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Deep Learning–based Detection of Intravenous Contrast Material on CT Scans

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"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Identifying intravenous (IV) contrast within CT scans is an important component of data curation for medical imaging-based, artificial intelligence (AI) model development and deployment. IV contrast is oftenpoorly documented in imagingmetadata, necessitating impractical ma nual annotation by clinician experts.


Deep Learning for Radiographic Measurement of Femoral Component Subsidence Following Total Hip Arthroplasty

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"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Femoral component subsidence following total hip arthroplasty (THA) is a worrisome radiographic finding. This study developed and evaluated a deep learning tool to automatically quantify femoral component subsidence between two serial anteroposterior (AP) hip radiographs.


AI with Statistical Confidence Scores for Detection of Acute/Subacute Hemorrhage in Noncontrast Head CT Scans

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"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To present a method that automatically detects, subtypes and locates acute/subacute intracranial hemorrhage (ICH) on noncontrast head CT (NCCT) and generates detection confidence scores to identify high-confidence data subsets with higher accuracy and improve radiologic worklist prioritization. Such scores may enable clinicians to better use AI tools.


Deep Learning–based Automatic Lung Segmentation on Multiresolution CT Scans from Healthy and Fibrotic Lungs in Mice

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To develop a model to accurately segment mouse lungs with varying levels of fibrosis and investigate its applicability to mouse images with different resolutions. In this experimental retrospective study, a U-Net was trained to automatically segment lungs on mouse CT images. The model was trained (n 1200), validated (n 300), and tested (n 154) on longitudinally acquired and semiautomatically segmented CT images, which included both healthy and irradiated mice (group A). A second independent group of 237 mice (group B) was used for external testing. The Dice score coefficient (DSC) and Hausdorff distance (HD) were used as metrics to quantify segmentation accuracy. Transfer learning was applied to adapt the model to high-spatial-resolution mouse micro-CT segmentation (n 20; group C [n 16 for training and n 4 for testing]). Spatially resolved quantification of differences toward reference masks revealed two hot spots close to the air-tissue interfaces, which are particularly prone to deviation.


Evaluating the Performance of a Convolutional Neural Network Algorithm for Measuring Thoracic Aortic Diameters in a Heterogeneous Population

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The purpose of this work was to assess the performance of a convolutional neural network (CNN) for automatic thoracic aortic measurements in a heterogeneous population. From June 2018 to May 2019, this study retrospectively analyzed 250 chest CT scans with or without contrast enhancement and electrocardiographic gating from a heterogeneous population with or without aortic pathologic findings. Aortic diameters at nine locations and maximum aortic diameter were measured manually and with an algorithm (Artificial Intelligence Rad Companion Chest CT prototype, Siemens Healthineers) by using a CNN. A total of 233 examinations performed with 15 scanners from three vendors in 233 patients (median age, 65 years [IQR, 54–72 years]; 144 men) were analyzed: 68 (29%) without pathologic findings, 72 (31%) with aneurysm, 51 (22%) with dissection, and 42 (18%) with repair. No evidence of a difference was observed in maximum aortic diameter between manual and automatic measurements (P .48).


@Radiology_AI

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Over the last several years, artificial intelligence (AI) has become one of the highest profile topics in radiology, recognized in part by the creation of this journal (1). This focus and interest has been driven largely by the potential AI shows to broadly change the way we practice radiology across every subspecialty. That potential has been demonstrated by a flood of manuscripts describing technical advances, algorithms, and proofs of concept aimed at a wide variety of radiologic tasks. However, no amount of demonstrated potential has a direct impact on patient care or clinical practice; achieving such an impact requires moving beyond the creation of AI to the deployment of AI into clinical environments for routine use. It is probably not surprising to those who practice radiology or work in radiology information technology that achieving this translational goal is challenging and has occurred at a much slower pace than suggested by some who feverishly predicted that AI would bring an end to radiology as a profession in a few short years.


@Radiology_AI

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Many noninterpretive artificial intelligence applications with the potential to improve multiple aspects of radiology practice, including workflow, efficiency, image acquisition, reporting, billing, and education, are either currently available or in development. Artificial intelligence (AI) models to improve workflow efficiency and safety include automated clinical decision support, study protocoling, examination scheduling, and worklist prioritization. Models to improve image acquisition focus on patient positioning, multimodal image registration, dose reduction, noise reduction, and artifact reduction. Models to improve reporting include automatic finding categorization using classification systems (eg, Breast Imaging Reporting and Data System, Liver Imaging Reporting and Data System), provider notification of incidental findings, and closing the loop on patient follow-up. Business applications include automated billing and coding, obtaining preauthorization, and optimization of performance on quality measures to increase reimbursement. Use of AI in resident education is somewhat controversial, but AI can be used to help flag high-risk cases for faster review by an attending physician, customize teaching files based on residents' needs, and help improve resident reporting. The radiology community has had a leading role in exploring medical applications of artificial intelligence (AI), and one of the primary drivers for this is the desire for increased accuracy and efficiency in clinical care. Radiologist responsibilities extend beyond image interpretation. AI tools have the potential to improve essential tasks in the imaging value chain, from image acquisition to generating and disseminating radiology reports (1). These applications are crucial in current medical environments with increasing workloads, increasing scan complexity, and the need to decrease costs and reduce errors (2–4).


@Radiology_AI

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CT pulmonary angiography (CTPA) is the first-line imaging test for evaluation of acute pulmonary emboli. However, diagnostic quality is heterogeneous across institutions and is frequently limited by suboptimal pulmonary artery (PA) contrast enhancement. In this retrospective study, a deep learning algorithm for measuring enhancement of the central PAs was developed and assessed for feasibility of its use in quality improvement of CTPA. In a convenience sample of 450 patients, automated measurement of CTPA enhancement showed high agreement with manual radiologist measurement (r 0.996). Using a threshold of less than 250 HU for suboptimal enhancement, the sensitivity and specificity of the automated classification were 100% and 99.5%, respectively.


The Scope Of Computer Vision In Nuclear Medicine

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The inclusion of technologies such as AI and computer vision in healthcare can greatly enhance high-precision applications like nuclear medicine. Nuclear medicine is a subfield of radiology that involves the use of minute amounts of radiation and radiation-based medicines, known as radiopharmaceuticals, to evaluate the composition and functioning of bones and tissue in patients. Today, nuclear medicine and radiology are prominent components of modern healthcare, especially for cancer diagnosis and treatment. X-rays and CT scans are some of the methods that involve radiation usage in healthcare. The use of powerful radiation beams to inhibit the growth of tumors in cancer patients is also a common healthcare application.


Deep Learning Is Hitting a Wall

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Let me start by saying a few things that seem obvious," Geoffrey Hinton, "Godfather" of deep learning, and one of the most celebrated scientists of our time, told a leading AI conference in Toronto in 2016. "If you work as a radiologist you're like the coyote that's already over the edge of the cliff but hasn't looked down." Deep learning is so well-suited to reading images from MRIs and CT scans, he reasoned, that people should "stop training radiologists now" and that it's "just completely obvious within five years deep learning is going to do better." Fast forward to 2022, and not a single radiologist has been replaced. Rather, the consensus view nowadays is that machine learning for radiology is harder than it looks1; at least for now, humans and machines complement each other's strengths.2 Deep learning is at its best when all we need are rough-ready results. Few fields have been more filled with hype and bravado than artificial intelligence. It has flitted from fad to fad decade ...