Building Radiology Equity: Themes from the 2023 RAD-AID Conference on International Radiology and Global Health.

Low-and middle-income countries (LMICs) are significantly impacted by the global scarcity of medical imaging services. Medical imaging is an essential component for diagnosis and guided treatment, which is needed to meet the current challenges of increasing chronic diseases and preparedness for acute-care response. We present some key themes essential for improving global health equity which were discussed at the 2023 RAD-AID Conference on International Radiology and Global Health. They include: (i) capacity-building, (ii) artificial intelligence (AI), (iii) community-based patient navigation, (iv) organizational design for multidisciplinary global health strategy, (v) implementation science, and (vi) innovation. Although not exhaustive, these themes should be considered influential as we guide and expand global health radiology programs in LMICs in the coming years.

Certified Imaging Informatics Professionals (CIIP) Demonstrate Value to the Healthcare Industry and Focus on Quality Through the ABII 10-Year Requirements Practice Option.

Strengthening the field of imaging informatics by further defining standards and advocating for continuous education are the cornerstones of the American Board of Imaging Informatics (ABII). ABII is the non-profit organization that governs the Imaging Informatics Professional certification program. ABII is responsible for awarding the Certified Imaging Informatics Professional (CIIP) designation to candidates who meet specified educational and experience-based criteria and pass a qualifying exam (1). For this paper, we analyzed Quality Improvement (QI) projects submitted to ABII for satisfaction of the 10-year requirements in 2017-2021. The project reports demonstrated a variety of interventions undertaken to ultimately improve patient care. A retrospective review of these reports exemplifies the critical role the Certified Imaging Informatics Professionals have in delivery of high quality, safe healthcare and their vital contributions to the healthcare industry and practice of medicine.

Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.

Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024 Supplemental material is available for this article.

Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis.

The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m2 and hepatic steatosis with threshold SHAD ≥ - 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN. The cohort contained 8914 patients-lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p < 0.0001). Ten phenotypes were significant in the PheWAS, including chronic kidney disease, renal failure, and cardiovascular disease. Hepatic steatosis was found to be associated with cardiovascular, kidney, and metabolic conditions, separate from overweight BMI.

Mapping the Relationship of White Matter Lesions to Depression in Multiple Sclerosis.

BACKGROUND: Multiple sclerosis (MS) is an immune-mediated neurological disorder, and up to 50% of patients experience depression. We investigated how white matter network disruption is related to depression in MS. METHODS: Using electronic health records, 380 participants with MS were identified. Depressed individuals (MS+Depression group; n = 232) included persons who had an ICD-10 depression diagnosis, had a prescription for antidepressant medication, or screened positive via Patient Health Questionnaire (PHQ)-2 or PHQ-9. Age- and sex-matched nondepressed individuals with MS (MS-Depression group; n = 148) included persons who had no prior depression diagnosis, had no psychiatric medication prescriptions, and were asymptomatic on PHQ-2 or PHQ-9. Research-quality 3T structural magnetic resonance imaging was obtained as part of routine care. We first evaluated whether lesions were preferentially located within the depression network compared with other brain regions. Next, we examined if MS+Depression patients had greater lesion burden and if this was driven by lesions in the depression network. Primary outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. RESULTS: MS lesions preferentially affected fascicles within versus outside the depression network (ß = 0.09, 95% CI = 0.08 to 0.10, p < .001). MS+Depression patients had more lesion burden (ß = 0.06, 95% CI = 0.01 to 0.10, p = .015); this was driven by lesions within the depression network (ß = 0.02, 95% CI = 0.003 to 0.040, p = .020). CONCLUSIONS: We demonstrated that lesion location and burden may contribute to depression comorbidity in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression patients had more disease than MS-Depression patients, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.

Mapping the relationship of white matter lesions to depression in multiple sclerosis.

Importance: Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression. Objective: To investigate how white matter network disruption is related to depression in MS. Design: Retrospective case-control study of participants who received research-quality 3-tesla neuroimaging as part of MS clinical care from 2010-2018. Analyses were performed from May 1 to September 30, 2022. Setting: Single-center academic medical specialty MS clinic. Participants: Participants with MS were identified via the electronic health record (EHR). All participants were diagnosed by an MS specialist and completed research-quality MRI at 3T. After excluding participants with poor image quality, 783 were included. Inclusion in the depression group (MS+Depression) required either: 1) ICD-10 depression diagnosis (F32-F34.*); 2) prescription of antidepressant medication; or 3) screening positive via Patient Health Questionnaire-2 (PHQ-2) or -9 (PHQ-9). Age- and sex-matched nondepressed comparators (MS-Depression) included persons with no depression diagnosis, no psychiatric medications, and were asymptomatic on PHQ-2/9. Exposure: Depression diagnosis. Main Outcomes and Measures: We first evaluated if lesions were preferentially located within the depression network compared to other brain regions. Next, we examined if MS+Depression patients had greater lesion burden, and if this was driven by lesions specifically in the depression network. Outcome measures were the burden of lesions (e.g., impacted fascicles) within a network and across the brain. Secondary measures included between-diagnosis lesion burden, stratified by brain network. Linear mixed-effects models were employed. Results: Three hundred-eighty participants met inclusion criteria, (232 MS+Depression: age[SD]=49[12], %females=86; 148 MS-Depression: age[SD]=47[13], %females=79). MS lesions preferentially affected fascicles within versus outside the depression network (ß=0.09, 95% CI=0.08-0.10, P<0.001). MS+Depression had more white matter lesion burden (ß=0.06, 95% CI=0.01-0.10, P=0.015); this was driven by lesions within the depression network (ß=0.02, 95% CI 0.003-0.040, P=0.020). Conclusions and Relevance: We provide new evidence supporting a relationship between white matter lesions and depression in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression had more disease than MS-Depression, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.

Deploying Artificial Intelligence for Thoracic Imaging Around the World.

PURPOSE: Artificial intelligence (AI) thoracic imaging applications are increasingly being deployed in low- and middle-income countries (LMICs). Radiologists have a critical gatekeeping role to ensure the effective and ethical implementation of AI solutions. RAD-AID International uses a three-pronged implementation strategy to overcome challenges pervasive in LMICs. METHODS: During a similar period, an AI software for chest radiography (CXR) interpretation was deployed at two tertiary hospitals located in Guyana and Nigeria. The three-pronged implementation strategy of clinical education, infrastructure implementation, and phased AI introduction was used. A PACS with a cloud component was installed at each institution. Radiology residents and attending physicians at these institutions completed an introduction-to-AI course to prime them for the use of AI solutions. A phased introduction of the AI software was performed to allow local validation as well as trust building and workflow integration. Local validation processes were used at each site by comparing AI outputs with standardized prospectively generated reports by local radiologists and study team members, allowing for slight differences in the goals of AI software use between sites. RESULTS: The PACS was successfully installed at both institutions. Thirty participants completed the introduction-to-AI course with an average pre-knowledge test score of 75% and an average posttest score of 95%. The focus of the validation process at various sites was reflective of the intended use of the AI software. In Guyana, it revealed an 87% concordance rate between radiologists and the AI model for triaging normal versus abnormal findings on CXR. In Nigeria, an 85% concordance rate between radiologists and the AI model for reporting tuberculosis on CXR was noted. The AI software was successfully deployed and is being used as intended at both institutions. CONCLUSIONS: There are unique barriers to the adoption of AI in LMICs requiring an implementation strategy in collaboration with local institutions and industry partners to ensure success.

Exploring the Clinical Translation of Generative Models Like ChatGPT: Promise and Pitfalls in Radiology, From Patients to Population Health.

Generative artificial intelligence (AI) tools such as GPT-4, and the chatbot interface ChatGPT, show promise for a variety of applications in radiology and health care. However, like other AI tools, ChatGPT has limitations and potential pitfalls that must be considered before adopting it for teaching, clinical practice, and beyond. We summarize five major emerging use cases for ChatGPT and generative AI in radiology across the levels of increasing data complexity, along with pitfalls associated with each. As the use of AI in health care continues to grow, it is crucial for radiologists (and all physicians) to stay informed and ensure the safe translation of these new technologies.

Understanding Biases and Disparities in Radiology AI Datasets: A Review.

Artificial intelligence (AI) continues to show great potential in disease detection and diagnosis on medical imaging with increasingly high accuracy. An important component of AI model creation is dataset development for training, validation, and testing. Diverse and high-quality datasets are critical to ensure robust and unbiased AI models that maintain validity, especially in traditionally underserved populations globally. Yet publicly available datasets demonstrate problems with quality and inclusivity. In this literature review, the authors evaluate publicly available medical imaging datasets for demographic, geographic, genetic, and disease representation or lack thereof and call for an increase emphasis on dataset development to maximize the impact of AI models.

Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology.

Scarce or absent radiology resources impede adoption of artificial intelligence (AI) for medical imaging by resource-poor health institutions. They face limitations in local equipment, personnel expertise, infrastructure, data-rights frameworks, and public policies. The trustworthiness of AI for medical decision making in global health and low-resource settings is hampered by insufficient data diversity, nontransparent AI algorithms, and resource-poor health institutions' limited participation in AI production and validation. RAD-AID's three-pronged integrated strategy for AI adoption in resource-poor health institutions is presented, which includes clinical radiology education, infrastructure implementation, and phased AI introduction. This strategy derives from RAD-AID's more-than-a-decade experience as a nonprofit organization developing radiology in resource-poor health institutions, both in the United States and in low- and middle-income countries. The three components synergistically provide the foundation to address health care disparities. Local radiology personnel expertise is augmented through comprehensive education. Software, hardware, and radiologic and networking infrastructure enables radiology workflows incorporating AI. These educational and infrastructure developments occur while RAD-AID delivers phased introduction, testing, and scaling of AI via global health collaborations.

Overcoming Challenges for Successful PACS Installation in Low-Resource Regions: Our Experience in Nigeria.

In this paper, we walk you through our challenges, successes, and experience while participating in a Global Health Outreach Project at the University College Hospital (UCH) Ibadan, Nigeria. The scope of the project was to install a Picture Archive and Communication System (PACS) to establish a centralized viewing network at UCH's Radiology Department, for each of their digital modalities. Installing a PACS requires robust servers, the ability to retrieve and archive studies, ensuring workstations can view studies, and the configuration of imaging modalities to send studies. We anticipated that we might experience hurdles for each of these requirements, due to limited resources and without the availability to make a site visit prior to the start of the project. While we ultimately experienced delays and troubleshooting was required at each turn of the install, with the help of dedicated volunteers both on and off-site and the UCH staff, our shared goal was accomplished.

Automating Import and Reconciliation of Outside Examinations Submitted to an Academic Radiology Department.

Although advances in electronic image sharing have made continuity of patient care easier, currently, the majority of outside studies are received on CD. At our institution, there were 9 full-time employees (FTE) at three locations using three workflows to manually upload, schedule, and process studies to PACS. As the demand to view and store outside studies has grown, so has the processing turnaround time. To reduce turnaround time and the need for human intervention, we developed an automated workflow to import outside studies from a CD to our PACS and reconcile them with an internal accession number and exam code.