A Lightweight Deep Learning Framework for Automatic MRI Data Sorting and Artifacts Detection
AbstractThe purpose of this study is to develop a lightweight and easily deployable deep learning system for fully automated content-based brain MRI sorting and artifacts detection. 22092 MRI volumes from 4076 patients between 2017 and 2021 were involved in this retrospective study. The dataset mainly contains 4 common contrast (T1-weighted (T1w), contrast-enhanced T1-weighted (T1c), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR)) in three perspectives (axial, coronal, and sagittal), and magnetic resonance angiography (MRA), as well as three typical artifacts (motion, aliasing, and metal artifacts). In the ...
Source: Journal of Medical Systems - November 24, 2023 Category: Information Technology Source Type: research
Evaluating the Performance of Different Large Language Models on Health Consultation and Patient Education in Urolithiasis
ConclusionClaude demonstrated superior performance compared with the other three in urolithiasis consultation and education. This study highlights the remarkable potential of LLMs in medical health consultations and patient education, although professional review, further evaluation, and modifications are still required. (Source: Journal of Medical Systems)
Source: Journal of Medical Systems - November 24, 2023 Category: Information Technology Source Type: research
A Lightweight Deep Learning Framework for Automatic MRI Data Sorting and Artifacts Detection
AbstractThe purpose of this study is to develop a lightweight and easily deployable deep learning system for fully automated content-based brain MRI sorting and artifacts detection. 22092 MRI volumes from 4076 patients between 2017 and 2021 were involved in this retrospective study. The dataset mainly contains 4 common contrast (T1-weighted (T1w), contrast-enhanced T1-weighted (T1c), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR)) in three perspectives (axial, coronal, and sagittal), and magnetic resonance angiography (MRA), as well as three typical artifacts (motion, aliasing, and metal artifacts). In the ...
Source: Journal of Medical Systems - November 24, 2023 Category: Information Technology Source Type: research
Evaluating the Performance of Different Large Language Models on Health Consultation and Patient Education in Urolithiasis
ConclusionClaude demonstrated superior performance compared with the other three in urolithiasis consultation and education. This study highlights the remarkable potential of LLMs in medical health consultations and patient education, although professional review, further evaluation, and modifications are still required. (Source: Journal of Medical Systems)
Source: Journal of Medical Systems - November 24, 2023 Category: Information Technology Source Type: research
“ChatGPT, Can You Help Me Save My Child’s Life?” - Diagnostic Accuracy and Supportive Capabilities to Lay Rescuers by ChatGPT in Prehospital Basic Life Support and Paediatric Advanced Life Support Cases – An In-silico Analysis
ConclusionConsidering these results of the recent ChatGPT versions, the validity, reliability and thus safety of ChatGPT/GPT-4 as an emergency support tool is questionable. However, whether humans would perform better in the same situation is uncertain. Moreover, other studies have shown that human emergency call operators are also inaccurate, partly with worse performance than ChatGPT/GPT-4 in our study. However, one of the main limitations of the study is that we used prototypical cases, and the management may differ from urban to rural areas and between different countries, indicating the need for further evaluation of ...
Source: Journal of Medical Systems - November 21, 2023 Category: Information Technology Source Type: research
TBUnet: A Pure Convolutional U-Net Capable of Multifaceted Feature Extraction for Medical Image Segmentation
AbstractMany current medical image segmentation methods utilize convolutional neural networks (CNNs), with some extended U-Net-based networks relying on deep feature representations to achieve satisfactory results. However, due to the limited receptive fields of convolutional architectures, they are unable to explicitly model the varying range dependencies present in medical images. Recently, advancements in large kernel convolution have allowed for the extraction of a wider range of low frequency information, making this task more achievable. In this paper, we propose TBUnet for solving the problem of difficult to accurat...
Source: Journal of Medical Systems - November 17, 2023 Category: Information Technology Source Type: research
Applications of Artificial Intelligence in Health Care Delivery
AbstractHealth care costs now comprise nearly one-fifth of the United States ’ gross domestic product, with the last 25 years marked by rising administrative costs, a lack of labor productivity growth, and rising patient and physician dissatisfaction. Policy experts have responded with a series of reforms that have – ironically - increased patient and physician administr ative burden with little meaningful effect on cost and quality. Artificial intelligence (AI), a topic of great consternation, can serve as the “wheat thresher” for health care delivery, empowering and freeing both patients and physicians by decreas...
Source: Journal of Medical Systems - November 17, 2023 Category: Information Technology Source Type: research
TBUnet: A Pure Convolutional U-Net Capable of Multifaceted Feature Extraction for Medical Image Segmentation
AbstractMany current medical image segmentation methods utilize convolutional neural networks (CNNs), with some extended U-Net-based networks relying on deep feature representations to achieve satisfactory results. However, due to the limited receptive fields of convolutional architectures, they are unable to explicitly model the varying range dependencies present in medical images. Recently, advancements in large kernel convolution have allowed for the extraction of a wider range of low frequency information, making this task more achievable. In this paper, we propose TBUnet for solving the problem of difficult to accurat...
Source: Journal of Medical Systems - November 17, 2023 Category: Information Technology Source Type: research
Applications of Artificial Intelligence in Health Care Delivery
AbstractHealth care costs now comprise nearly one-fifth of the United States ’ gross domestic product, with the last 25 years marked by rising administrative costs, a lack of labor productivity growth, and rising patient and physician dissatisfaction. Policy experts have responded with a series of reforms that have – ironically - increased patient and physician administr ative burden with little meaningful effect on cost and quality. Artificial intelligence (AI), a topic of great consternation, can serve as the “wheat thresher” for health care delivery, empowering and freeing both patients and physicians by decreas...
Source: Journal of Medical Systems - November 17, 2023 Category: Information Technology Source Type: research
Shaping Social Media: Is Twitter an Equitable tool for Professional Development?
Conclusion: Male users have significantly higher levels of influence in health policy on Twitter. Given the importance of Twitter as a tool for professional development, it is crucial that institutional leaders and policymakers are aware of potential inequities in user reach. Future studies should evaluate additional factors shaping user influence in healthcare on Twitter, with a focus on equity, diversity, and trustworthiness. (Source: Journal of Medical Systems)
Source: Journal of Medical Systems - November 16, 2023 Category: Information Technology Source Type: research
A Retrospective Analysis Investigating Whether Case Volume Experience of the Anesthesiologist Correlates with Intraoperative Efficiency for Joint Arthroplasty
The objective of this retrospective study was to determine if there was an association between anesthesiology experience (e.g. historic case volume) and operating room (OR) efficiency times for lower extremity joint arthroplasty cases. The primary outcome was time from patient in the OR to anesthesia ready (i.e. after spinal or general anesthesia induction was complete). The secondary outcomes included time from anesthesia ready to surgical incision, and time from incision to closing completed. Mixed effects linear regression was performed, in which the random effect was the anesthesiology attending provider. There were 4,...
Source: Journal of Medical Systems - November 16, 2023 Category: Information Technology Source Type: research
Screening for Psychological Distress in Healthcare Workers Using Machine Learning: A Proof of Concept
AbstractThe purpose of this study was to train and test preliminary models using two machine learning algorithms to identify healthcare workers at risk of developing anxiety, depression, and post-traumatic stress disorder. The study included data from a prospective cohort study of 816 healthcare workers collected using a mobile application during the first two waves of COVID-19. Each week, the participants responded to 11 questions and completed three screening questionnaires (one for anxiety, one for depression, and one for post-traumatic stress disorder). Then, the research team selected two questions (out of the 11), wh...
Source: Journal of Medical Systems - November 16, 2023 Category: Information Technology Source Type: research
Health Technology Readiness amongst Patients with Suspected Breast Cancer Using the READHY-tool - a Cross-sectional Study
AbstractInformation technologies are increasingly used when informing patients about their disease, treatment and prognosis. These digital platforms have many advantages compared to traditional education interventions. However, there are concerns that some patients may have difficulty with this mode of information delivery. Newly diagnosed breast cancer patients are dependent on understanding their treatment options to make informed treatment decisions. Yet, there is a lack of published material on breast cancer patients and their relationship with technology. We aimed to assess health technology readiness profiles amongst...
Source: Journal of Medical Systems - November 16, 2023 Category: Information Technology Source Type: research
Shaping Social Media: Is Twitter an Equitable tool for Professional Development?
Conclusion: Male users have significantly higher levels of influence in health policy on Twitter. Given the importance of Twitter as a tool for professional development, it is crucial that institutional leaders and policymakers are aware of potential inequities in user reach. Future studies should evaluate additional factors shaping user influence in healthcare on Twitter, with a focus on equity, diversity, and trustworthiness. (Source: Journal of Medical Systems)
Source: Journal of Medical Systems - November 16, 2023 Category: Information Technology Source Type: research
