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.

Teaching Physician Supervision: Medicare's Ongoing Imperative for Academic Radiologists.

OBJECTIVE. Medicare permits radiologists to bill for trainee work but only in narrowly defined circumstances and with considerable consequences for noncompliance. The purpose of this article is to introduce relevant policy rationale and definitions, review payment requirements, outline documentation and operational considerations for diagnostic and interventional radiology services, and offer practical suggestions for academic radiologists striving to optimize regulatory compliance. CONCLUSION. As academic radiology departments advance their missions of service, teaching, and scholarship, most rely on residents and fellows to support expanding clinical demands. Given the risks of technical noncompliance, institutional commitment and ongoing education regarding teaching supervision compliance are warranted.

Virtual and augmented reality: potential applications in radiology.

The modern-day radiologist must be adept at image interpretation, and the one who most successfully leverages new technologies may provide the highest value to patients, clinicians, and trainees. Applications of virtual reality (VR) and augmented reality (AR) have the potential to revolutionize how imaging information is applied in clinical practice and how radiologists practice. This review provides an overview of VR and AR, highlights current applications, future developments, and limitations hindering adoption.

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes. This article is a simultaneous joint publication in Radiology, Journal of the American College of Radiology, Canadian Association of Radiologists Journal, and Insights into Imaging. Published under a CC BY-NC-ND 4.0 license. Online supplemental material is available for this article.

Interpreting Radiographs with Concurrently Obtained Patient Photographs.

A technology for automatically obtaining patient photographs along with portable radiographs was implemented clinically at a large academic hospital. This article highlights several cases in which image-related clinical context, provided by the patient photographs, provided quality control information regarding patient identification, laterality, or position and assisted the radiologist with the interpretation. The information in the photographs can easily minimize unnecessary calls to the patient's nursing staff for clarifications and can lead to new methods of physically assessing patients. Published under a CC BY 4.0 license.

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

Software-based PET-MR image coregistration: combined PET-MRI for the rest of us!

BACKGROUND: With the introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI), a new imaging option to acquire multimodality images with complementary anatomical and functional information has become available. Compared with hybrid PET/computed tomography (CT), hybrid PET/MRI is capable of providing superior anatomical detail while removing the radiation exposure associated with CT. The early adoption of hybrid PET/MRI, however, has been limited. OBJECTIVE: To provide a viable alternative to the hybrid PET/MRI hardware by validating a software-based solution for PET-MR image coregistration. MATERIALS AND METHODS: A fully automated, graphics processing unit-accelerated 3-D deformable image registration technique was used to align PET (acquired as PET/CT) and MR image pairs of 17 patients (age range: 10 months-21 years, mean: 10 years) who underwent PET/CT and body MRI (chest, abdomen or pelvis), which were performed within a 28-day (mean: 10.5 days) interval. MRI data for most of these cases included single-station post-contrast axial T1-weighted images. Following registration, maximum standardized uptake value (SUVmax) values observed in coregistered PET (cPET) and the original PET were compared for 82 volumes of interest. In addition, we calculated the target registration error as a measure of the quality of image coregistration, and evaluated the algorithm's performance in the context of interexpert variability. RESULTS: The coregistration execution time averaged 97±45 s. The overall relative SUVmax difference was 7% between cPET-MRI and PET/CT. The average target registration error was 10.7±6.6 mm, which compared favorably with the typical voxel size (diagonal distance) of 8.0 mm (typical resolution: 0.66 mm × 0.66 mm × 8 mm) for MRI and 6.1 mm (typical resolution: 3.65 mm × 3.65 mm × 3.27 mm) for PET. The variability in landmark identification did not show statistically significant differences between the algorithm and a typical expert. CONCLUSION: We have presented a software-based solution that achieves the many benefits of hybrid PET/MRI scanners without actually needing one. The method proved to be accurate and potentially clinically useful.

Utilization of Workflow Process Maps to Analyze Gaps in Critical Event Notification at a Large, Urban Hospital.

Stroke care is a time-sensitive workflow involving multiple specialties acting in unison, often relying on one-way paging systems to alert care providers. The goal of this study was to map and quantitatively evaluate such a system and address communication gaps with system improvements. A workflow process map of the stroke notification system at a large, urban hospital was created via observation and interviews with hospital staff. We recorded pager communication regarding 45 patients in the emergency department (ED), neuroradiology reading room (NRR), and a clinician residence (CR), categorizing transmissions as successful or unsuccessful (dropped or unintelligible). Data analysis and consultation with information technology staff and the vendor informed a quality intervention-replacing one paging antenna and adding another. Data from a 1-month post-intervention period was collected. Error rates before and after were compared using a chi-squared test. Seventy-five pages regarding 45 patients were recorded pre-intervention; 88 pages regarding 86 patients were recorded post-intervention. Initial transmission error rates in the ED, NRR, and CR were 40.0, 22.7, and 12.0 %. Post-intervention, error rates were 5.1, 18.8, and 1.1 %, a statistically significant improvement in the ED (p < 0.0001) and CR (p = 0.004) but not NRR (p = 0.208). This intervention resulted in measureable improvement in pager communication to the ED and CR. While results in the NRR were not significant, this intervention bolsters the utility of workflow process maps. The workflow process map effectively defined communication failure parameters, allowing for systematic testing and intervention to improve communication in essential clinical locations.

Sociodemographic Variables Reporting in Human Radiology Artificial Intelligence Research.

PURPOSE: Artificial intelligence (AI) is rapidly reshaping how radiology is practiced. Its susceptibility to biases, however, is a primary concern as more AI algorithms become available for widespread use. So far, there has been limited evaluation of how sociodemographic variables are reported in radiology AI research. This study aims to evaluate the presence and extent of sociodemographic reporting in human subjects radiology AI original research. METHODS: All human subjects original radiology AI articles published from January to December 2020 in the top six US radiology journals, as determined by impact factor, were reviewed. Reporting of any sociodemographic variables (age, gender, and race or ethnicity) as well as any sociodemographic-based results were extracted. RESULTS: Of the 160 included articles, 54% reported at least one sociodemographic variable, 53% reported age, 47% gender, and 4% race or ethnicity. Six percent reported any sociodemographic-based results. There was significant variation in reporting of at least one sociodemographic variable by journal, ranging from 33% to 100%. CONCLUSIONS: Reporting of sociodemographic variables in human subjects original radiology AI research remains poor, putting the results and subsequent algorithms at increased risk of biases.

Leveraging Social Media and Web Presence to Discuss and Promote Diversity, Equity, and Inclusion in Radiology.

PURPOSE: This article seeks to better understand how radiology residency programs leverage their social media presences during the 2020 National Residency Match Program (NRMP) application cycle to engage with students and promote diversity, equity, and inclusion to prospective residency applicants. METHODS: We used publicly available information to determine how broad a presence radiology programs have across specific platforms (Twitter [Twitter, Inc, San Francisco, California], Facebook [Facebook, Inc, Menlo Park, California], Instagram [Facebook, Inc], and website pages) as well as what strategies these programs use to promote diversity, equity, and inclusion. RESULTS: During the 2020 NRMP application cycle, radiology residency programs substantially increased their social media presence across the platforms we examined. We determined that 29.3% (39 of 133), 58.9% (43 of 73), and 29.55% (13 of 44) of programs used Twitter, Instagram, and Facebook, respectively; these accounts were established after an April 1, 2020, advisory statement from the NRMP. Program size and university affiliation were correlated with the degree of social media presence. Those programs using social media to promote diversity, equity, and inclusion used a broad but similar approach across programs and platforms. CONCLUSION: The events of 2020 expedited the growth of social media among radiology residency programs, which subsequently ushered in a new medium for conversations about representation in medicine. However, the effectiveness of this medium to promote meaningful expansion of diversity, equity, and inclusion in the field of radiology remains to be seen.

Computer input devices: neutral party or source of significant error in manual lesion segmentation?

Lesion segmentation involves outlining the contour of an abnormality on an image to distinguish boundaries between normal and abnormal tissue and is essential to track malignant and benign disease in medical imaging for clinical, research, and treatment purposes. A laser optical mouse and a graphics tablet were used by radiologists to segment 12 simulated reference lesions per subject in two groups (one group comprised three lesion morphologies in two sizes, one for each input device for each device two sets of six, composed of three morphologies in two sizes each). Time for segmentation was recorded. Subjects completed an opinion survey following segmentation. Error in contour segmentation was calculated using root mean square error. Error in area of segmentation was calculated compared to the reference lesion. 11 radiologists segmented a total of 132 simulated lesions. Overall error in contour segmentation was less with the graphics tablet than with the mouse (P < 0.0001). Error in area of segmentation was not significantly different between the tablet and the mouse (P = 0.62). Time for segmentation was less with the tablet than the mouse (P = 0.011). All subjects preferred the graphics tablet for future segmentation (P = 0.011) and felt subjectively that the tablet was faster, easier, and more accurate (P = 0.0005). For purposes in which accuracy in contour of lesion segmentation is of the greater importance, the graphics tablet is superior to the mouse in accuracy with a small speed benefit. For purposes in which accuracy of area of lesion segmentation is of greater importance, the graphics tablet and mouse are equally accurate.

Ethical considerations in artificial intelligence.

With artificial intelligence (AI) precipitously perched at the apex of the hype curve, the promise of transforming the disparate fields of healthcare, finance, journalism, and security and law enforcement, among others, is enormous. For healthcare - particularly radiology - AI is anticipated to facilitate improved diagnostics, workflow, and therapeutic planning and monitoring. And, while it is also causing some trepidation among radiologists regarding its uncertain impact on the demand and training of our current and future workforce, most of us welcome the potential to harness AI for transformative improvements in our ability to diagnose disease more accurately and earlier in the populations we serve.

Improving Billing Accuracy Through Enterprise-Wide Standardized Structured Reporting With Cross-Divisional Shared Templates.

OBJECTIVE: We describe our experience in implementing enterprise-wide standardized structured reporting for chest radiographs (CXRs) via change management strategies and assess the economic impact of structured template adoption. METHODS: Enterprise-wide standardized structured CXR reporting was implemented in a large urban health care enterprise in two phases from September 2016 to March 2019: initial implementation of division-specific structured templates followed by introduction of auto launching cross-divisional consensus structured templates. Usage was tracked over time, and potential radiologist time savings were estimated. Correct-to-bill (CTB) rates were collected between January 2018 and May 2019 for radiography. RESULTS: CXR structured template adoption increased from 46% to 92% in phase 1 and to 96.2% in phase 2, resulting in an estimated 8.5 hours per month of radiologist time saved. CTB rates for both radiographs and all radiology reports showed a linearly increasing trend postintervention with radiography CTB rate showing greater absolute values with an average difference of 20% throughout the sampling period. The CTB rate for all modalities increased by 12%, and the rate for radiography increased by 8%. DISCUSSION: Change management strategies prompted adoption of division-specific structured templates, and exposure via auto launching enforced widespread adoption of consensus templates. Standardized structured reporting resulted in both economic gains and projected radiologist time saved.

Vision and quality in the digital imaging environment: how much does the visual acuity of radiologists vary at an intermediate distance?

OBJECTIVE: The purpose of this study was to examine the intermediate-distance visual acuity of a cross section of radiologists and to identify variation in visual acuity during a typical workday. SUBJECTS AND METHODS: Forty-eight radiologists completed a brief survey before undergoing visual acuity testing, with corrective lenses if routinely used, at three times of the day. Testing was performed with modified versions of a U.S. Federal Aviation Administration visual acuity test instrument. RESULTS: The mean acuity of radiologists across all measurements was 20/15 (logarithm of the minimum angle of resolution [logMAR], -0.109 +/- 0.105 [SD]). Visual acuity ranged from 20/30 to 20/10 (logMAR, 0.176 to -0.301). Mean visual acuity in the morning session was approximately 20/16 (logMAR, -0.0856). This value was statistically significantly lower than the mean visual acuity in both the early afternoon (logMAR, -0.124; p = 0.003) and the late afternoon (logMAR, -0.118; p = 0.015), both of which were approximately 20/15. This change was within the expected test-retest variability of Snellen acuity measurements. CONCLUSION: Although a statistically significant difference was detected between the visual acuity of radiologists in the morning and acuity in other parts of the day, this difference was relatively modest and within previously published ranges of variability for similar visual acuity tests. It is unlikely that such variation in visual acuity among radiologists influences diagnostic performance. Not every radiologist had 20/20 vision, a few needed visual correction, and more than a few had not undergone a thorough eye examination for as many as 15 years before the study.

An Introduction to Health Disparities for the Practicing Radiologist.

A substantial and growing body of literature explores health disparities in radiology and imaging. The term "health disparities" refers to health differences related to disadvantages experienced by vulnerable populations, often caused by underlying social determinants of health. As such, health disparities are often closely tied to issues of social justice. Radiologists can work to reduce health disparities in different ways, including through supporting education, diversity and inclusion efforts, disparities research, and advocacy.

MRI quantitation of abdominal skeletal muscle correlates with CT-based analysis: implications for sarcopenia measurement.

Sarcopenia is associated with poor outcomes in a variety of conditions, including malignancy. Abdominal skeletal muscle area (SMA) segmentation using computed tomography (CT) has been shown to be an accurate surrogate for identifying sarcopenia. While magnetic resonance imaging (MRI) segmentation of SMA has been validated in cadaver limbs, few studies have validated abdominal SMA segmentation using MRI at lumbar level mid-L3. Our objective was to assess the reproducibility and concordance of CT and MRI segmentation analyses of SMA at mid-L3. This retrospective analysis included a random sample of 10 patients with renal cell carcinoma (RCC) and CT abdomen/pelvis, used to assess intra-observer variability of SMA measurements using CT. An additional sample of 9 patients with RCC and both CT and T2-weighted (T2w) MRI abdomen/pelvis was used to assess intra-observer variability of SMA using MRI and concordance of SMA between MRI and CT. SMA was segmented using Slice-O-Matic. SMA reproducibility was assessed using intraclass correlation coefficient (ICC). SMA concordance was analyzed using Bland-Altman plot and Pearson correlation coefficient. The intra-observer variability of CT and MRI SMA at mid-L3 was low, with ICC of 0.998 and 0.985, respectively. Bland-Altman analysis revealed bias of 0.74% for T2w MRI over CT. The Pearson correlation coefficient was 0.997 (p < 0.0001), demonstrating strong correlation between CT and T2w MRI. Abdominal SMA at mid-L3 is reproducibly segmented for both CT and T2w MRI, with strong correlation between the 2 modalities. T2w MRI can be used interchangeably with CT for assessment of SMA and sarcopenia. This finding has important clinical implications.

ACR Appropriateness Criteria® Suspected Appendicitis-Child.

Acute appendicitis represents the most common abdominal surgical urgency/emergency in children. Imaging remains a central tool in the diagnosis of acute appendicitis and has been shown to facilitate management and decrease the rate of negative appendectomies. The initial consideration for imaging in a child with suspected acute appendicitis is based on clinical assessment, which can be facilitated with published scoring systems. The level of clinical risk (low, intermediate, high) and the clinical scenario (suspicion for complication) define the need for imaging and the optimal imaging modality. In some situations, no imaging is required, while in others ultrasound, CT, or MRI may be appropriate. This review frames the presentation of suspected acute appendicitis in terms of the clinical risk and also discusses the unique situations of the equivocal or nondiagnostic initial ultrasound examination and suspected appendicitis with suspicion for complication (eg, bowel obstruction). The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine.AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice.This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future.The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.