Lessons Learned in Change Management in Deploying Novel Informatics Solutions: Experience Implementing a Point-of-Care Patient Photography System with Radiography.

We describe implementation of a point-of-care system for simultaneous acquisition of patient photographs along with portable radiographs at a large academic hospital. During the implementation process, we observed several technical challenges in the areas of (1) hardware-automatic triggering for photograph acquisition, camera hardware enclosure, networking, and system server hardware and (2) software-post-processing of photographs. Additionally, we also faced cultural challenges involving workflow issues, communication with technologists and users, and system maintenance. We describe our solutions to address these challenges. We anticipate that these experiences will provide useful insights into deploying and iterating new technologies in imaging informatics.

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.

A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images.

Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals' PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners' examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.

Initial Experience With Patient Visible Light Images Obtained Simultaneously With Portable Radiographs.

OBJECTIVE. Visible light images in the form of point-of-care photographs obtained at the time of medical imaging can be useful for detecting wrong-patient errors and providing image-related clinical context. Our goal was to implement a system to automatically obtain point-of-care patient photographs along with portable radiographs. CONCLUSION. We discuss one academic medical center's initial experience in integrating the system into the clinical workflow and initial use cases ranging from cardiothoracic and abdominal imaging to musculoskeletal imaging, for which such point-of-care photographs were deemed clinically beneficial.

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.

Quantitative Ultrasound for Measuring Obstructive Severity in Children with Hydronephrosis.

PURPOSE: We define sonographic biomarkers for hydronephrotic renal units that can predict the necessity of diuretic nuclear renography. MATERIALS AND METHODS: We selected a cohort of 50 consecutive patients with hydronephrosis of varying severity in whom 2-dimensional sonography and diuretic mercaptoacetyltriglycine renography had been performed. A total of 131 morphological parameters were computed using quantitative image analysis algorithms. Machine learning techniques were then applied to identify ultrasound based safety thresholds that agreed with the t½ for washout. A best fit model was then derived for each threshold level of t½ that would be clinically relevant at 20, 30 and 40 minutes. Receiver operating characteristic curve analysis was performed. Sensitivity, specificity and area under the receiver operating characteristic curve were determined. Improvement obtained by the quantitative imaging method compared to the Society for Fetal Urology grading system and the hydronephrosis index was statistically verified. RESULTS: For the 3 thresholds considered and at 100% sensitivity the specificities of the quantitative imaging method were 94%, 70% and 74%, respectively. Corresponding area under the receiver operating characteristic curve values were 0.98, 0.94 and 0.94, respectively. Improvement obtained by the quantitative imaging method over the Society for Fetal Urology grade and hydronephrosis index was statistically significant (p <0.05 in all cases). CONCLUSIONS: Quantitative imaging analysis of renal sonograms in children with hydronephrosis can identify thresholds of clinically significant washout times with 100% sensitivity to decrease the number of diuretic renograms in up to 62% of children.

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 chemical shift MRI in the diagnosis of disorders affecting pediatric bone marrow.

OBJECTIVE: MRI signal intensity of pediatric bone marrow can be difficult to interpret using conventional methods. Chemical shift imaging (CSI), which can quantitatively assess relative fat content, may improve the ability to accurately diagnose bone marrow abnormalities in children. METHODS: Consecutive pelvis and extremity MRI at a children's hospital over three months were retrospectively reviewed for inclusion of CSI. Medical records were reviewed for final pathological and/or clinical diagnosis. Cases were classified as normal or abnormal, and if abnormal, subclassified as marrow-replacing or non-marrow-replacing entities. Regions of interest (ROI) were then drawn on corresponding in and out-of-phase sequences over the marrow abnormality or over a metaphysis and epiphysis in normal studies. Relative signal intensity ratio for each case was then calculated to determine the degree of fat content in the ROI. RESULTS: In all, 241 MRI were reviewed and 105 met inclusion criteria. Of these, 61 had normal marrow, 37 had non-marrow-replacing entities (osteomyelitis without abscess n = 17, trauma n = 9, bone infarction n = 8, inflammatory arthropathy n = 3), and 7 had marrow-replacing entities (malignant neoplasm n = 4, bone cyst n = 1, fibrous dysplasia n = 1, and Langerhans cell histiocytosis n = 1). RSIR averages were: normal metaphyseal marrow 0.442 (0.352-0.533), normal epiphyseal marrow 0.632 (0.566-698), non-marrow-replacing diagnoses 0.715 (0.630-0.799), and marrow-replacing diagnoses 1.06 (0.867-1.26). RSIR for marrow-replacing entities proved significantly different from all other groups (p < 0.05). ROC analysis demonstrated an AUC of 0.89 for RSIR in distinguishing marrow-replacing entities. CONCLUSION: CSI techniques can help to differentiate pathologic processes that replace marrow in children from those that do not.

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.

Checklist for Reproducibility of Deep Learning in Medical Imaging.

The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproducibility and refine techniques. Our study addresses the unique challenges presented by DL in medical imaging by developing a comprehensive checklist using the Delphi method to enhance reproducibility and reliability in this dynamic field. We compiled a preliminary checklist based on a comprehensive review of existing checklists and relevant literature. A panel of 11 experts in medical imaging and DL assessed these items using Likert scales, with two survey rounds to refine responses and gauge consensus. We also employed the content validity ratio with a cutoff of 0.59 to determine item face and content validity. Round 1 included a 27-item questionnaire, with 12 items demonstrating high consensus for face and content validity that were then left out of round 2. Round 2 involved refining the checklist, resulting in an additional 17 items. In the last round, 3 items were deemed non-essential or infeasible, while 2 newly suggested items received unanimous agreement for inclusion, resulting in a final 26-item DL model reporting checklist derived from the Delphi process. The 26-item checklist facilitates the reproducible reporting of DL tools and enables scientists to replicate the study's results.

Adrenal gland abnormality detection using random forest classification.

Adrenal abnormalities are commonly identified on computed tomography (CT) and are seen in at least 5 % of CT examinations of the thorax and abdomen. Previous studies have suggested that evaluation of Hounsfield units within a region of interest or a histogram analysis of a region of interest can be used to determine the likelihood that an adrenal gland is abnormal. However, the selection of a region of interest can be arbitrary and operator dependent. We hypothesize that segmenting the entire adrenal gland automatically without any human intervention and then performing a histogram analysis can accurately detect adrenal abnormality. We use the random forest classification framework to automatically perform a pixel-wise classification of an entire CT volume (abdomen and pelvis) into three classes namely right adrenal, left adrenal, and background. Once we obtain this classification, we perform histogram analysis to detect adrenal abnormality. The combination of these methods resulted in a sensitivity and specificity of 80 and 90 %, respectively, when analyzing 20 adrenal glands seen on volumetric CT datasets for abnormality.

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.

AI pitfalls and what not to do: mitigating bias in AI.

Various forms of artificial intelligence (AI) applications are being deployed and used in many healthcare systems. As the use of these applications increases, we are learning the failures of these models and how they can perpetuate bias. With these new lessons, we need to prioritize bias evaluation and mitigation for radiology applications; all the while not ignoring the impact of changes in the larger enterprise AI deployment which may have downstream impact on performance of AI models. In this paper, we provide an updated review of known pitfalls causing AI bias and discuss strategies for mitigating these biases within the context of AI deployment in the larger healthcare enterprise. We describe these pitfalls by framing them in the larger AI lifecycle from problem definition, data set selection and curation, model training and deployment emphasizing that bias exists across a spectrum and is a sequela of a combination of both human and machine factors.

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.

Development and Implementation of an Integrated Imaging Informatics Track for Radiology Residents: Our 3-Year Experience.

RATIONALE AND OBJECTIVES: Imaging Informatics is an emerging and fast-evolving field that encompasses the management of information during all steps of the imaging value chain. With many information technology tools being essential to the radiologists' day-to-day work, there is an increasing need for qualified professionals with clinical background, technology expertise, and leadership skills. To answer this, we describe our experience in the development and implementation of an Integrated Imaging Informatics Track (I3T) for radiology residents at our institution. MATERIALS AND METHODS: The I3T was created by a resident-driven initiative funded by an intradepartmental resident grant. Its curriculum is delivered through a combination of monthly small group discussions, operational meetings, recommended readings, lectures, and early exposure to the National Imaging Informatics Course. The track is steered and managed by the I3T Committee, including trainees and faculty advisors. Up to two first-year residents are selected annually based on their curriculum vitae and an interest application. Successful completion of the program requires submission of a capstone project and at least one academic deliverable (national meeting presentation, poster, exhibit, manuscript and/or grant). RESULTS: In our three-year experience, the seven I3T radiology residents have reported a total of 58 scholarly activities related to Imaging Informatics. I3T residents have assumed leadership roles within our organization and nationally. All residents have successfully carried out their clinical responsibilities. CONCLUSION: We have developed and implemented an I3T for radiology residents at our institution. These residents have been successful in their clinical, scholarship and leadership pursuits.