Integration and evaluation of chest X-ray artificial intelligence in clinical practice.

Purpose: To integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice.

Approach: In clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow.

Translating AI to Clinical Practice: Overcoming Data Shift with Explainability.

To translate artificial intelligence (AI) algorithms into clinical practice requires generalizability of models to real-world data. One of the main obstacles to generalizability is data shift, a data distribution mismatch between model training and real environments. Explainable AI techniques offer tools to detect and mitigate the data shift problem and develop reliable AI for clinical practice.

Cost drivers in endovascular pulmonary embolism interventions.

Aim: To determine the costs associated with endovascular pulmonary embolism (PE) interventions.

Materials And Methods: Procedural costs were determined utilising time-driven activity-based costing (TDABC). A multidisciplinary team created process maps describing personnel, space, equipment, materials, and time required for each procedural step.

Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets.

Objective: Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically.

Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks.

Rationale And Objectives: To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool.

Materials And Methods: A retrospective data set including 2000 de-identified images from intensive care unit patients was split into 1488 for training and 512 for testing. AI was developed to automatically identify the ETT, trachea, and carina using semantically embedded neural networks that combine a declarative knowledge base with deep neural networks.

Republished: Robotic assistant spinal angiography: a case report and technical considerations.

Robotic-assisted technology has shown to be promising in coronary and peripheral vascular interventions. Early case reports have also demonstrated its efficacy in neuro-interventions. However, there is no prior report demonstrating use of the robotic-assisted platform for spinal angiography.

Electronic Health Record-Integrated Tumor Board Application to Save Preparation Time and Reduce Errors.

Purpose: Multidisciplinary oncology meetings, or tumor boards (TBs), ensure and facilitate communication between specialties regarding the management of cancer cases to improve patient care. The organization of TB and the preparation and presentation of patient cases are typically inefficient processes that require the exchange of patient information via e-mail, the hunting for data and images in the electronic health record, and the copying and pasting of patient data into desktop presentation software.

Methods: We implemented a standards-based electronic health record-integrated application that automated several aspects of TB organization and preparation.

Sodium MR Neuroimaging.

Sodium MR imaging has the potential to complement routine proton MR imaging examinations with the goal of improving diagnosis, disease characterization, and clinical monitoring in neurologic diseases. In the past, the utility and exploration of sodium MR imaging as a valuable clinical tool have been limited due to the extremely low MR signal, but with recent improvements in imaging techniques and hardware, sodium MR imaging is on the verge of becoming clinically realistic for conditions that include brain tumors, ischemic stroke, and epilepsy. In this review, we briefly describe the fundamental physics of sodium MR imaging tailored to the neuroradiologist, focusing on the basics necessary to understand factors that play into making sodium MR imaging feasible for clinical settings and describing current controversies in the field.

Robotic assistant spinal angiography: a case report and technical considerations.

Robotic-assisted technology has shown to be promising in coronary and peripheral vascular interventions. Early case reports have also demonstrated its efficacy in neuro-interventions. However, there is no prior report demonstrating use of the robotic-assisted platform for spinal angiography.

Federated learning improves site performance in multicenter deep learning without data sharing.

Objective: To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).

Materials And Methods: Deep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions.

Results: We found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset.

Angular needle tracker and stabilizer for image-guided interventions.

Introduction: Minimally invasive image-guided interventions have changed the face of procedural medicine. For these procedures, safety and efficacy depend on precise needle placement. Needle targeting devices help improve the accuracy of needle placement, but their use has not seen broad penetration.

Continuous Learning AI in Radiology: Implementation Principles and Early Applications.

Artificial intelligence (AI) is becoming increasingly present in radiology and health care. This expansion is driven by the principal AI strengths: automation, accuracy, and objectivity. However, as radiology AI matures to become fully integrated into the daily radiology routine, it needs to go beyond replicating static models, toward discovering new knowledge from the data and environments around it.

Early-Stage Radiology Volume Effects and Considerations with the Coronavirus Disease 2019 (COVID-19) Pandemic: Adaptations, Risks, and Lessons Learned.

Objective: The coronavirus disease 2019 (COVID-19) pandemic resulted in significant loss of radiologic volume as a result of shelter-at-home mandates and delay of non-time-sensitive imaging studies to preserve capacity for the pandemic. We analyze the volume-related impact of the COVID-19 pandemic on six academic medical systems (AMSs), three in high COVID-19 surge (high-surge) and three in low COVID-19 surge (low-surge) regions, and a large national private practice coalition. We sought to assess adaptations, risks of actions, and lessons learned.

Prostate Microstructure in Prostate Cancer Using 3-T MRI with Diffusion-Relaxation Correlation Spectrum Imaging: Validation with Whole-Mount Digital Histopathology.

Background Microstructural MRI has the potential to improve diagnosis and characterization of prostate cancer (PCa), but validation with histopathology is lacking. Purpose To validate ex vivo diffusion-relaxation correlation spectrum imaging (DR-CSI) in the characterization of microstructural tissue compartments in prostate specimens from men with PCa by using registered whole-mount digital histopathology (WMHP) as the reference standard. Materials and Methods Men with PCa who underwent 3-T MRI and robotic-assisted radical prostatectomy between June 2018 and January 2019 were prospectively studied.

Radiology's Information Architecture Could Migrate to One Emulating That of Smartphones.

Diagnostic radiology (DxR), having had successful serial co-evolutions with imaging equipment and PACS, is faced with another. With a backdrop termed "globotics transition," it should create an IT and informatics infrastructure capable of integrating artificial intelligence (AI) into current critical communication functions of PACS and incorporating functions currently residing in balkanized products. DxR will face the challenge of adopting sustaining and disruptive AI innovations simultaneously.

Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet.

Multi-parametric MRI (mp-MRI) is considered the best non-invasive imaging modality for diagnosing prostate cancer (PCa). However, mp-MRI for PCa diagnosis is currently limited by the qualitative or semi-quantitative interpretation criteria, leading to inter-reader variability and a suboptimal ability to assess lesion aggressiveness. Convolutional neural networks (CNNs) are a powerful method to automatically learn the discriminative features for various tasks, including cancer detection.

Deep transfer learning-based prostate cancer classification using 3 Tesla multi-parametric MRI.

Purpose: The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI (3T mp-MRI) with whole-mount histopathology (WMHP) validation.

Methods: With IRB approval, 140 patients with 3T mp-MRI and WMHP comprised the study cohort. The DTL-based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients.

A system using patient-specific 3D-printed molds to spatially align in vivo MRI with ex vivo MRI and whole-mount histopathology for prostate cancer research.

Background: Patient-specific 3D-printed molds and ex vivo MRI of the resected prostate have been two important strategies to align MRI with whole-mount histopathology (WMHP) for prostate cancer (PCa) research, but the combination of these two strategies has not been systematically evaluated.

Purpose: To develop and evaluate a system that combines patient-specific 3D-printed molds with ex vivo MRI (ExV) to spatially align in vivo MRI (InV), ExV, and WMHP in PCa patients.

Study Type: Prospective cohort study.

Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate.

Purpose: We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer.

Materials And Methods: In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors.

Bits and bytes: the future of radiology lies in informatics and information technology.

Unlabelled: Advances in informatics and information technology are sure to alter the practice of medical imaging and image-guided therapies substantially over the next decade. Each element of the imaging continuum will be affected by substantial increases in computing capacity coincident with the seamless integration of digital technology into our society at large. This article focuses primarily on areas where this IT transformation is likely to have a profound effect on the practice of radiology.