Generating synthetic medical images with limited data using auxiliary classifier generative adversarial network: a study on thyroid ultrasound images.

BACKGROUND AND OBJECTIVE: The availability of labeled data is crucial for training deep neural networks. However, in some cases, the available data is limited or unlabeled, which poses a significant obstacle in developing accurate models. Various approaches exist to address this issue, such as Image Augmentation, Transfer Learning, and GANs. However, these approaches often require a significant amount of training data or may not generate desired results. In this article, we present a novel method for generating synthetic images from very limited data using the ACGAN. METHODS: We conducted experiments on a real dataset consisting of 198 ultrasound images of calcified and cystic thyroid gland nodules. We explored and improved different architectures and techniques in the Axillary Classifier Generative Adversarial Network (ACGAN) to generate high-quality synthetic images. To evaluate the generated images, we used the Fréchet Inception Distance (FID) test and human observation. Additionally, we developed an image blending method to generate larger images that simulate the output of an ultrasound device. To validate the accuracy of the merged images, a specialist doctor reviewed the generated data. RESULTS: The modified ACGAN architecture successfully generated new synthetic images from limited data. The output images were assessed based on the image progress ratio with the FID test and human observation. Moreover, the Image blending method was successful in producing larger output images that mimic the nature of the ultrasound device output images. The final merged images were validated by a specialist doctor who confirmed their accuracy. CONCLUSIONS: Our method has significant implications for medical imaging, as it enables the generation of synthetic labeled data for training deep learning models, leading to better diagnostic accuracy and improved patient outcomes. This study provides a proof-of-concept for generating synthetic medical images from limited labeled data and can inspire future research in this area.

The Effect of Text Message-based Support Program (TextRehab) on Treatment Outcomes of Patients with Hand Flexor Tendon Injuries after Repair: A Randomized Controlled Trial Study Protocol.

Background: Post-operative rehabilitation for patients with flexor tendon injuries is necessary for a full recovery. This randomized controlled trial study investigates the effectiveness of a text message-based rehabilitation program (i.e., TextRehab) on the improvement rate of hand rehabilitation in patients with flexor tendon injuries after repair. Methods: This study is designed as a randomized, three-month, single-center, two-arm, parallel controlled trial. A total of 40 patients will be randomly classified as either the control or intervention group. Both groups receive usual care; however, the intervention group is also asked to perform the designed rehabilitation activities through the TextRehab program. The activity instructions are sent to patients step by step at least once a day. Self-reported outcomes will be assessed at 6 and 12 weeks after discharge and include self-reported Patient Rated Wrist Evaluation, self-reported Quick-Disability of Arm, Shoulder, and Hand, and Visual Analogue Scale. Moreover, the reports of the physician regarding the grip strength and Total Active Motion will be assessed at week 12. Results: The development of the message scheduling system and its contents is completed. This trial has the code of ethics in research (removed due to blinding issues). Study results are expected to be available in mid-2021. Conclusion: The TextRehab program is developed to provide advice, motivation, information, and care for patients with hand flexor tendon injuries after repair. This trial provides evidence of the effectiveness of sending text messages on persuading patients to perform home-based rehabilitation activities.

Clinical informatics solutions in COVID-19 pandemic: Scoping literature review.

Background: The global outbreak of COVID-19 (coronavirus disease 2019) disease has highlighted the importance of disease monitoring, diagnosing, treating, and screening. Technology-based instruments could efficiently assist healthcare systems during pandemics by allowing rapid and widespread transfer of information, real-time tracking of data transfer, and virtualization of meetings and patient visits. Therefore, this study was conducted to investigate the applications of clinical informatics (CI) during the COVID-19 outbreak. Methods: A comprehensive search was performed on Medline and Scopus databases in September 2020. Eligible studies were selected based on the inclusion and exclusion criteria. The extracted data from the studies reviewed were about study sample, study type, objectives, clinical informatics domain, applied method, sample size, outcomes, findings, and conclusion. The risk of bias was evaluated in the studies using appropriate instruments based on the type of each study. The selected studies were then subjected to thematic synthesis. Results: In this review study, 72 out of 2716 retrieved articles met the inclusion criteria for full-text analysis. Most of the articles reviewed were done in China and the United States of America. The majority of the studies were conducted in the following CI domains: prediction models (60%), telehealth (36%), and mobile health (4%). Most of the studies in telehealth domain used synchronous methods, such as online and phone- or video-call consultations. Mobile applications were developed as self-triage, self-scheduling, and information delivery tools during the COVID-19 pandemic. The most common types of prediction models among the reviewed studies were neural network (49%), classification (42%), and linear models (4.5%). Conclusion: The present study showed clinical informatics applications during COVID-19 and identified current gaps in this field. Health information technology and clinical informatics seem to be useful in assisting clinicians and managers to combat COVID-19. The most common domains in clinical informatics for research on the COVID-19 crisis were prediction models and telehealth. It is suggested that future researchers conduct scoping reviews to describe and analyze other levels of medical informatics, including bioinformatics, imaging informatics, and public health informatics.

Telehealth-Based Services During the COVID-19 Pandemic: A Systematic Review of Features and Challenges.

Background: As an ever-growing popular service, telehealth catered for better access to high-quality healthcare services. It is more valuable and cost-effective, particularly in the middle of the current COVID-19 pandemic. Accordingly, this study aimed to systematically review the features and challenges of telehealth-based services developed to support COVID-19 patients and healthcare providers. Methods: A comprehensive search was done for the English language and peer-reviewed articles published until November 2020 using PubMed and Scopus electronic databases. In this review paper, only studies focusing on the telehealth-based service to support COVID-19 patients and healthcare providers were included. The first author's name, publication year, country of the research, study objectives, outcomes, function type including screening, triage, prevention, diagnosis, treatment or follow-up, target population, media, communication type, guideline-based design, main findings, and challenges were extracted, classified, and tabulated. Results: Of the 5,005 studies identified initially, 64 met the eligibility criteria. The studies came from 18 countries. Most of them were conducted in the United States and China. Phone calls, mobile applications, videoconferencing or video calls, emails, websites, text messages, mixed-reality, and teleradiology software were used as the media for communication. The majority of studies used a synchronous communication. The articles addressed the prevention, screening, triage, diagnosis, treatment, and follow-up aspects of COVID-19 which the most common purpose was the patients' follow-up (34/64, 53%). Thirteen group barriers were identified in the literature, which technology acceptance and user adoption, concerns about the adequacy and accuracy of subjective patient assessment, and technical issues were the most frequent ones. Conclusion: This review revealed the usefulness of telehealth-based services during the COVID-19 outbreak and beyond. The features and challenges identified through the literature can be helpful for a better understanding of current telehealth approaches and pointed out the need for clear guidelines, scientific evidence, and innovative policies to implement successful telehealth projects.

Development of a National Roadmap for Electronic Prescribing Implementation.

BACKGROUND: In July 2015, Iran Food and Drug Administration convened a multi-stakeholder workgroup (workgroup) to help develop recommendations for electronic prescribing implementation in Iran. OBJECTIVES: In general, the consensus of the workgroup was to focus on solutions that incrementally reduce the burden on patients, providers, and payers, and require minimal rework by using national standards that have already been used for Health Information Interchange. We used a road mapping method which includes a number of systematic steps and is adapted from the standard scientific method. Medical Informatics Experts Developed protocols for Scoping Reviews, Systematic reviews and Health Technology Assessment study and then collected evidence from peer-reviewed scholarly journal publications and gray literature. Health Insurance companies representatives and Electronic Prescribing pilot studies executives were asked to report their experiences in the case of e-prescribing. RESULTS: After five meetings, by comparing and contrasting the national and international evidence, the recommendations were finalized in expert panels. In this paper, we report recommendations from this roadmap.

First principles study on spin and orbital magnetism of 3d transition metal monatomic nanowires (Mn, Fe and Co).

We have demonstrated the electronic structure and magnetic properties of 3d transition metal nanowires (Mn, Fe and Co) in the framework of relativistic density functional theory. The equilibrium bond lengths were optimized using the generalized gradient approximation. In a full relativistic regime individual spin and orbital moments induced from spin polarization via spin-orbit coupling were calculated. In order to get an upper estimate for orbital moments, we used an orbital polarization correction to our exchange-correlation functional. We found that the orbital magnetic moments of Fe and Co linear chains are strongly enhanced in the presence of an orbital polarization correction. We have calculated the exchange coupling parameters between two nearest-neighbor magnetic atoms according to a Heisenberg-like model in the presence of the orbital polarization correction. We found that the Co and Fe nanowires behave like a ferromagnetic linear chain whereas a Mn monatomic nanowire remains antiferromagnetic.