bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial Intelligence–Assisted Diagnosis of Anterior Cruciate Ligament Tears From Magnetic Resonance Images: Algorithm Development and Validation Study
Authors: Chen Kun-Hui , Yang Chih-Yu , Wang Hsin-Yi , Ma Hsiao-Li , Lee Oscar Kuang-Sheng 
Published: January 2023

Background
Anterior cruciate ligament (ACL) injuries are common in sports and are critical knee injuries that require prompt diagnosis. Magnetic resonance imaging (MRI) is a strong, noninvasive tool for detecting ACL tears, which requires training to read accurately. Clinicians with different experiences in reading MR images require different information for the diagnosis of ACL tears. Artificial intelligence (AI) image processing could be a promising approach in the diagnosis of ACL tears.

Objective
This study sought to use AI to (1) diagnose ACL tears from complete MR images, (2) identify torn-ACL images from complete MR images with a diagnosis of ACL tears, and (3) differentiate intact-ACL and torn-ACL MR images from the selected MR images.

Methods
The sagittal MR images of torn ACL (n=1205) and intact ACL (n=1018) from 800 cases and the complete knee MR images of 200 cases (100 torn ACL and 100 intact ACL) from patients aged 20-40 years were retrospectively collected. An AI approach using a convolutional neural network was applied to build models for the objective. The MR images of 200 independent cases (100 torn ACL and 100 intact ACL) were used as the test set for the models. The MR images of 40 randomly selected cases from the test set were used to compare the reading accuracy of ACL tears between the trained model and clinicians with different levels of experience.

Results
The first model differentiated between torn-ACL, intact-ACL, and other images from complete MR images with an accuracy of 0.9946, and the sensitivity, specificity, precision, and F1-score were 0.9344, 0.9743, 0.8659, and 0.8980, respectively. The final accuracy for ACL-tear diagnosis was 0.96. The model showed a significantly higher reading accuracy than less experienced clinicians. The second model identified torn-ACL images from complete MR images with a diagnosis of ACL tear with an accuracy of 0.9943, and the sensitivity, specificity, precision, and F1-score were 0.9154, 0.9660, 0.8167, and 0.8632, respectively. The third model differentiated torn- and intact-ACL images with an accuracy of 0.9691, and the sensitivity, specificity, precision, and F1-score were 0.9827, 0.9519, 0.9632, and 0.9728, respectively.

Conclusions
This study demonstrates the feasibility of using an AI approach to provide information to clinicians who need different information from MRI to diagnose ACL tears.
mHealth Apps Targeting Obesity and Overweight in Young People: App Review and Analysis
Authors: Vlahu-Gjorgievska Elena , Burazor Andrea , Win Khin Than , Trajkovik Vladimir 
Published: January 2023

Background
Overweight and obesity have been linked to several serious health problems and medical conditions. With more than a quarter of the young population having weight problems, the impacts of overweight and obesity on this age group are particularly critical. Mobile health (mHealth) apps that support and encourage positive health behaviors have the potential to achieve better health outcomes. These apps represent a unique opportunity for young people (age range 10-24 years), for whom mobile phones are an indispensable part of their everyday living. However, despite the potential of mHealth apps for improved engagement in health interventions, user adherence to these health interventions in the long term is low.

Objective
The aims of this research were to (1) review and analyze mHealth apps targeting obesity and overweight and (2) propose guidelines for the inclusion of user interface design patterns (UIDPs) in the development of mHealth apps for obese young people that maximizes the impact and retention of behavior change techniques (BCTs).

Methods
A search for apps was conducted in Google Play Store using the following search string: [“best weight loss app for obese teens 2020”] OR [“obesity applications for teens”] OR [“popular weight loss applications”]. The most popular apps available in both Google Play and Apple App Store that fulfilled the requirements within the inclusion criteria were selected for further analysis. The designs of 17 mHealth apps were analyzed for the inclusion of BCTs supported by various UIDPs. Based on the results of the analysis, BCT-UI design guidelines were developed. The usability of the guidelines was presented using a prototype app.

Results
The results of our analysis showed that only half of the BCTs are implemented in the reviewed apps, with a subset of those BCTs being supported by UIDPs. Based on these findings, we propose design guidelines that associate the BCTs with UIDPs. The focus of our guidelines is the implementation of BCTs using design patterns that are impactful for the young people demographics. The UIDPs are classified into 6 categories, with each BCT having one or more design patterns appropriate for its implementation. The applicability of the proposed guidelines is presented by mock-ups of the mHealth app “Morphe,” intended for young people (age range 10-24 years). The presented use cases showcase the 5 main functionalities of Morphe: learn, challenge, statistics, social interaction, and settings.

Conclusions
The app analysis results showed that the implementation of BCTs using UIDPs is underutilized. The purposed guidelines will help developers in designing mHealth apps for young people that are easy to use and support behavior change. Future steps involve the development and deployment of the Morphe app and the validation of its usability and effectiveness.
Overdose Alert and Response Technologies: State-of-the-art Review
Authors: Oteo Alberto , Daneshvar Hadi , Baldacchino Alexander , Matheson Catriona 
Published: January 2023

Background
Drug overdose deaths, particularly from opioids, are a major global burden, with 128,000 deaths estimated in 2019. Opioid overdoses can be reversed through the timely administration of naloxone but only if responders are able to administer it. There is an emerging body of research and development in technologies that can detect the early signs of an overdose and facilitate timely responses.

Objective
Our aim was to identify and classify overdose-specific digital technologies being developed, implemented, and evaluated.

Methods
We conducted a “state-of-the-art review.” A systematic search was conducted in MEDLINE, Embase, Web of Science, Scopus, ACM, IEEE Xplore, and SciELO. We also searched references from articles and scanned the gray literature. The search included terms related to telehealth and digital technologies, drugs, and overdose and papers published since 2010. We classified our findings by type of technology and its function, year of publication, country of study, study design, and theme. We performed a thematic analysis to classify the papers according to the main subject.

Results
Included in the selection were 17 original research papers, 2 proof-of-concept studies, 4 reviews, 3 US government grant registries, and 6 commercial devices that had not been named in peer-reviewed literature. All articles were published between 2017 and 2022, with a marked increase since 2019. All were based in or referred to the United States or Canada and concerned opioid overdose. In total, 39% (9/23) of the papers either evaluated or described devices designed to monitor vital signs and prompt an alert once a certain threshold indicating a potential overdose has been reached. A total of 43% (10/23) of the papers focused on technologies to alert potential responders to overdoses and facilitate response. In total, 48% (11/23) of the papers and 67% (4/6) of the commercial devices described combined alert and response devices. Sensors monitor a range of vital signs, such as oxygen saturation level, respiratory rate, or movement. Response devices are mostly smartphone apps enabling responders to arrive earlier to an overdose site. Closed-loop devices that can detect an overdose through a sensor and automatically administer naloxone without any external intervention are still in the experimental or proof-of-concept phase. The studies were grouped into 4 themes: acceptability (7/23, 30%), efficacy or effectiveness (5/23, 22%), device use and decision-making (3/23, 13%), and description of devices (6/23, 26%).

Conclusions
There has been increasing interest in the research and application of these technologies in recent years. Literature suggests willingness to use these devices by people who use drugs and affected communities. More real-life studies are needed to test the effectiveness of these technologies to adapt them to the different settings and populations that might benefit from them.
Understanding Public Attitudes and Willingness to Share Commercial Data for Health Research: Survey Study in the United Kingdom
Authors: Hirst Yasemin , Stoffel Sandro T , Brewer Hannah R , Timotijevic Lada , Raats Monique M , Flanagan James M 
Published: January 2023

Background
Health research using commercial data is increasing. The evidence on public acceptability and sociodemographic characteristics of individuals willing to share commercial data for health research is scarce.

Objective
This survey study investigates the willingness to share commercial data for health research in the United Kingdom with 3 different organizations (government, private, and academic institutions), 5 different data types (internet, shopping, wearable devices, smartphones, and social media), and 10 different invitation methods to recruit participants for research studies with a focus on sociodemographic characteristics and psychological predictors.

Methods
We conducted a web-based survey using quota sampling based on age distribution in the United Kingdom in July 2020 (N=1534). Chi-squared tests tested differences by sociodemographic characteristics, and adjusted ordered logistic regressions tested associations with trust, perceived importance of privacy, worry about data misuse and perceived risks, and perceived benefits of data sharing. The results are shown as percentages, adjusted odds ratios, and 95% CIs.

Results
Overall, 61.1% (937/1534) of participants were willing to share their data with the government and 61% (936/1534) of participants were willing to share their data with academic research institutions compared with 43.1% (661/1534) who were willing to share their data with private organizations. The willingness to share varied between specific types of data—51.8% (794/1534) for loyalty cards, 35.2% (540/1534) for internet search history, 32% (491/1534) for smartphone data, 31.8% (488/1534) for wearable device data, and 30.4% (467/1534) for social media data. Increasing age was consistently and negatively associated with all the outcomes. Trust was positively associated with willingness to share commercial data, whereas worry about data misuse and the perceived importance of privacy were negatively associated with willingness to share commercial data. The perceived risk of sharing data was positively associated with willingness to share when the participants considered all the specific data types but not with the organizations. The participants favored postal research invitations over digital research invitations.

Conclusions
This UK-based survey study shows that willingness to share commercial data for health research varies; however, researchers should focus on effectively communicating their data practices to minimize concerns about data misuse and improve public trust in data science. The results of this study can be further used as a guide to consider methods to improve recruitment strategies in health-related research and to improve response rates and participant retention.
Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study
Authors: Gaviria-Valencia Simon , Murphy Sean P , Kaggal Vinod C , McBane II Robert D , Rooke Thom W , Chaudhry Rajeev , Alzate-Aguirre Mateo , Arruda-Olson Adelaide M 
Published: January 2023

Background
Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports.

Objective
This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification.

Methods
The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard.

Results
A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97).

Conclusions
Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA.
Artificial Intelligence–Based Ethical Hacking for Health Information Systems: Simulation Study
Authors: He Ying , Zamani Efpraxia , Yevseyeva Iryna , Luo Cunjin 
Published: January 2023

Background
Health information systems (HISs) are continuously targeted by hackers, who aim to bring down critical health infrastructure. This study was motivated by recent attacks on health care organizations that have resulted in the compromise of sensitive data held in HISs. Existing research on cybersecurity in the health care domain places an imbalanced focus on protecting medical devices and data. There is a lack of a systematic way to investigate how attackers may breach an HIS and access health care records.

Objective
This study aimed to provide new insights into HIS cybersecurity protection. We propose a systematic, novel, and optimized (artificial intelligence–based) ethical hacking method tailored specifically for HISs, and we compared it with the traditional unoptimized ethical hacking method. This allows researchers and practitioners to identify the points and attack pathways of possible penetration attacks on the HIS more efficiently.

Methods
In this study, we propose a novel methodological approach to ethical hacking in HISs. We implemented ethical hacking using both optimized and unoptimized methods in an experimental setting. Specifically, we set up an HIS simulation environment by implementing the open-source electronic medical record (OpenEMR) system and followed the National Institute of Standards and Technology’s ethical hacking framework to launch the attacks. In the experiment, we launched 50 rounds of attacks using both unoptimized and optimized ethical hacking methods.

Results
Ethical hacking was successfully conducted using both optimized and unoptimized methods. The results show that the optimized ethical hacking method outperforms the unoptimized method in terms of average time used, the average success rate of exploit, the number of exploits launched, and the number of successful exploits. We were able to identify the successful attack paths and exploits that are related to remote code execution, cross-site request forgery, improper authentication, vulnerability in the Oracle Business Intelligence Publisher, an elevation of privilege vulnerability (in MediaTek), and remote access backdoor (in the web graphical user interface for the Linux Virtual Server).

Conclusions
This research demonstrates systematic ethical hacking against an HIS using optimized and unoptimized methods, together with a set of penetration testing tools to identify exploits and combining them to perform ethical hacking. The findings contribute to the HIS literature, ethical hacking methodology, and mainstream artificial intelligence–based ethical hacking methods because they address some key weaknesses of these research fields. These findings also have great significance for the health care sector, as OpenEMR is widely adopted by health care organizations. Our findings offer novel insights for the protection of HISs and allow researchers to conduct further research in the HIS cybersecurity domain.
Update on the treatment of multisystem inflammatory syndrome in children associated with COVID-19
Authors: Long Fangyuan , Zhu Shiheng , Wang Zeguang , Zhang Shungeng , He Jinlong , Ge Xinbin , Ning Jun 
Published: January 2023

In late 2019, SARS-CoV-2 was detected in China and spread worldwide. In rare cases, children who were infected with COVID-19 may develop multisystem inflammatory syndrome (MIS-C), which could have higher mortality than COVID-19 itself. Therefore, diagnosis and management are critical for treatment. Specifically, most of the initial treatment options of MIS-C choose intravenous immunoglobulin (IVIG) and steroids as the first-line treatment for patients. Moreover, antagonists of some cytokines are used as potential future therapeutics. Of note, therapeutic plasmapheresis can be used as a treatment for refractory severe MIS-C. We believe that each patient, especially those with comorbid conditions, should have individualized treatment based on both multidisciplinary consensus approach and expert opinion.
The Current Trends in Microspheres: A Review
Authors: Ingle Tusharkumar Gautam , Pande Shrikant D. , Sawarkar Roshni , Padole Dipti 
Published: January 2023

The idea of targeted drug delivery is to concentrate the treatment in the target tissues while lowering the relative concentration of the drug in the non-target tissues. As a result, the medication is concentrated at the desired location. As a result, the medication has no effect on the tissues nearby. Therefore, by combining the drug with a carrier particle like microspheres, nanoparticles, liposomes, niosomes, etc. that regulates the release and absorption characteristics of the drug, carrier technology offers an intelligent way for drug delivery. Microspheres are naturally biodegradable powders made of proteins or synthetic polymers that flow freely and preferably have a particle size of less than 200 m. If improved, it is the trustworthy method for maintaining the desired concentration at the site of interest without unfavourable effects and reliably delivering the drug to the target site with specificity. Microspheres attracted a lot of interest for their sustained release as well as their ability to direct anti-cancer medications to the tumour. Microspheres will play a key role in novel drug delivery in the future by fusing together a variety of other strategies, especially in diseased cell sorting, diagnostics, gene & genetic materials, safe, targeted, and efficient in vivo delivery, and supplements as miniature representations of diseased organs and tissues in the body.
Keywords: Microspheres, controlled release, Types of microspheres, Methods of preparation, characterisation of microspheres, applications.
<sup>68</sup>Ga-FAPI-04: the first experience of clinical application in Russia
Authors: Ivanova A. A. , Stanzhevskii A. A. , Maystrenko D. N. , Shatik S. V. , Sysoev D. S. , Antuganova Yu. O. , Nadporojskii M. A. , Antuganov D. O. 
Published: January 2023

INTRODUCTION: According to the literature, 68Ga-FAPI-04 has receptor specificity for malignant neoplasms with overexpression of the fibroblast activation protein and is used to visualize various types of neoplasms, in particular head and neck cancer, gastrointestinal tract, lung, breast cancer with a high contrast ratio of the tumor to the background, and may possibly become an alternative to 18F-FDG.OBJECTIVE: The results of the first experience of using PET/CT with 68Ga-FAPI-04 in Russia.MATERIALS AND METHODS: A comparative analysis of the results of 68Ga-FAPI-04 and 18F-FDG PET/CTs with an interval of 1–3 days was carried out in 13 patients (four women and nine men) with various oncological diseases, examined from February to December 2021 in Granov Russian Research Center of Radiology and Surgical Technologies.RESULTS: In all 13 patients, it was possible to identify both primary tumors and their metastases with different tracer uptake. 68Ga-FAPI PET/CT compared with 18F-FDG PET/CT revealed more metastatic foci (135 vs 127) predominantly in the liver, peritoneum, mesentery, omentum, and brain due to low background uptake in these organs. In our observation, foci of increased 68Ga-FAPI-04 uptake localized in non-enlarged retroperitoneal lymph nodes in two patients. Also, in two patients with bone metastases from bladder cancer and stomach cancer, one false positive and one false negative result was obtained with 68Ga-FAPI-04.DISCUSSION: The high uptake of 68Ga-FAPI-04 in the tumor makes it a promising tracer for many types of cancer, especially in cases, where conventional 18F-FDG PET/CT faces limitations due to its pharmacokinetics. At the same time, PET/CT with 68Ga-FAPI-04, aimed at visualizing the tumor microenvironment, may have a higher sensitivity in detecting small lesions due to the predominance of stroma in them. 68Ga-FAPI showed better results in detecting both lytic and osteoblastic bone metastases compared to 18F-FDG.CONCLUSION: 68Ga-FAPI is a promising tracer for molecular imaging of most malignant neoplasms and requires further study. 68Ga-FAPI-04 can become an addition or a full-fledged solution when other tracers have limitations.