Publications by authors named "Ameena Elahi"

Importance: Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects 2.4 million people world-wide, and up to 60% experience anxiety.

Objective: We investigated how anxiety in MS is associated with white matter lesion burden in the uncinate fasciculus (UF).

View Article and Find Full Text PDF

Background: Health insurance in the United States varies in coverage of essential diagnostic tests, therapies, and specialists. Health disparities between privately and publicly insured patients with MS have not been comprehensively assessed. The objective of this study is to evaluate the impact of public versus private insurance on longitudinal brain outcomes in MS.

View Article and Find Full Text PDF

Although numerous AI algorithms have been published, the relatively small number of algorithms used clinically is partly due to the difficulty of implementing AI seamlessly into the clinical workflow for radiologists and for their healthcare enterprise. The authors developed an AI orchestrator to facilitate the deployment and use of AI tools in a large multi-site university healthcare system and used it to conduct opportunistic screening for hepatic steatosis. During the 60-day study period, 991 abdominal CTs were processed at multiple different physical locations with an average turnaround time of 2.

View Article and Find Full Text PDF

Low- and middle-income countries 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.

View Article and Find Full Text PDF

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).

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

A PHP Error was encountered

Severity: Warning

Message: fopen(/var/lib/php/sessions/ci_sessionj7q1ieqbl98oaogt2cuaf891j8acavmv): Failed to open stream: No space left on device

Filename: drivers/Session_files_driver.php

Line Number: 177

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: session_start(): Failed to read session data: user (path: /var/lib/php/sessions)

Filename: Session/Session.php

Line Number: 137

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once