integrating artificial intelligence (AI)-based image analysis algorithms into existing radiology workflows such that (1) radiologists can significantly benefit from enhanced automation in various imaging tasks due to AI, and (2) radiologists' feedback is utilized to further improve the AI application. This is achieved by establishing three maturity levels where (1) research enables the visualization of AI-based results/annotations by radiologists without generating new patient records; (2) production allows the AI-based system to generate results stored in an institution's picture-archiving and communication system; and (3) feedback equips radiologists with tools for editing the AI inference results for periodic retraining of the deployed AI systems, thereby allowing continuous organic improvement of AI-based radiology-workflow solutions. A case study (i.

A User Interface for Optimizing Radiologist Engagement in Image Data Curation for Artificial Intelligence.

Purpose: To delineate image data curation needs and describe a locally designed graphical user interface (GUI) to aid radiologists in image annotation for artificial intelligence (AI) applications in medical imaging.

Materials And Methods: GUI components support image analysis toolboxes, picture archiving and communication system integration, third-party applications, processing of scripting languages, and integration of deep learning libraries. For clinical AI applications, GUI components included two-dimensional segmentation and classification; three-dimensional segmentation and quantification; and three-dimensional segmentation, quantification, and classification.

Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation, Photometric Conversion, or Transfer Learning.

Collecting and curating large medical-image datasets for deep neural network (DNN) algorithm development is typically difficult and resource-intensive. While transfer learning (TL) decreases reliance on large data collections, current TL implementations are tailored to two-dimensional (2D) datasets, limiting applicability to volumetric imaging (e.g.

Diagnostic Accuracy of Digital Breast Tomosynthesis in the Evaluation of Palpable Breast Abnormalities.

Rationale And Objectives: The role of digital breast tomosynthesis (DBT) in evaluating palpable abnormalities has not been evaluated and its accuracy compared to 2D mammography is unknown. The purpose of this study was to evaluate combined 2D mammography, DBT, and ultrasound (US) at palpable sites.

Materials And Methods: Two breast imagers reviewed blinded consecutive cases with combined 2D mammograms and DBT examinations performed for palpable complaints.

Radiology and Enterprise Medical Imaging Extensions (REMIX).

Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of "big imaging data," as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence.

Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.

Purpose To evaluate the performance of an artificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or hydrocephalus (HMH) at non-contrast material-enhanced head computed tomographic (CT) examinations and to determine algorithm performance for detection of suspected acute infarct (SAI). Materials and Methods This HIPAA-compliant retrospective study was completed after institutional review board approval. A training and validation dataset of noncontrast-enhanced head CT examinations that comprised 100 examinations of HMH, 22 of SAI, and 124 of noncritical findings was obtained resulting in 2583 representative images.

Gingival Perfusion and Tissue Biomarkers During Early Healing of Postextraction Regenerative Procedures: A Prospective Case Series.

Background: Postextraction alveolar bone loss, mostly affecting the buccal plate, occurs despite regenerative procedures. To better understand possible determinants, this prospective case series assesses gingival blood perfusion and tissue molecular responses in relation to postextraction regenerative outcomes.

Methods: Adults scheduled to receive bone grafting in maxillary, non-molar, single-tooth extraction sites were recruited.

CT Gray-Level Texture Analysis as a Quantitative Imaging Biomarker of Epidermal Growth Factor Receptor Mutation Status in Adenocarcinoma of the Lung.

Objective: The purpose of this study was to investigate the radiogenomic correlation between CT gray-level texture features and epidermal growth factor receptor (EGFR) mutation status in adenocarcinoma of the lung.

Materials And Methods: This retrospective study included 25 patients with exon 19 short inframe deletion (exon 19) and 21 patients with exon 21 L858R point (exon 21) EGFR mutations among 125 patients with EGFR mutant adenocarcinoma of the lung. The randomly formed control group consisted of 20 patients selected from 126 patients with EGFR mutation-negative (wild-type) adenocarcinomas.

Outcomes of benign breast papillomas diagnosed at image-guided vacuum-assisted core needle biopsy.

Purpose: To determine the upgrade rate of benign papillomas diagnosed at image-guided vacuum-assisted core needle biopsy (VACNB) and to compare our results with the summarized literature.

Materials And Methods: A database search was performed to identify patients older than 18 years of age with benign papillomas diagnosed at VACNB between 2004 and 2013. A total of 199 papillomas in 184 patients were identified.

Quantitative computerized two-point correlation analysis of lung CT scans correlates with pulmonary function in pulmonary sarcoidosis.

Background: Chest CT scans are commonly used to clinically assess disease severity in patients presenting with pulmonary sarcoidosis. Despite their ability to reliably detect subtle changes in lung disease, the utility of chest CT scans for guiding therapy is limited by the fact that image interpretation by radiologists is qualitative and highly variable. We sought to create a computerized CT image analysis tool that would provide quantitative and clinically relevant information.

Unexpectedly high prevalence of sarcoidosis in a representative U.S. Metropolitan population.

The prevalence of sarcoidosis in the United States is unknown, with estimates ranging widely from 1 to 40 per 100,000. We sought to determine the prevalence of sarcoidosis in our health system compared to other rare lung diseases and to further establish if the prevalence was changing over time. We interrogated the electronic medical records of all patients treated in our health system from 1995 to 2010 (1.