Limitations in and Solutions for Improving the Functionality of Picture Archiving and Communication System: an Exploratory Study of PACS Professionals' Perspectives

Emerging Applications of Picture Archiving and Communication Systems and Their Impact on Research and Education: A Literature Review

In recent times, technological advancements have remarkably improved picture archiving and communication system (PACS) capabilities beyond their conventional use in radiology departments. Researchers and instructors have started employing PACS functionalities to improve medical research processes, promote interdisciplinary collaborations, and facilitate learning. To illustrate this point further, the PACS enables researchers to handle and analyze huge amounts of imaging data with superior precision and speed, supporting innovative studies in areas like disease progression, treatment outcomes, and imaging modalities. Moreover, a PACS integrated with artificial intelligence (AI) algorithms leads to significant improvements in image processing, diagnostic accuracy, and personalized treatment, thus marking a new approach to medical imaging. The PACS supported by AI is mostly transformative since they allow for improved early disease detection capabilities as well as automated image processing and decision assistance, which increase diagnostic accuracy and clinical outcomes. Such systems can rapidly process large quantities of visual data with an accuracy rate surpassing earlier endeavors. In medical research, however, combining PACS with AI allows challenging imaging datasets to be examined, thereby making findings that were not previously possible. The capacity to combine imaging outcomes with clinical information is valuable for medical students and professionals in the field of education. They can access extensive medical image collections and case studies using PACS. This link is critical for teaching and learning as it allows students to interact with concrete events and improve their diagnostic accuracy in a controlled environment. This review discusses how the PACS affects educational courses and clinical outcomes based on the available literature. Our aim was not only to outline recent research or developments but also to present a comprehensive overview regarding the growing role played by PACS in the modern healthcare system and academics. Similarly, we look at the challenges and opportunities associated with the wide adoption of PACS, highlighting possible future areas of study or teaching methodologies. Issues such as data security, interoperability, and the need for defined protocols are included to give an exhaustive understanding of what PACS can and cannot do. Through this study, we stress PACS's revolutionary potential in advancing research methodology and educational practices, eventually contributing to enhanced patient care and knowledge dissemination in healthcare areas. The continual growth of PACS technology and its applications is expected to reshape the landscape of medical research and education, making it a vital component in the quest for medical excellence. By knowing the present trends and future potential, stakeholders in healthcare and education may better employ PACS to reach their objectives and boost overall results.

Do Cone Beam CT Picture Archiving and Communication Systems Viewer Interfaces Meet the Expectations of Dental Professionals From a Usability Perspective?

Background Cone beam computed tomography (CBCT) has revolutionized dental and maxillofacial imaging by providing high-resolution 3D visualizations, essential for accurate diagnosis and treatment planning. Despite its clinical advancements, the usability of CBCT viewer interfaces, which play a crucial role in the effective interpretation of imaging data, remains a critical concern. Objective This study aims to evaluate the usability of a CBCT viewer interface in clinical settings, focusing on the effectiveness, efficiency, and satisfaction perspectives, to identify potential areas for improvement. Methods Twenty-two participants (N=22) were assigned the task of locating the mental foramen in a mandible dataset, selected randomly, using the multiplanar reconstruction (MPR) mode of a CBCT viewer interface on a local network. The task's effectiveness was gauged by the completion rate, while efficiency was measured through the duration of the task, the number of mouse clicks, and the cursor's path in both pixels and meters. Satisfaction or perceived usability was evaluated using the system usability scale (SUS-TR), and computer system usability questionnaire (T-CSUQ), among other scales, with participants also providing open-ended feedback. Demographic characteristics served as classification variables. Results All participants completed the given task. No demography-related differences in efficiency were observed. Perceived usability (SUS-TR: 60.68±19.58, T-CSUQ: 43.63±16.34) was below the industry standards, categorizing the system as a detractor. Commonly reported issues included accessing the MPR window, cursor behavior, and unclear error messages. The mean SUS-TR score negatively correlated with efficiency-related variables (p<0.05 for each). Conclusions The study's findings indicate that the CBCT viewer interface does not fully meet dental professionals' usability expectations, as evidenced by the task's completion rate, efficiency metrics, and below-average usability scores. Despite the successful task completion by all participants, the identified issues in interface design, such as difficulties in accessing the MPR window and unclear error messages, highlight significant areas for improvement. To enhance user satisfaction and efficiency, future developments of CBCT viewer interfaces should incorporate feedback from end-users and prioritize user-friendly design principles.

A challenging work environment: The lived experiences of Eswatini diagnostic radiographers in the public health sector

BACKGROUND

Diagnostic radiographers play a vital role in healthcare so it is important for senior management to understand radiographer's duties and work experiences. There has been a number of studies conducted on the experiences of radiographers in other countries like the United Kingdom and South Africa. From these studies a number of work place challenges were identified. In the Eswatini healthcare context, there has been no research conducted on the daily experiences of diagnostic radiographers working in Eswatini. The country's leadership aims to attain its Vision 2022 which focusses on the attainment of the millennium development goals. This vision affects all healthcare professions so for it to be successful, it is imperative to understand what it means to be a diagnostic radiographer working in the country of Eswatini. This paper aims to address this gap in literature.

AIM

The aim of this paper is to explore and describe the lived experiences of Eswatini diagnostic radiographers working in the public health sector.

METHODS

A qualitative, exploratory, descriptive and phenomenological design was utilised. Participants were purposively sampled within the public health sector. Diagnostic radiographers participated voluntarily and willingly consented to participate in focus group interviews (n = 18).

RESULTS

A challenging work environment emerged as a principal theme from the participants narratives with 6 sub-themes namely: Lack of resources and consumables, Shortage of radiographers, Absence of radiologists, Lack of proper radiation monitoring and safety, Poor remuneration, Stagnant professional growth.

CONCLUSIONS

The findings of this study provided new insights into what Eswatini radiographers are experiencing in the public health sector. It is evident that there are a number of challenges which need to be addressed by the Eswatini management in order to successfully implement Vision 2022. Based on this study, there is the potential for a future study on the development of a professional identity for radiographers in Eswatini.

A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift

Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.

Survey-based Evaluation of the Use of Picture Archiving and Communication Systems in an Eye Hospital-Ophthalmologists' Perspective

PURPOSE

Picture archiving and communication system (PACS) is a medical imaging system for sharing, storage, retrieval, and access of medical images stored. Our study aimed to identify ophthalmologists' views on PACS, with the comparison between 3 platforms, namely electronic patient record (ePR), HEYEX (Heidelberg Engineering, Switzerland), and FORUM (Zeiss, US), following their implementation in an eye hospital for common ophthalmic investigations [visual field, optical coherence tomography (OCT) of retinal nerve fiber layer and macula, and fluorescein/indocyanine green angiography (FA/ICG)].

METHODS

An online survey was distributed among ophthalmologists in a single center. Primary outcome included comparison of PACS with paper-based system. Secondary outcomes included pattern of use and comparison of different PACS platforms.

RESULTS

Survey response rate was 28/37 (75.7%). Images were most commonly accessed through ePR (median: 80% of time, interquartile range: 50 to 90%).All systems scored highly in information display items (median scores ≥7.5 out of 10) and in reducing patient identification error in investigation filing and retrieval during consultation compared to paper (score ≥7.0). However, ePR was inferior to paper in "facilitating comparison with previous results" in all investigation types (scores 3.0 to 4.5). ePR scored significantly higher in all system quality items than HEYEX ( P  < 0.001) and FORUM ( P  < 0.022), except login response time ( P  = 0.081). HEYEX scored significantly higher among vitreoretinaluveitis members (VRU) for information quality items for OCT macula and FA/ICG [VRU: 10.0 (8.0 to 10.0), non-VRU: 8.0 (6.75 to 9.25), P  = 0.042].

CONCLUSIONS

Overall feedback for PACS among ophthalmologists was positive, with limitations of inefficiency in use of information, for example, comparison with previous results. Subspecialty played an important role in evaluating PACS.

A New Digital Platform for Collecting Measurement Data from the Novel Imaging Sensors in Urology

The use of UT and EIT technologies gives the opportunity to develop new, effective, minimally invasive diagnostic methods for urology. The introduction of new diagnostic methods into medicine requires the development of new tools for collecting, processing and analysing the data obtained from them. Such system might be seen as a part of the electronic health record EHR system. The digital medical data management platform must provide the infrastructure that will make medical activity possible and effective in the presented scope. The solution presented in this article was implemented using the newest computer technologies to obtain advantages such as mobility, versatility, flexibility and scalability. The architecture of the developed platform, technological stack proposals, database structure and user interface are presented. In the course of this study, an analysis of known and available standards such as Hl7, RIM, DICOM, and tools for collecting medical data was performed, and the results obtained using them are also presented. The developed digital platform also falls into an innovative path of creating a network of sensors communicating with each other in the digital space, resulting in the implementation of the IoT (Internet of Things) vision. The issues of building software based on the architecture of microservices were discussed emphasizing the role of message brokers. The selected message brokers were also analysed in terms of available features and message transmission time.

COVID-19: Challenges and coping strategies in radiology departments in Nigeria

Context:

COVID-19 came suddenly, bringing to the fore the challenges inherent in the health system. In a developing country, such as Nigeria, which already had myriad problems with funds and equipment in the health sector.

Aims:

This study aims to examine the challenges encountered by the staff in Radiology facilities and how they combated the challenges.

Settings and Design:

A descriptive cross-sectional study of radiology facilities in Nigeria.

Subjects and Methods:

Radiologists from nine government and four private facilities who attended to COVID-19 patients were asked to fill questionnaires on challenges faced and their coping strategies. Responses were sent through E-mail.

Statistical Analysis Used:

Data from the responses were analyzed using Microsoft excel for Mac 2011 and presented as figures and tables.

Results:

Majority of the government 7 (77.8%) and private facilities 4 (100%) had no equipment dedicated only to COVID-19 patients. Seven (77.8%) government facilities complained of inadequate staff, poor availability of personal protective equipment (PPEs) 8 (88.9%), and lack of technology for remote viewing 7 (77.8%). Fear of cross-infection was a challenge in one of the facilities 1 (11.1%). Coping strategies adopted include ensuring less traffic in the department by discouraging walk-in patients and canceling non-emergent cases, booking suspected/confirmed cases for lighter times, using old film for face shields and cloth for facemasks, staff education on COVID-19 and preventive measures, and sending reports to physicians through E-mail.

Conclusions:

There were a lot of challenges during the COVID-19 crisis, with government hospitals experiencing more challenges than private facilities. The challenges included among others inadequate staff strength and lack of technology for remote viewing. Some were overcome using education and by production of facemasks/shields production using recycled materials.

DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications

In the era of data-driven medicine, rapid access and accurate interpretation of medical images are becoming increasingly important. The DICOM Image ANalysis and Archive (DIANA) system is an open-source, lightweight, and scalable Python interface that enables users to interact with hospital Picture Archiving and Communications Systems (PACS) to access such data. In this work, DIANA functionality was detailed and evaluated in the context of retrospective PACS data retrieval and two prospective clinical artificial intelligence (AI) pipelines: bone age (BA) estimation and intra-cranial hemorrhage (ICH) detection. DIANA orchestrates activity beginning with post-acquisition study discovery and ending with online notifications of findings. For AI applications, system latency (exam completion to system report time) was quantified and compared to that of clinicians (exam completion to initial report creation time). Mean DIANA latency was 9.04 ± 3.83 and 20.17 ± 10.16 min compared to clinician latency of 51.52 ± 58.9 and 65.62 ± 110.39 min for BA and ICH, respectively, with DIANA latencies being significantly lower (p < 0.001). DIANA's capabilities were also explored and found effective in retrieving and anonymizing protected health information for "big-data" medical imaging research and analysis. Mean per-image retrieval times were 1.12 ± 0.50 and 0.08 ± 0.01 s across x-ray and computed tomography studies, respectively. The data herein demonstrate that DIANA can flexibly integrate into existing hospital infrastructure and improve the process by which researchers/clinicians access imaging repository data. This results in a simplified workflow for large data retrieval and clinical integration of AI models.

Joint Learning of Multi-level Tasks for Diabetic Retinopathy Grading on Low-resolution Fundus Images

Diabetic retinopathy (DR) is a leading cause of permanent blindness among the working-age people. Automatic DR grading can help ophthalmologists make timely treatment for patients. However, the existing grading methods are usually trained with high resolution (HR) fundus images, such that the grading performance decreases a lot given low resolution (LR) images, which are common in clinic. In this paper, we mainly focus on DR grading with LR fundus images. According to our analysis on the DR task, we find that: 1) image super-resolution (ISR) can boost the performance of both DR grading and lesion segmentation; 2) the lesion segmentation regions of fundus images are highly consistent with pathological regions for DR grading. Based on our findings, we propose a convolutional neural network (CNN)-based method for joint learning of multi-level tasks for DR grading, called DeepMT-DR, which can simultaneously handle the low-level task of ISR, the mid-level task of lesion segmentation and the high-level task of disease severity classification on LR fundus images. Moreover, a novel task-aware loss is developed to encourage ISR to focus on the pathological regions for its subsequent tasks: lesion segmentation and DR grading. Extensive experimental results show that our DeepMT-DR method significantly outperforms other state-of-the-art methods for DR grading over three datasets. In addition, our method achieves comparable performance in two auxiliary tasks of ISR and lesion segmentation.

Intelligent Algorithm-Based Picture Archiving and Communication System of MRI Images and Radiology Information System-Based Medical Informatization

Objective

The study aimed to explore the application value of picture archiving and communication system (PCAS) of MRI images based on radial basis function (RBF) neural network algorithm combined with the radiology information system (RIS).

Methods

551 patients who required MRI examination in a hospital from May 2016 to May 2021 were selected as research subjects. Patients were divided into two groups according to their own wishes. Those who were willing to use the RBF neural network algorithm-based PCAS of MRI images combined with RIS were set as the combined group, involving a total of 278 cases; those who were unwilling were set as the regular group, involving a total of 273 cases. The RBF neural network algorithm-based PCAS of MRI images combined with RIS was trained and tested for classification performance and then used for comparison analysis.

Result

The actual output (0.031259–0.038515) of all test samples was almost the same as the target output (0.000000) (P > 0.05). In the first 50,000 learnings, the iteration error of the RBF neural network dropped rapidly and finally stabilized at 0.038. The classification accuracy of the RBF neural network algorithm-based PCAS of MRI images combined with RIS for the head was 94.28%, that of abdomen was 97.22%, and it was 93.10% for knee joint, showing no statistically significant differences (P > 0.05), and the total classification accuracy was as high as 95%. The time spent in the examination in the combined group was about 2 hours, and that in the regular group was about 4 hours (P > 0.05). The satisfaction of the combined group (96.76%) was significantly higher than that of the control group (46.89%) (P > 0.05).

Conclusion

The RBF neural network has good classification performance for MRI images. To incorporate intelligent algorithms into the medical information system can optimize the system. RBF has good application prospects in the medical information system, and it is worthy of continuous exploration.

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.