Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

Purpose: Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Methods: Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. Results: In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. Conclusions: The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the syste

Me, You and AI – Managing Human AI Collaboration in Computer Aided Intelligent Diagnosis

This research-in-progress paper focuses on examining configurations of collaboration between physicians and AI in decision making. From prior literature, we know that complex decisions in hospitals are the result of a collaborative decision-making process among physicians in a team. However, research from an information systems (IS) perspective in this area has so far focused on individual’s interactions with AI, while collaboration in decision-making for complex clinical cases reflects common practice also in technologically supported environments. Therefore, we aim to shed light on the question “which configuration of human AI collaboration in decision making is most recommendable for AI-enabled systems?” We plan to conduct a scenario-based experiment to investigate accuracy, speed, and satisfaction with various configurations of physician AI collaboration in the context of computer-aided intelligent diagnosis (CAID) systems. Our primary contribution will be a multidimensional evaluation of selected collaboration configurations aimed at improving healthcare with technology

Rapid Development of Medical Imaging Tools with Open-Source Libraries

Rapid prototyping is an important element in researching new imaging analysis techniques and developing custom medical applications. In the last ten years, the open source community and the number of open source libraries and freely available frameworks for biomedical research have grown significantly. What they offer are now considered standards in medical image analysis, computer-aided diagnosis, and medical visualization. A cursory review of the peer-reviewed literature in imaging informatics (indeed, in almost any information technology-dependent scientific discipline) indicates the current reliance on open source libraries to accelerate development and validation of processes and techniques. In this survey paper, we review and compare a few of the most successful open source libraries and frameworks for medical application development. Our dual intentions are to provide evidence that these approaches already constitute a vital and essential part of medical image analysis, diagnosis, and visualization and to motivate the reader to use open source libraries and software for rapid prototyping of medical applications and tools

Exploring individual user differences in the 2D/3D interaction with medical image data

User-centered design is often performed without regard to individual user differences. In this paper, we report results of an empirical study aimed to evaluate whether computer experience and demographic user characteristics would have an effect on the way people interact with the visualized medical data in a 3D virtual environment using 2D and 3D input devices. We analyzed the interaction through performance data, questionnaires and observations. The results suggest that differences in gender, age and game experience have an effect on people’s behavior and task performance, as well as on subjective\ud user preferences

Augmented Reality in Forensics and Forensic Medicine - Current Status and Future Prospects

Forensic investigations require a vast variety of knowledge and expertise of each specialist involved. With the increase in digitization and advanced technical possibilities, the traditional use of a computer with a screen for visualization and a mouse and keyboard for interactions has limitations, especially when visualizing the content in relation to the real world. Augmented reality (AR) can be used in such instances to support investigators in various tasks at the scene as well as later in the investigation process. In this article, we present current applications of AR in forensics and forensic medicine, the technological basics of AR, and the advantages that AR brings for forensic investigations. Furthermore, we will have a brief look at other fields of application and at future developments of AR in forensics