Intelligent DICOM Viewers | Andrew Shawa | Talks | Data Mining Seminars

Andrew Shawa gave a talk titled "Intelligent DICOM Viewers: Orthanc Plugin for Semi-Automated Interpretation of Medical Images" on May 15, 2024, at 17H30 GMT+2.
This talk is part of a series of seminars organised as part of the "CSC 5741: Data Mining and Warehousing" [1] postgraduate course in the Department of Computer Science [2] at The University of Zambia [3]
Video Timeline
00:00:00 Speaker Introduction
00:00:47 Talk Overview
00:01:29 Enterprise Medical Imaging Project
00:01:43 Presentation Outline
00:01:46 Overview
00:03:32 Challenges in Medical Interpretation
00:04:41 Orthanc Plugin for Semi-Automated Interpretation
00:07:55 Methodology: Integration
00:09:37 Benefits of Integration
00:11:11 User Evaluation of Intelligent DICOM Viewer Plugin
00:12:45 Conclusion and Future Work
00:13:45 Demonstration
Title of Talk
Intelligent DICOM Viewers: Orthanc Plugin for Semi-Automated Interpretation of Medical Images
Abstract
Medical image interpretation is a crucial part of radiological workflows, providing valuable input to the final output of the process: medical image interpretation reports. Radiologists typically employ Digital Imaging and Communication in Medicine (DICOM) Viewers. While numerous types of DICOM Viewers have been implemented, there has been arguably little focus on how such software tools can be made more effective by integrating them with Artificial Intelligence services. This paper presents a study conducted to design and implement an Orthanc Web-based Picture Archiving and Communication System (PACS) plugin DICOM Viewer for facilitating the semi-automated interpretation of medical images using Artificial Intelligence (AI). An Orthanc DICOM Viewer plugin, interoperable with two (2) models-Pneumonia Classification and Detection models-was implemented using the Python programming language. The plugin was evaluated with Radiologist Residents at a large University Teaching Hospital in a controlled setting. TAM 2 instrument was used to assess its perceived usefulness and ease of use. The TAM 2 constructs were rated positively, with study participants expressing a desire to incorporate other pathologies to the plugin and, additionally, integrating similar tools in more widely used DICOM Viewers such as RadiAnt and Weasis. The integration of AI models has the potential to reduce the turnaround time and workload involved in interpreting medical images. More significantly, the positive responses related to perceived usefulness of the tool suggest a potential that such tools have in improving delivery of services at the point of clinical care.
Biography
Andrew Shawa [4, 5] holds a degree in ICT with Education from the University of Zambia (UNZA) and a postgraduate diploma in Computer Science, also from UNZA. He is currently involved in the Enterprise Medical Imaging Zambia (EMIZ) project [6], where he utilizes his expertise to contribute to the advancement of healthcare technology.
[1] lightonphiri.org/teaching
[2] www.cs.unza.zm
[3] unza.zm
[4] emi.org.zm/user/5
[5] datalab.unza.zm/index.php/peo...
[6] emi.org.zm