Receiver Operating Characteristic (ROC) Analysis

Visual expertise covers a broad range of types of studies and methodologies. Many studies incorporate some measure(s) of observer performance or how well participants perform on a given task. Receiver Operating Characteristic (ROC) analysis is a method commonly used in signal detection tasks (i.e., those in which the observer must decide whether or not a target is present or absent; or must classify a given target as belonging to one category or another), especially those in the medical imaging literature. This frontline paper will review some of the core theoretical underpinnings of ROC analysis, provide an overview of how to conduct an ROC study, and discuss some of the key variants of ROC analysis and their applications

Sustaining and Realizing the Promise of Telemedicine

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140277/1/tmj.2012.0282.pd

The Taxonomy of Telemedicine

The purpose of this article is to present a taxonomy for telemedicine. The field has markedly grown, with an increasing number of applications, a variety of technologies, and newly introduced terminology. A taxonomy would serve to bring conceptual clarity to this burgeoning set of alternatives to in-person healthcare delivery. The article starts with a brief discussion of the importance of taxonomy as an information management strategy to improve knowledge sharing, facilitate research and policy initiatives, and provide some guidance for the orderly development of telemedicine. We provide a conceptual context for the proliferation of related concepts, such as telehealth, e-health, and m-health, as well as a classification of the content of these concepts. Our main concern is to develop an explicit taxonomy of telemedicine and to demonstrate how it can be used to provide definitive information about the true effects of telemedicine in terms of cost, quality, and access. Taxonomy development and refinement is an iterative process. If this initial attempt at classification proves useful, subject matter experts could enhance the development and proliferation of telemedicine by testing, revising, and verifying this taxonomy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90498/1/tmj-2E2011-2E0103.pd

The Empirical Foundations of Teleradiology and Related Applications: A Review of the Evidence

Introduction: Radiology was founded on a technological discovery by Wilhelm Roentgen in 1895. Teleradiology also had its roots in technology dating back to 1947 with the successful transmission of radiographic images through telephone lines. Diagnostic radiology has become the eye of medicine in terms of diagnosing and treating injury and disease. This article documents the empirical foundations of teleradiology. Methods: A selective review of the credible literature during the past decade (2005?2015) was conducted, using robust research design and adequate sample size as criteria for inclusion. Findings: The evidence regarding feasibility of teleradiology and related information technology applications has been well documented for several decades. The majority of studies focused on intermediate outcomes, as indicated by comparability between teleradiology and conventional radiology. A consistent trend of concordance between the two modalities was observed in terms of diagnostic accuracy and reliability. Additional benefits include reductions in patient transfer, rehospitalization, and length of stay.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140295/1/tmj.2016.0149.pd

Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory

Abstract Background Significant interest exists in establishing radiologic imaging as a valid biomarker for assessing the response of cancer to a variety of treatments. To address this problem, we have chosen to study patients with metastatic colorectal carcinoma to learn whether statistical learning theory can improve the performance of radiologists using CT in predicting patient treatment response to therapy compared with the more traditional RECIST (Response Evaluation Criteria in Solid Tumors) standard. Results Predictions of survival after 8 months in 38 patients with metastatic colorectal carcinoma using the Support Vector Machine (SVM) technique improved 30% when using additional information compared to WHO (World Health Organization) or RECIST measurements alone. With both Logistic Regression (LR) and SVM, there was no significant difference in performance between WHO and RECIST. The SVM and LR techniques also demonstrated that one radiologist consistently outperformed another. Conclusions This preliminary research study has demonstrated that SLT algorithms, properly used in a clinical setting, have the potential to address questions and criticisms associated with both RECIST and WHO scoring methods. We also propose that tumor heterogeneity, shape, etc. obtained from CT and/or MRI scans be added to the SLT feature vector for processing

The Empirical Foundations of Telemedicine Interventions in Primary Care

Introduction: This article presents the scientific evidence for the merits of telemedicine interventions in primary care. Although there is no uniform and consistent definition of primary care, most agree that it occupies a central role in the healthcare system as first contact for patients seeking care, as well as gatekeeper and coordinator of care. It enables and supports patient-centered care, the medical home, managed care, accountable care, and population health. Increasing concerns about sustainability and the anticipated shortages of primary care physicians have sparked interest in exploring the potential of telemedicine in addressing many of the challenges facing primary care in the United States and the world. Materials and Methods: The findings are based on a systematic review of scientific studies published from 2005 through 2015. The initial search yielded 2,308 articles, with 86 meeting the inclusion criteria. Evidence is organized and evaluated according to feasibility/acceptance, intermediate outcomes, health outcomes, and cost. Results: The majority of studies support the feasibility/acceptance of telemedicine for use in primary care, although it varies significantly by demographic variables, such as gender, age, and socioeconomic status, and telemedicine has often been found more acceptable by patients than healthcare providers. Outcomes data are limited but overall suggest that telemedicine interventions are generally at least as effective as traditional care. Cost analyses vary, but telemedicine in primary care is increasingly demonstrated to be cost-effective. Conclusions: Telemedicine has significant potential to address many of the challenges facing primary care in today's healthcare environment. Challenges still remain in validating its impact on clinical outcomes with scientific rigor, as well as in standardizing methods to assess cost, but patient and provider acceptance is increasingly making telemedicine a viable and integral component of primary care around the world.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140293/1/tmj.2016.0045.pd

Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic

Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era

GazeGNN: A Gaze-Guided Graph Neural Network for Disease Classification

The application of eye-tracking techniques in medical image analysis has become increasingly popular in recent years. It collects the visual search patterns of the domain experts, containing much important information about health and disease. Therefore, how to efficiently integrate radiologists' gaze patterns into the diagnostic analysis turns into a critical question. Existing works usually transform gaze information into visual attention maps (VAMs) to supervise the learning process. However, this time-consuming procedure makes it difficult to develop end-to-end algorithms. In this work, we propose a novel gaze-guided graph neural network (GNN), GazeGNN, to perform disease classification from medical scans. In GazeGNN, we create a unified representation graph that models both the image and gaze pattern information. Hence, the eye-gaze information is directly utilized without being converted into VAMs. With this benefit, we develop a real-time, real-world, end-to-end disease classification algorithm for the first time and avoid the noise and time consumption introduced during the VAM preparation. To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data. Our experiments on the public chest X-ray dataset show that our proposed method exhibits the best classification performance compared to existing methods