Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation

Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5-7% (up to 30%). Artificial intelligence (AI) systems have the capacity to improve diagnostic accuracy and workflow efficiency when integrated into clinical decision pathways. Previous studies have evaluated AI systems against physicians, whereas we aim to compare the benefits of incorporating AI into their final diagnostic decision. This work analyzed the potential for artificial intelligence (AI)-based decision support systems to improve physician accuracy, variability, and efficiency. The decision support system (DSS) assessed was Koios DS, which provides automated sonographic nodule descriptor predictions and a direct cancer risk assessment aligned to ACR TI-RADS. The study was conducted retrospectively between (08/2020) and (10/2020). The set of cases used included 650 patients (21% male, 79% female) of age 53 ± 15. Fifteen physicians assessed each of the cases in the set, both unassisted and aided by the DSS. The order of the reading condition was randomized, and reading blocks were separated by a period of 4 weeks. The system's impact on reader accuracy was measured by comparing the area under the ROC curve (AUC), sensitivity, and specificity of readers with and without the DSS with FNA as ground truth. The impact on reader variability was evaluated using Pearson's correlation coefficient. The impact on efficiency was determined by comparing the average time per read. There was a statistically significant increase in average AUC of 0.083 [0.066, 0.099] and an increase in sensitivity and specificity of 8.4% [5.4%, 11.3%] and 14% [12.5%, 15.5%], respectively, when aided by Koios DS. The average time per case decreased by 23.6% (p = 0.00017), and the observed Pearson's correlation coefficient increased from r = 0.622 to r = 0.876 when aided by Koios DS. These results indicate that providing physicians with automated clinical decision support significantly improved diagnostic accuracy, as measured by AUC, sensitivity, and specificity, and reduced inter-reader variability and interpretation times.

Ontologies in the New Computational Age of Radiology: RadLex for Semantics and Interoperability in Imaging Workflows

From basic research to the bedside, precise terminology is key to advancing medicine and ensuring optimal and appropriate patient care. However, the wide spectrum of diseases and their manifestations superimposed on medical team-specific and discipline-specific communication patterns often impairs shared understanding and the shared use of common medical terminology. Common terms are currently used in medicine to ensure interoperability and facilitate integration of biomedical information for clinical practice and emerging scientific and educational applications alike, from database integration to supporting basic clinical operations such as billing. Such common terminologies can be provided in ontologies, which are formalized representations of knowledge in a particular domain. Ontologies unambiguously specify common concepts and describe the relationships between those concepts by using a form that is mathematically precise and accessible to humans and machines alike. RadLex^® is a key RSNA initiative that provides a shared domain model, or ontology, of radiology to facilitate integration of information in radiology education, clinical care, and research. As the contributions of the computational components of common radiologic workflows continue to increase with the ongoing development of big data, artificial intelligence, and novel image analysis and visualization tools, the use of common terminologies is becoming increasingly important for supporting seamless computational resource integration across medicine. This article introduces ontologies, outlines the fundamental semantic web technologies used to create and apply RadLex, and presents examples of RadLex applications in everyday radiology and research. It concludes with a discussion of emerging applications of RadLex, including artificial intelligence applications. ^© RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Imaging Informatics Fellowship Curriculum: Building Consensus on the Most Critical Topics and the Future of the Informatics Fellowship

The existing fellowship imaging informatics curriculum, established in 2004, has not undergone formal revision since its inception and inaccurately reflects present-day radiology infrastructure. It insufficiently equips trainees for today's informatics challenges as current practices require an understanding of advanced informatics processes and more complex system integration. We sought to address this issue by surveying imaging informatics fellowship program directors across the country to determine the components and cutline for essential topics in a standardized imaging informatics curriculum, the consensus on essential versus supplementary knowledge, and the factors individual programs may use to determine if a newly developed topic is an essential topic. We further identified typical program structural elements and sought fellowship director consensus on offering official graduate trainee certification to imaging informatics fellows. Here, we aim to provide an imaging informatics fellowship director consensus on topics considered essential while still providing a framework for informatics fellowship programs to customize their individual curricula.

Pancreaticoduodenal Groove: Spectrum of Disease and Imaging Features

The pancreaticoduodenal groove (PDG) is a small space between the pancreatic head and duodenum where vital interactions between multiple organs and physiologic processes take place. Muscles, nerves, and hormones perform a coordinated dance, allowing bile and pancreatic enzymes to aid in digestion and absorption of critical nutrition. Given the multitude of organs and cells working together, a variety of benign and malignant entities can arise in or adjacent to this space. Management of lesions in this region is also complex and can involve observation, endoscopic resection, or challenging surgeries such as the Whipple procedure. The radiologist plays an important role in evaluation of abnormalities involving the PDG. While CT is usually the first-line examination for evaluation of this complex region, MRI offers complementary information. Although features of abnormalities involving the PDG can often overlap, understanding the characteristic imaging and pathologic features generally allows categorization of disease entities based on the suspected organ of origin and the presence of ancillary features. The goal of the authors is to provide radiologists with a conceptual approach to entities implicating the PDG to increase the accuracy of diagnosis and assist in appropriate management or presurgical planning. They briefly discuss the anatomy of the PDG, followed by a more in-depth presentation of the features of disease categories. A table summarizing the entities that occur in this region by underlying cause and anatomic location is provided. ^©RSNA, 2022.

Biomedical Ontologies to Guide AI Development in Radiology

The advent of deep learning has engendered renewed and rapidly growing interest in artificial intelligence (AI) in radiology to analyze images, manipulate textual reports, and plan interventions. Applications of deep learning and other AI approaches must be guided by sound medical knowledge to assure that they are developed successfully and that they address important problems in biomedical research or patient care. To date, AI has been applied to a limited number of real-world radiology applications. As AI systems become more pervasive and are applied more broadly, they will benefit from medical knowledge on a larger scale, such as that available through computer-based approaches. A key approach to represent computer-based knowledge in a particular domain is an ontology. As defined in informatics, an ontology defines a domain's terms through their relationships with other terms in the ontology. Those relationships, then, define the terms' semantics, or "meaning." Biomedical ontologies commonly define the relationships between terms and more general terms, and can express causal, part-whole, and anatomic relationships. Ontologies express knowledge in a form that is both human-readable and machine-computable. Some ontologies, such as RSNA's RadLex radiology lexicon, have been applied to applications in clinical practice and research, and may be familiar to many radiologists. This article describes how ontologies can support research and guide emerging applications of AI in radiology, including natural language processing, image-based machine learning, radiomics, and planning.

Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial

Background Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice. Purpose To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists' signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center- and radiologist-level effects was used to compare the two experimental groups. Results Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001). Conclusion Use of an artificial intelligence algorithm improved skeletal age assessment accuracy and reduced interpretation times for radiologists, although differences were observed between centers. Clinical trial registration no. NCT03530098 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

Cinebot: Creation of Movies and Animated GIFs Directly from PACS-Efficiency in Presentation and Education

The presentation of radiology exams can be enhanced through the use of dynamic images. Dynamic images differ from static images by the use of animation and are especially useful for depicting real-time activity such as the scrolling or the flow of contrast to enhance pathology. This is generally superior to a collection of static images as a representation of clinical workflow and provides a more robust appreciation of the case in question. Dynamic images can be shared electronically to facilitate teaching, case review, presentation, and sharing of interesting cases to be viewed in detail on a computer or mobile devices for education. The creation of movies or animated images from radiology data has traditionally been challenging based on technological limitations inherent in converting the Digital Imaging and Communications in Medicine (DICOM) standard to other formats or concerns related to the presence of protected health information (PHI). The solution presented here, named Cinebot, allows a simple "one-click" generation of anonymized dynamic movies or animated images within the picture archiving and communication system (PACS) workflow. This approach works across all imaging modalities, including stacked cross-sectional and multi-frame cine formats. Usage statistics over 2 years have shown this method to be well-received and useful throughout our enterprise.

Automated coronary calcium scoring using deep learning with multicenter external validation

Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = -2.86; Cohen's Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT exams with validation on external datasets (total n = 303) obtained from four geographically disparate health systems. On identifying patients with any CAC (i.e., CAC ≥ 1), sensitivity and PPV was high across all datasets (ranges: 80-100% and 87-100%, respectively). For CAC ≥ 100 on routine non-gated chest CTs, which is the latest recommended threshold to initiate statin therapy, our model showed sensitivities of 71-94% and positive predictive values in the range of 88-100% across all the sites. Adoption of this model could allow more patients to be screened with CAC scoring, potentially allowing opportunistic early preventive interventions.

Radiology Reporting in the Era of Patient-Centered Care: How Can We Improve Readability?

Radiology reports are consumed not only by referring physicians and healthcare providers, but also by patients. We assessed report readability in our enterprise and implemented a two-part quality improvement intervention with the goal of improving report accessibility. A total of 491,813 radiology reports from ten hospitals within the enterprise from May to October, 2018 were collected. We excluded echocardiograms, rehabilitation reports, administrator reports, and reports with negative scores leaving 461,219 reports and report impressions for analysis. A grade level (GL) was calculated for each report and impression by averaging four readability metrics. Next, we conducted a readability workshop and distributed weekly emails with readability GLs over a period of 6 months to each attending radiologist at our primary institution. Following this intervention, we utilized the same exclusion criteria and analyzed 473,612 reports from May to October, 2019. The mean GL for all reports and report impressions was above 13 at every hospital in the enterprise. Following our intervention, a statistically significant drop in GL for reports and impressions was demonstrated at all locations, but a larger and significant improvement was observed in impressions at our primary site. Radiology reports across the enterprise are written at an advanced reading level making them difficult for patients and their families to understand. We observed a significantly larger drop in GL for impressions at our primary site than at all other sites following our intervention. Radiologists at our home institution improved their report readability after becoming more aware of their writing practices.

Developing Deeper Radiology Exam Insight to Optimize MRI Workflow and Patient Experience

Process variability during the acquisition of magnetic resonance imaging (MRI) can lengthen examination times and introduce unexpected exam differences which can negatively impact the cost and quality of care provided to patients. Digital Imaging and Communications in Medicine (DICOM) metadata can provide more accurate study data and granular series-level information that can be used to increase operational efficiency, optimize patient care, and reduce costs associated with MRI examinations. Systematic use of such data analysis could be used as a continuous operational optimization and quality control mechanism.

The Radiologist's Gaze: Mapping Three-Dimensional Visual Search in Computed Tomography of the Abdomen and Pelvis

A radiologist's search pattern can directly influence patient management. A missed finding is a missed opportunity for intervention. Multiple studies have attempted to describe and quantify search patterns but have mainly focused on chest radiographs and chest CTs. Here, we describe and quantify the visual search patterns of 17 radiologists as they scroll through 6 CTs of the abdomen and pelvis. Search pattern tracings varied among individuals and remained relatively consistent per individual between cases. Attendings and trainees had similar eye metric statistics with respect to time to first fixation (TTFF), number of fixations in the region of interest (ROI), fixation duration in ROI, mean saccadic amplitude, or total number of fixations. Attendings had fewer numbers of fixations per second versus trainees (p < 0.001), suggesting efficiency due to expertise. In those cases that were accurately interpreted, TTFF was shorter (p = 0.04), the number of fixations per second and number of fixations in ROI were higher (p = 0.04, p = 0.02, respectively), and fixation duration in ROI was increased (p = 0.02). We subsequently categorized radiologists as "scanners" or "drillers" by both qualitative and quantitative methods and found no differences in accuracy with most radiologists being categorized as "drillers." This study describes visual search patterns of radiologists in interpretation of CTs of the abdomen and pelvis to better approach future endeavors in determining the effects of manipulations such as fatigue, interruptions, and computer-aided detection.

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT

An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings between ontologies enable the reuse and interoperability of biomedical knowledge. We sought to map concepts of the Radiology Gamuts Ontology (RGO), an ontology that links diseases and imaging findings to support differential diagnosis in radiology, to terms in three key vocabularies for clinical radiology: the International Classification of Diseases, version 10, Clinical Modification (ICD-10-CM), the Radiological Society of North America's radiology lexicon (RadLex), and the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). RGO (version 0.7; Jan 2018) incorporated 16,918 terms (classes) for diseases, interventions, and imaging observations linked by 1782 subsumption (class-subclass) relations and 55,569 causal ("may cause") relations. RGO classes were mapped to RadLex (46,656 classes, version 3.15), SNOMED CT (347,358 classes, version 2018AA), and ICD-10-CM (94,645 classes, version 2018AA) using the National Center for Biomedical Ontology (NCBO) Annotator web service. We identified 1275 exact mappings from RGO to RadLex, 5302 to SNOMED CT, and 941 to ICD-10-CM. RGO terms mapped to one ontology (n = 3401), two ontologies (n = 1515), or all three ontologies (n = 198). The mapped ontologies provide additional terms to support data mining from textual information in the electronic health record. The current work builds on efforts to map RGO to ontologies of diseases and phenotypes. Mappings between ontologies can support automated knowledge discovery, diagnostic reasoning, and data mining.

The Case for User-Centered Artificial Intelligence in Radiology

Past technology transition successes and failures have demonstrated the importance of user-centered design and the science of human factors; these approaches will be critical to the success of artificial intelligence in radiology.

A Survey of Imaging Informatics Fellowships and Their Curricula: Current State Assessment

In a 2016 survey of imaging informatics ("II") fellowship graduates, the surveyed fellowship graduates expressed the "opinion that II fellowships needed further formalization and standardization" Liao et al. (J Digit Imaging, 2016). This, coupled with the fact that the original published "standardized" curriculum is about 15 years out of date in our rapidly changing systems, suggests an opportunity for curriculum improvement. Before agreeing on improved structural and content suggestions for fellowships, we completed a current-state assessment of how each fellowship organizes its education and what requirements each have for fellowship completion. In this work, we aimed to collect existing information about imaging informatics fellowship curricula by contacting institutions across the country. A survey was completed by phone with the fellowship directors of existing imaging informatics fellowships across the country. Additionally, we collected existing documentation that outlines the curricula currently in use at institutions. We reviewed both the interview responses and documentation to assess overlapping trends and institutional differences in curriculum structure and content. All fellowships had suggested reading lists, didactic lectures, and a required project for each fellow. There were required practicum activities or teaching experience each in two fellowships, and one fellowship had a mandatory certification requirement for graduation. Curriculum topics in Technical Informatics or Business and Management were covered by a majority of institutions, while Quality and Safety and Research topics had inconsistent coverage across fellowships. Our plan is to reengage II fellowship directors to develop a core curriculum, which is part of the Society of Imaging Informatics in Medicine strategic plan.

Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset

Pneumothorax is a potentially life-threatening condition that requires prompt recognition and often urgent intervention. In the ICU setting, large numbers of chest radiographs are performed and must be interpreted on a daily basis which may delay diagnosis of this entity. Development of artificial intelligence (AI) techniques to detect pneumothorax could help expedite detection as well as localize and potentially quantify pneumothorax. Open image analysis competitions are useful in advancing state-of-the art AI algorithms but generally require large expert annotated datasets. We have annotated and adjudicated a large dataset of chest radiographs to be made public with the goal of sparking innovation in this space. Because of the cumbersome and time-consuming nature of image labeling, we explored the value of using AI models to generate annotations for review. Utilization of this machine learning annotation (MLA) technique appeared to expedite our annotation process with relatively high sensitivity at the expense of specificity. Further research is required to confirm and better characterize the value of MLAs. Our adjudicated dataset is now available for public consumption in the form of a challenge.

Minimizing Barriers in Learning for On-Call Radiology Residents-End-to-End Web-Based Resident Feedback System

Feedback is an essential part of medical training, where trainees are provided with information regarding their performance and further directions for improvement. In diagnostic radiology, feedback entails a detailed review of the differences between the residents' preliminary interpretation and the attendings' final interpretation of imaging studies. While the on-call experience of independently interpreting complex cases is important to resident education, the more traditional synchronous "read-out" or joint review is impossible due to multiple constraints. Without an efficient method to compare reports, grade discrepancies, convey salient teaching points, and view images, valuable lessons in image interpretation and report construction are lost. We developed a streamlined web-based system, including report comparison and image viewing, to minimize barriers in asynchronous communication between attending radiologists and on-call residents. Our system provides real-time, end-to-end delivery of case-specific and user-specific feedback in a streamlined, easy-to-view format. We assessed quality improvement subjectively through surveys and objectively through participation metrics. Our web-based feedback system improved user satisfaction for both attending and resident radiologists, and increased attending participation, particularly with regards to cases where substantive discrepancies were identified.

Integrating ontologies of human diseases, phenotypes, and radiological diagnosis

Mappings between ontologies enable reuse and interoperability of biomedical knowledge. The Radiology Gamuts Ontology (RGO)-an ontology of 16 918 diseases, interventions, and imaging observations-provides a resource for differential diagnosis and automated textual report understanding in radiology. An automated process with subsequent manual review was used to identify exact and partial matches of RGO entities to the Disease Ontology (DO) and the Human Phenotype Ontology (HPO). Exact mappings identified equivalent concepts; partial mappings identified subclass and superclass relationships. A total of 7913 distinct RGO entities (46.8%) were mapped to one or both of the two target ontologies. Integration of RGO's causal knowledge resulted in 9605 axioms that expressed direct causal relationships between DO diseases and HPO phenotypic abnormalities, and allowed one to formulate queries about causal relations using the abstraction properties in those two ontologies. The mappings can be used to support automated diagnostic reasoning, data mining, and knowledge discovery.

The Impact of Interruptions on Chest Radiograph Interpretation: Effects on Reading Time and Accuracy

RATIONALE AND OBJECTIVES

The objective of this study was to experimentally test the effect of interruptions on image interpretation by comparing reading time and response accuracy of interrupted case reads to uninterrupted case reads in resident and attending radiologists.

MATERIALS AND METHODS

Institutional review board approval was obtained before participant recruitment from an urban academic health-care system during January 2016-March 2016. Eleven resident and 12 attending radiologists examined 30 chest radiographs, rating their confidence regarding the presence or the absence of a pneumothorax. Ten cases were normal (ie, no pneumothorax present), 10 cases had an unsubtle pneumothorax (ie, readily perceivable by a nonexpert), and 10 cases had a subtle pneumothorax. During three reads of each case type, the participants were interrupted with 30 seconds of a secondary task. The total reading time and the accuracy of interrupted and uninterrupted cases were compared. A mixed-factors analysis of variance was run on reading time and accuracy with experience (resident vs attending) as a between-subjects factor and case type (normal, unsubtle, or subtle) and interruption (interruption vs no interruption) as within-subjects factors.

RESULTS

Interrupted tasks had significantly longer reading times than uninterrupted cases (P = .032). During subtle cases, interruptions reduced accuracy (P = .034), but during normal cases, interruptions increased accuracy (P = .038).

CONCLUSIONS

Interruptions increased reading times and increased the tendency for a radiologist to conclude that a case is normal for both resident and attending radiologists, demonstrating that interruptions reduce efficiency and introduce patient safety concerns during reads of abnormal cases.

PathBot: A Radiology-Pathology Correlation Dashboard

Pathology is considered the "gold standard" of diagnostic medicine. The importance of radiology-pathology correlation is seen in interdepartmental patient conferences such as "tumor boards" and by the tradition of radiology resident immersion in a radiologic-pathology course at the American Institute of Radiologic Pathology. In practice, consistent pathology follow-up can be difficult due to time constraints and cumbersome electronic medical records. We present a radiology-pathology correlation dashboard that presents radiologists with pathology reports matched to their dictations, for both diagnostic imaging and image-guided procedures. In creating our dashboard, we utilized the RadLex ontology and National Center for Biomedical Ontology (NCBO) Annotator to identify anatomic concepts in pathology reports that could subsequently be mapped to relevant radiology reports, providing an automated method to match related radiology and pathology reports. Radiology-pathology matches are presented to the radiologist on a web-based dashboard. We found that our algorithm was highly specific in detecting matches. Our sensitivity was slightly lower than expected and could be attributed to missing anatomy concepts in the RadLex ontology, as well as limitations in our parent term hierarchical mapping and synonym recognition algorithms. By automating radiology-pathology correlation and presenting matches in a user-friendly dashboard format, we hope to encourage pathology follow-up in clinical radiology practice for purposes of self-education and to augment peer review. We also hope to provide a tool to facilitate the production of quality teaching files, lectures, and publications. Diagnostic images have a richer educational value when they are backed up by the gold standard of pathology.

Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors

Radiology report errors occur for many reasons including the use of pre-filled report templates, wrong-word substitution, nonsensical phrases, and missing words. Reports may also contain clinical errors that are not specific to the speech recognition including wrong laterality and gender-specific discrepancies. Our goal was to create a custom algorithm to detect potential gender and laterality mismatch errors and to notify the interpreting radiologists for rapid correction. A JavaScript algorithm was devised to flag gender and laterality mismatch errors by searching the text of the report for keywords and comparing them to parameters within the study's HL7 metadata (i.e., procedure type, patient sex). The error detection algorithm was retrospectively applied to 82,353 reports 4 months prior to its development and then prospectively to 309,304 reports 15 months after implementation. Flagged reports were reviewed individually by two radiologists for a true gender or laterality error and to determine if the errors were ultimately corrected. There was significant improvement in the number of flagged reports (pre, 198/82,353 [0.24%]; post, 628/309,304 [0.20%]; P = 0.04) and reports containing confirmed gender or laterality errors (pre, 116/82,353 [0.014%]; post, 285/309,304 [0.09%]; P < 0.0001) after implementing our error notification system. The number of flagged reports containing an error that were ultimately corrected improved dramatically after implementing the notification system (pre, 17/116 [15%]; post, 239/285 [84%]; P < 0.0001). We developed a successful automated tool for detecting and notifying radiologists of potential gender and laterality errors, allowing for rapid report correction and reducing the overall rate of report errors.

Use of data mining at the Food and Drug Administration

Objectives This article summarizes past and current data mining activities at the United States Food and Drug Administration (FDA).

Target audience We address data miners in all sectors, anyone interested in the safety of products regulated by the FDA (predominantly medical products, food, veterinary products and nutrition, and tobacco products), and those interested in FDA activities.

Scope Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.

MR imaging and osseous spinal intervention and intervertebral disk intervention

Percutaneous spine intervention, a wide range of invasive spine procedures performed through a puncture hole or through a small incision not requiring soft tissue closure and with few or no skin sutures or staples, is rapidly emerging as an effective alternative to open surgery. This article describes many of the minimally invasive osseous, intervertebral disk, and spinal nerve interventions currently being performed, including both well-established procedures and procedures developed recently. A general introduction to these types of procedures is provided, along with the characteristic pre- and postprocedural MR imaging appearance related to these techniques. The article also discusses reported and theoretical complications that may arise and their respective MR imaging appearances.

Diffusion abnormalities of the globi pallidi in manganese neurotoxicity

Manganese is an essential trace metal required for normal central nervous system function, which is toxic when in excess amounts in serum. Manganese neurotoxicity has been demonstrated in patients with chronic liver/biliary failure where an inability to excrete manganese via the biliary system causes increased serum levels, and in patients on total parenteral nutrition (TPN), occupational/inhalational exposure, or other source of excess exogenous manganese. Manganese has been well described in the literature to deposit selectively in the globi pallidi and to induce focal neurotoxicity. We present a case of a 53-year-old woman who presented for a brain MR 3 weeks after liver transplant due to progressively decreasing level of consciousness. The patient had severe liver failure by liver function tests and bilirubin levels, and had also been receiving TPN since the transplant. The MR demonstrated symmetric hyperintensity on T1-weighted images in the globi pallidi. Apparent diffusion coefficient (ADC) map indicated restricted diffusion in the globi pallidi bilaterally. The patient eventually succumbed to systemic aspergillosis 3 days after the MR. The serum manganese level was 195 mcg/l (micrograms per liter) on postmortem exam (over 20 times the upper limits of normal). The patient was presumed to have suffered from manganese neurotoxicity since elevated serum manganese levels have been shown in the literature to correlate with hyperintensity on T1-weighted images, neurotoxicity symptoms, and focal concentration of manganese in the globi pallidi. Neuropathologic sectioning of the globi pallidi at autopsy was also consistent with manganese neurotoxicity.