Data System Research Articles

An Enhanced Edge Computing Technique for Detection of Voltage Fluctuation in Grid-tied Renewable Energy

Renewable energy sources (RES) such as solar photovoltaic and wind are becoming the most attractive power generation options in many nations. Even while high penetration seems likely, power quality anomalies such as voltage fluctuation, harmonics, and frequency fluctuation associated with RES hinder seamless integration. The variability and unpredictability of these sources create the most oddities. In grid-tied renewable energy, monitoring power quality efficiently is crucial. Power grid monitoring solutions in related literature use sensor-based cloud and edge computing techniques. The existing systems struggle with excessive latency when delivering large amounts of generated data to the cloud. To fill this gap, a new approach for the detection and localization of voltage fluctuation is proposed in this study. The approach integrated three techniques namely; feed-forward neural network (FFNN), Stockwell transform, and anomaly-aware edge computing to detect and locate voltage fluctuation in a GtRE. Using MATLAB/Simulink, virtual emulation of a modified IEEE 33 Bus and a GtRE representing a section of Ado Ekiti (in Nigeria) low-voltage distribution grid are carried out for data generation and system evaluation. Feature extraction was carried out in a Python IDE using Stockwell transform. The voltage fluctuation events are detected and localized based on the extracted features using the trained FFNN model deployed and evaluated within three microcontroller-based computing devices. The proposed approach integrated anomaly-aware with edge computing to send only voltage data that are considered abnormal to a dedicated data center for visualization and storage. Performance evaluation of the proposed technique on the simulated GtRE demonstrates a significant decrease of 98% and 90% in latency when compared to cloud computing and conventional edge computing respectively. Comparison of the proposed approach to two closely related solutions in literature also demonstrates a 50% and 92.5 % reduction in latency. The contribution of the study is the reduced latency and minimal bandwidth utilization achieved by the implementation of the developed technique.

A Context-Based Perspective on Frost Analysis in Reuse-Oriented Big Data-System Developments

The large amount of available data, generated every second via sensors, social networks, organizations, and so on, has generated new lines of research that involve novel methods, techniques, resources, and/or technologies. The development of big data systems (BDSs) can be approached from different perspectives, all of them useful, depending on the objectives pursued. In particular, in this work, we address BDSs in the area of software engineering, contributing to the generation of novel methodologies and techniques for software reuse. In this article, we propose a methodology to develop reusable BDSs by mirroring activities from software product line engineering. This means that the process of building BDSs is approached by analyzing the variety of domain features and modeling them as a family of related assets. The contextual perspective of the proposal, along with its supporting tool, is introduced through a case study in the agrometeorology domain. The characterization of variables for frost analysis exemplifies the importance of identifying variety, as well as the possibility of reusing previous analyses adjusted to the profile of each case. In addition to showing interesting findings from the case, we also exemplify our concept of context variety, which is a core element in modeling reusable BDSs.

Clinician Staffing and Quality of Care in US Health Centers

Health centers are vital primary care safety nets for underserved populations, but optimal clinician staffing associated with quality care is unclear. Understanding the association of clinician staffing patterns with quality of care may inform care delivery, scope-of-practice policy, and resource allocation. To describe the association of clinician staffing models and ratios with quality-of-care metrics in health centers. This cross-sectional study of US health centers used data from the 2022 Health Resources and Services Administration Uniform Data System (UDS). Clinician staffing ratios, expressed as the fraction of full-time equivalents (FTEs) per 1000 visits of physicians, advanced practice registered nurses (APRNs), and physician associates (PAs) to total clinician FTEs at each health center. Percentage of eligible patients receiving the corresponding service or outcome for 14 individual clinical quality metrics collected by the UDS, including infant immunizations; screening for cancer, depression, tobacco use, hypertension, HIV, and glycated hemoglobin levels; weight and body mass index (BMI) assessment; and appropriate statin, aspirin, and/or antiplatelet therapy. This analysis of 791 health centers serving 16 114 842 patients (56.6% female) identified 5 clinician staffing models: balanced (similar FTEs of physicians, APRNs, and PAs; 152 [19.2%] of health centers), higher FTEs of APRNs than physicians (174 [22.0%]), higher FTEs of physicians than APRNs (160 [20.2%]), approximately equal FTEs of physicians and APRNs (263 [33.2%]), and large scale (42 [5.3%]). Adjusted linear models showed positive associations between physician FTEs per 1000 visits and cervical (β, 14.9; 95% CI, 3.1-26.7), breast (β, 15.7; 95% CI, 3.2-28.1), and colorectal (β, 18.3; 95% CI, 6.0-30.6) cancer screening. Generalized additive models showed nonlinear positive associations beginning at a physician FTE ratio of 0.45 (95% CI, 0.02-6.22) for infant vaccinations, 0.39 (95% CI, 0.05-2.21) for cervical cancer screening, 0.39 (95% CI, 0.02-1.67) for breast cancer screening, 0.47 (95% CI, 0.00-5.76) for HIV testing, and 0.70 (95% CI, 0.18-19.96) for depression in remission; APRN FTE ratio of 0.45 (95% CI, 0.17-7.46) for adult BMI assessment; and PA FTE ratio of 0.16 (95% CI, 0.11-3.88) for infant vaccinations. Staffing models were not associated with 7 of the 14 metrics analyzed. In this cross-sectional study of health centers, physician FTE ratio was associated with higher performance in cancer screening, infant vaccinations, and HIV testing; APRN FTE ratio was associated with higher performance in preventative health assessments; and PA FTE ratio was associated with higher performance in infant vaccination. These findings suggest that targeted staffing strategies may be associated with quality of care in certain domains and that tailored approaches to health center staffing based on community-specific needs are warranted.

Frontiers of Artificial Intelligence in Agricultural Sector: Trends and Transformations

Artificial intelligence (AI) in agriculture is transforming the sector by improving resources efficiency, sustainability and productivity. Our study examined a number of AI-related applications such as pest control, crop monitoring, precision farming and soil health evaluation. AI powered devices enables automated fertilization, harvesting and irrigation, and therefore, cutting down the labor expenses and resource waste. Predictive analytics in AI helps with crop yield and weather forecasts which ultimately improves the planning and risk management. The paper also discusses the challenges and limitations of AI adoption in agriculture, such as the need for reliable data, technical expertise and infrastructure investment. Ultimately, the findings highlights the AI can have positive transformative potential in creating resilient agricultural practices that can meet the demands of a growing global population while minimizing environmental impact. However, one of the biggest uses of AI is precision farming, which uses the technology to optimize inputs like water, fertilizer and pesticides by adjusting them to the unique requirements of the crop and the field. AI techniques also make it possible to detect the pests and diseases through picture recognition and predictive analytics, which ultimately minimizes the crop loss and allows for prompt interventions. Widespread use may be hampered by issues with data quality, model interpretability, expensive prices and system integration. Furthermore, issues with labor impact, regulatory frameworks and scalability complicate its adoption. In order to fully utilize AI in agriculture, researchers, farmers and policymakers must work together to overcome these challenges and develop workable and accessible solutions that are suited to a variety of agricultural environments. Present review also highlighted how AI involvement has the ability to revolutionize agricultural sector by developing resilient methods that can both minimize environmental effects and meet the needs of an expanding global population. The agriculture industry can set the path for a sustainable future by adopting AI advances and guaranteeing the environmental stewardship and goals of food safety and security.

Ring-Enhancement on CEUS: Is it Useful in the Differential Diagnosis of Solid Thyroid Nodules?

To investigate the efficiency of contrast-enhanced ultrasound (CEUS) features, particularly ring-enhancement patterns, in the differential diagnosis of thyroid nodules. 302 nodules with CEUS ring-enhancement were retrospectively enrolled, including 135 benign and 167 malignant ones. The ring-enhancement patterns were classified into regular and irregular hyper- or hypo-ring enhancement. Comparative analyses of ultrasound (US) and CEUS features between benign and malignant nodules were performed. The diagnostic performances of the ring-enhancement patterns and Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) were compared in nodules with different sizes. Irregular hypo-ring enhancement was much more common in malignancies than that in benign ones, and it was an independent predictor for thyroid malignant nodules. With irregular hypo-ring enhancement as the diagnostic criteria for malignant nodules, the specificity was higher than that of C-TIRADS (85.2% vs. 75.6%, p = .037) while the AUC was comparable (0.845 vs. 0.803, p = .136) in all nodules. When the nodule size was taken into account, the specificity and AUC were both significantly higher than those of C-TIRADS (92.8% vs. 81.1%, p = .021; 0.907 vs. 0.823, p = .026) in nodules ≥10 mm, which can decrease the unnecessary FNA rate. Irregular hypo-ring enhancement was a valuable CEUS feature for the differential diagnosis of thyroid nodules, especially in nodules ≥10 mm.

LI-RADS radiation-based treatment response algorithm for HCC: what to know and how to use it.

Locoregional treatments (LRT) continue to advance for hepatocellular carcinoma (HCC). Selective internal radiation therapy (SIRT) or transarterial radioembolization (TARE) with radioactive 90 Yttrium (Y90) microspheres is currently widely accepted, and external beam and stereotactic body radiation (EBRT/SBRT) are increasingly used as LRT1-5. Assessment of treatment response after these radiation-based therapies can be challenging, given that the adjacent liver also undergoes treatment related changes, inflammatory changes occur, and there is a variable time for response to develop. In 2017, the liver imaging reporting and data system (LI-RADS) workgroup initially developed a single algorithm for the imaging assessment of treatment response encompassing all types of locoregional therapies, the LI-RADS treatment response (LR-TR) algorithm. Recognizing that response and imaging patterns differ between radiation and non-radiation based therapies, the LR-TR working group recently updated the algorithm to reflect the unique characteristics of tumor response for therapies involving radiation. This article aims to elucidate the changes in the new version of the LI-RADS TR, with a guide for algorithm utilization and illustration of expected and unexpected findings post liver directed therapies for HCC.

Effect of patient age and breast parenchymal density on Breast Imaging-Reporting and Data System (BIRADS-4) Subcategorization

Background & Objectives: BI-RADS (Breast Imaging-Reporting and Data System) is a standard radiological risk assessment for breast lesions, including six categories, of which category four is the widest range of likelihood cancer risk (2% to 95%). This study aimed to evaluate the effect of patient age and ACR breast density on the positive predictive value (PPV) of BI-RADS 4 subcategorization (4A, 4B, and 4C). Methods: A retrospective study was conducted at King Fahed Hospital (KFH) between September 1, 2021, and June 30, 2022. PPV was calculated based on a histopathological report for all lesions. The correlation was made with patients’ age groups ranging from < 45 years, 45-55 years, and > 55 years and four types of breast density on mammography. We used IBM-SPSS for data synthesis. The Chi-square test was used to assess any correlation between variables. A p-value < 0.05 was considered statistically significant. Results: The mean age was 44.62 ±14.32. Of the 248 cases, 81% were benign, 17% were malignant, and 2% were high-risk lesions. The age-related PPV of each BI-RADS category showed no significant differences among all age groups. However, the breast composition-related PPV cases with BI-RADS categories differed significantly within subcategory 4C (p-value<0.001). Conclusions: There were no positive relationships between increasing age and PPV in BI-RADS 4 subcategories 4A, B, and C. Breast composition-related PPV showed significant differences with BI-RADS 4C subcategories. doi: https://doi.org/10.12669/pjms.40.10.9552 How to cite this: Ghunaim HA. Effect of patient age and breast parenchymal density on Breast Imaging-Reporting and Data System (BIRADS-4) Subcategorization. Pak J Med Sci. 2024;40(10):2356-2362. doi: https://doi.org/10.12669/pjms.40.10.9552 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Combined strategy of upfront CTCA and optimal treatment for stable chest pain: rationale and design of the CLEAR-CAD trial.

Patients with stable chest pain suspected of coronary artery disease (CAD) usually undergo multiple diagnostic tests to confirm or rule out obstructive CAD. Some tests may not effectively assess the presence of CAD, precluding optimal treatment. Adiagnostic strategy of upfront computed tomography coronary angiography (CTCA) combined with optimal medical therapy (OMT) tailored to the extent of CAD may be superior to standard care in preventing major adverse cardiac events. The CLEAR-CAD trial is aprospective, open-label, multicentre, randomised, superiority trial of an upfront CTCA-guided strategy in 6444 patients presenting in an outpatient setting with suspected CAD compared with standard care, in approximately 30participating centres in the Netherlands. The upfront CTCA-guided strategy consists of an initial CTCA which is assessed using the Coronary Artery Disease-Reporting and Data System (CAD-RADS2.0). In patients without CAD (CAD-RADS0) no specific cardiac medication is mandated. Patients with non-obstructive CAD (CAD-RADS 1-2) are treated with preventive OMT. Patients with obstructive CAD (CAD-RADS ≥ 3) are treated with preventive and anti-anginal OMT; in the presence of pharmacologically refractory symptoms patients undergo selective revascularisation after non-invasive functional imaging for myocardial ischaemia (≥ 10%). Patients with significant left main or proximal left anterior descending coronary artery stenosis on CTCA undergo direct invasive coronary angiography and subsequent revascularisation. The primary endpoint is the composite of all-cause death and myocardial infarction. The CLEAR-CAD trial is the first randomised study to investigate the efficacy of acombined upfront CTCA-guided medical and selective revascularisation strategy in an outpatient setting with suspected CAD compared with standard care.

Sex-specific analysis of acute alcohol use in suicides and reporting of alcohol as a contributor to suicide deaths in New Zealand 2007-2020: a cross-sectional study of coronial data.

Acute alcohol use (AAU) can increase suicide risk. It is unknown if this effect differs by population sub-group in New Zealand, and what characteristics are associated with alcohol being coded as contributory to death, when AAU is identified. This study aimed to answer: 1) are the characteristics associated with suicide involving AAU different between females and males, and 2) among suicides that involved AAU, what factors are associated with alcohol being coded as a contributory factor? Secondary analysis was conducted of suicide data from 2007-2020, from the National Coronial Information System. Binomial regression models for females and males were used to estimate sex-specific differences in risk of suicide involving AAU. Poisson regression modelling was used to estimate the relative risk of alcohol being coded as contributory where AAU was identified. Suicide was more likely to involve AAU among Māori females (adjusted risk ratio [ARR] 1.35, 95% confidence interval [CI] 1.08-1.68) and Pacific females (ARR 1.75, 95% CI 1.22-2.51), compared to European females. Compared to males who were employed, all other employment statuses had significantly lower risk of suicide that involved AAU. Those who died by hanging (ARR 0.75, 95% CI 0.62-0.92) or firearms (ARR 0.55, 95% CI 0.38-0.90) were less likely to have alcohol coded as contributory, compared to those who died by poisoning. Targeted public health interventions designed by and for specific demographic groups (particularly Māori and Pacific females) are needed, alongside universal interventions that address social and structural determinants. Data systems and coding must accurately reflect the association between AAU and suicide in New Zealand.

The Critical Role of SREs in Safeguarding Healthcare Data Integrity and Availability

This article explores the critical role of Site Reliability Engineers (SREs) in the rapidly evolving landscape of healthcare technology. As healthcare organizations increasingly adopt digital solutions, SREs have become indispensable in ensuring data integrity, system availability, and regulatory compliance. The article discusses the unique challenges faced by SREs in healthcare environments, including managing vast amounts of sensitive patient data, implementing robust security measures, and maintaining high system reliability. It examines the key responsibilities of SREs, such as proactive monitoring, incident response, data consistency management, and security compliance. Furthermore, the article highlights the significant impact of SRE practices on healthcare delivery, including enhanced patient care, improved operational efficiency, risk mitigation, and cost reduction. Through a combination of statistical data and industry insights, this work underscores the growing importance of SREs in shaping the future of healthcare technology and improving overall patient outcomes.

Elderly predialysis care pathways and early morbidity-mortality upon start of dialysis.

The ageing of the population with advanced chronic kidney disease (CKD) increases the complexity of care pathways. Our aim was to identify subgroups of older people according to predialysis care pathways and describe their association with early morbidity-mortality after transition to dialysis. This study included 22,128 incident dialysis patients aged ≥ 75 years during 2009-2017 from the French nationwide registry linked to the National Health Data System. Predialysis care pathways were identified by ascending hierarchical classification based on preselected healthcare use indicators in the previous year. Their association with a composite outcome of death or hospitalization ≥ 50% of the time off dialysis within the first year of dialysis was studied by multivariable logistic regression accounting for demographics, comorbidities, functional status, conditions of dialysis initiation, socioeconomic deprivation index, and home-to-dialysis center travel time. Five care pathway profiles were identified, characterized by limited healthcare use (cluster 1, 28%), non-nephrology ambulatory care (cluster 2, 17%), nephrology ambulatory care (cluster 3, 37%), and a high level of non-nephrology or nephrology hospitalizations (clusters 4 and 5, both 9%). Profile subgroups did not differ according to patient age and comorbidities, but clusters 1, 2 and 4 displayed higher levels of social deprivation. Compared to cluster 3, the odds ratios of primary composite outcome were significantly increased for clusters 1, 4, and 5 (OR (95% CI) of 1.16 (1.08-1.25), 1.17 (1.05-1.32), and 1.12 (1.01-1.25) respectively). Moreover, prolonged hospitalizations were also more common in all groups, compared to cluster 3. Despite similar comorbidity profile, older people with advanced CKD experience very heterogeneous predialysis care pathways, some of which associated with higher burden of hospitalization after the transition to dialysis.

Data Leakage of the Indonesian Elections Commission in Legal Aspects of Personal Data Protection

Hackers illegally accessed the Indonesian General Elections Commission’s (KPU) voter data system to collect voter data to sell to third parties. The regulation requires accountability for voter data leakage to protect people’s privacy rights in Indonesia’s personal data protection concept. Legal analysis of the modus operandi of personal data sales cases results in patterns of information system vulnerabilities, which can then be used to prevent personal data leakage and improve voter data protection in Indonesian elections. One of the reasons for passing the personal data protection law is the rampant cases of confidential data leakage that occur in government and private institutions in Indonesia. Hackers of voter data systems aim to profit from personal data sold to third parties. The role of the cybersecurity task force team needs to be improved with more concrete arrangements in law enforcement, and mitigating voter data leakage can provide legitimacy for the implementation of credible, reliable, and professional elections in Indonesia. Establishing the task force will optimize the application of voter data systems in conducting general elections in Indonesia and improve personal data protection.

Blue-cloud DAB: developing a platform to harmonize, assess and disseminate marine metadata collections

AbstractThe integration and harmonization of marine data from diverse sources are vital for advancing global oceanographic research and ensuring seamless discovery and access of critical datasets. This paper presents a comprehensive analysis of the metadata harmonization efforts within the Blue-cloud 2026 project, which brokers data from numerous Blue Data Infrastructures (BDIs), leveraging the Discovery and Access Broker technology. The platform enables discovery and analysis of marine data collections while facilitating interoperability with other components of the marine digital ecosystem, such as virtual laboratories and the Semantic Analyzer. It also supports the flow of Blue-cloud information to other initiatives like the Global Earth Observations System of Systems. For data managers, the findings emphasize the importance of enhancing metadata quality, revealing discrepancies in core metadata elements, and the need for more consistent use of controlled vocabularies. For cyberinfrastructure developers, the study details the challenges of accommodating a wide array of interfaces from different data systems, highlighting the adoption of an extensible brokering architecture that harmonizes metadata models and protocols. The study also emphasizes the importance of metadata analysis in ensuring effective searches for end users, highlighting challenges in aggregating diverse sources, where data providers may have structured the content with different objectives compared to those of the system of systems. End users will gain insights into the current metadata content of Blue-cloud, enabling them to search and access data from multiple BDIs with an understanding of the technical complexities behind the scenes.

Estimation of liver standardized uptake value in F18-FDG PET/CT scanning: impact of different malignancies, blood glucose level, body weight normalization, and imaging systems.

The aim of this work was to investigate homogeneity and stability of liver SUV in terms of different malignancies considering different body normalization schemes and blood glucose concentrations as well as PET/CT imaging systems. The study included 207 patients with four different types of cancers namely breast, lymphoma, lung, and bone-metastasis. Data acquisition was performed with GE Discovery IQ, Biograph mCT, uMI 550, and Ingenuity TF64 after a single intravenous injection of 194 ± 67.5 MBq of 18F-FDG. In body weight normalization, SUVmax and SUVmean in bone-mets as well as SUVmean in lung patients were not statistically different among scanners especially for data corrected for glucose levels (p = 0.062, 0.121, and 0.150, respectively). In SUVlbm derived from lung patients, there was no significant differences in Philips in comparison to GE and Siemens (both, p > 0.05) for data corrected and not corrected for glucose levels. In SUVbsa, the only non-significant difference revealed among scanners was in the measurements of SUVmean obtained from lung and bone-mets (p = 0.107 and 0.114) both corrected for glucose levels. In SUVbmi, SUVmean of lung and bone-mets as well as SUVmax of bone-mets showed a non-significant differences among the four different scanning systems (p = 0.303, 0.091, and 0.222, respectively) for data corrected for glucose levels. Liver glucose correction needs further investigations in individual tumors but could be potentially affected by whether measurements are made on SUVmean versus SUVmax, body weight normalization, as well as the imaging system. As such, selection of normalization to body weight method should be carefully selected before clinical adoption and clinically adopted and body surface area would provide the highest correlation. As such, normalization of body weight should be carefully made before clinical adoption. SUVmean proves to be useful and stable metric when liver is corrected for blood glucose levels.

Prospective evaluation of PI-RADSv2.1 using multiparametric and biparametric MRI for detecting clinically significant prostate cancer based on MRI/US fusion-guided biopsy.

To evaluate the cancer detection rates for each category of Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1) using multiparametric magnetic resonance imaging (mpMRI) and biparametric MRI (bpMRI) based on MRI/ultrasound (US)-fusion biopsy. This prospective study included participants who underwent mpMRI or bpMRI with a PI-RADSv2.1 interpretation and subsequently received MRI/US-fusion biopsy between August 2022 and December 2023. The lesion-based detection rates of clinically significant prostate cancer (csPCa) in each PI-RADSv2.1 category and the correlation between PI-RADSv2.1 categories and International Society of Urological Pathology (ISUP) grade groups were analyzed. The diagnostic performance of PI-RADSv2.1 in predicting csPCa was evaluated, and diagnostic performance of mpMRI and bpMRI was compared using cut-offs, with PI-RADSv2.1 categories ≥ 3 or ≥ 4 defined as positive. A total of 247 lesions from 216 participants were included in this study. A total of 157 patients underwent mpMRI and the remaining 59 underwent bpMRI. The csPCa detection rates for each PI-RADSv2.1 category of mpMRI and bpMRI were as follows: category 1, 0% (0/11); 2, 13% (3/23); 3, 16% (5/31); 4, 60% (43/72); 5, 65% (26/40), in mpMRI; category 1, 0% (0/4); 2, 33% (1/3); 3, 25% (3/12); 4, 61% (19/31); 5, 75% (15/20) in bpMRI. PI-RADSv2.1 categories were significantly positively associated with csPCa detection rates in both mpMRI and bpMRI (p < 0.0001 and p = 0.00048, respectively). PI-RADSv2.1 categories correlated with ISUP grade groups for mpMRI and bpMRI (p < 0.0001 for both). There were no significant differences in the detection rates between mpMRI and bpMRI for PI-RADS v2.1 positive and negative lesions. PI-RADSv2.1 using mpMRI and bpMRI could stratify the risk of csPCa, and the csPCa detection rate of bpMRI was compatible with that of mpMRI using cut-offs of PI-RADSv2.1 categories ≥ 3 or ≥ 4.

Risk of Bias in Liver Imaging Reporting and Data System Studies Using QUADAS-2.

Purpose: Use a tailored version of the Quality Assessment of Diagnostic Accuracy Studies tool to evaluate risk of bias and applicability across LIRADS related publications. Method: A tailored QUADAS-2 tool was created through consensus approach to assess risk of bias and applicability across 37 LI-RADS related publications. Studies were selected from 2017 to 2022 using the assistance of experienced hospital librarians to search for studies evaluating the diagnostic accuracy of CT, MRI, or contrast-enhanced ultrasound for HCC using LI-RADS through multiple different databases. QUADAS-2 assessments were performed in duplicate and independently by 2 authors with experience using the QUADAS-2 tool. Disagreements were resolved with a third expert reviewer. Consensus QUADAS-2 assessments were tabulated for each domain. Results: Using the tailored QUADAS-2 tool, 31 of the 37 included LI-RADS studies were assessed as high risk of bias, and 9 out of 37 studies demonstrated concerns for applicability. Patient selection (21 out of 37 studies) and flow/timing (24 out of 37 studies) domains demonstrated the highest risk of bias. 6 out of 37 studies in the index domain demonstrated high risk of bias. 2 out of 37 studies showed high risk of bias in the reference standard domain. Conclusion: A significant proportion of LI-RADS research is at risk of bias with concerns for applicability. Identifying risk of bias in such research is essential to recognize limitations of a study that may affect the validity of the results. Areas for improvement in LI-RADS research include reducing selection bias, avoiding inappropriate exclusions, and decreasing verification bias.

Early Effects of the End-Stage Renal Disease Treatment Choices Model on Kidney Transplant Waitlist Additions.

Despite the mortality benefit of kidney transplantation over dialysis, only 13% of patients with end-stage kidney disease are on the transplant waitlist. Given the low rates of transplant waitlisting in the US, Medicare launched the End-Stage Renal Disease Treatment Choices (ETC) Model in 2021, the largest mandatory trial of payment incentives in kidney disease, which randomized 30% of healthcare markets to financial bonuses/penalties to improve kidney transplantation and home dialysis use. This study examines the effect of ETC payment adjustments on US kidney transplant waitlist additions. Using data from the Organ Procurement and Transplantation Network registry, we examined kidney transplant waitlisting trends between 01/01/2017 and 06/30/2022. Participants were divided into intervention and control arms of the ETC Model. Using an interrupted time series design, we compared slope changes in waitlist additions post-ETC Model implementation (implementation date: 01/01/2021) between the two arms, while accounting for differential changes during the COVID-19 pandemic. Results were stratified by race and ethnicity (White, Black, Hispanic, and other). To examine balance between the two ETC arms, we conducted supplementary analyses using United States Renal Data System and Medicare data. Following implementation of the ETC Model, there were 5,550 waitlist additions in the intervention and 11,332 additions in the control arm (versus 14,023 and 30,610 additions before the ETC Model). Post-ETC, there were no significant differences in kidney transplant waitlist additions between the two arms for the overall cohort (slope difference 6.9 new listings/month, 95% CI: -7.4 to 21.1) or among either White (slope difference 2.6/month, 95% CI -3.0 to 8.1), Black (slope difference 2.2/month, 95% CI: -4.3 to 8.7), or Hispanic (slope difference 0.2/month, 95% CI: -4.5 to 4.9) patients. In the 18 months following implementation, the ETC Model was not associated with significant changes in new kidney transplant waitlist additions.

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Background: Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this study was to evaluate the performance of a recent Food and Drug Administration (FDA)-cleared AI tool for detection of malignancy in thyroid nodules on US. Methods: One year of consecutive thyroid US with ≥1 nodule from Duke University Hospital and its affiliate community hospital (649 nodules from 347 patients) were retrospectively evaluated. Included nodules had ground truth diagnoses by surgical pathology, fine needle aspiration (FNA), or three-year follow-up US showing stability. An FDA-cleared AI tool (Koios DS Thyroid) analyzed each nodule to generate (i) American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) descriptors, scores, and follow-up recommendations and (ii) an AI-adapter score to further adjust risk assessments and recommendations. Four groups were then compared: (i) Koios with AI-adapter, (ii) Koios without AI-adapter, (iii) clinical radiology report, and (iv) radiology report combined with AI-adapter. Performance of the final recommendations (FNA or no FNA) was determined based on ground truth, and comparison between the four groups was made using sensitivity, specificity, and receiver-operating-curve analysis. Results: Of 649 nodules, 32 were malignant and 617 were benign. Performance of Koios with AI-adapter enabled was similar to radiologists (area under the curve [AUC] 0.70 for both, [CI 0.60-0.81] and [0.60-0.79], respectively). Koios with AI-adapter had improved specificity compared to radiologists (0.63 [CI: 0.59-0.67] versus 0.43 [CI: 0.38-0.48]) but decreased sensitivity (0.69 [CI: 0.50-0.83) versus 0.81 [CI: 0.61, 0.92]). Highest performance was seen when the radiology interpretation was combined with the AI-adapter (AUC 0.76, [CI: 0.67-0.85]). Combined with the AI-adapter, radiologist specificity improved from 0.43 ([CI: 0.38-0.48]) to 0.53 ([CI: 0.49-0.58]) (McNemar's test p < 0.001), resulting in 17% fewer FNA recommendations, with unchanged sensitivity (0.81, p = 1). Conclusion: Koios DS demonstrated standalone performance similar to radiologists, though with lower sensitivity and higher specificity. Performance was best when radiologist interpretations were combined with the AI-adapter component, with improved specificity and reduced unnecessary FNA recommendations.

Information System for Processing Data of Swallow's Nests at Father Jimris's Swallow House Prabumulih City

Father jimris' swallow house is a rolling business in the dragon-pineapple industry located in the village of the prabumutown. The date-processing process in the Mr. Jimris' home is still done by hand, causing employees or old employees to do work, and this research is aimed at creating an application or a swallow's nest data processing system using PHP programming languages and mysql as databases. The development method used is rads (rapid aplication development) consisting of need planning, design and application processes. The results of this study are expected to make it easier for employees to process and access data and be able to make data processing more effective and efficient

Outcomes among Patients with End-Stage Kidney Disease and Chronic Limb-Threatening Ischemia: A Population-based Cohort Study.

To understand mortality and secondary outcomes in patients with both end-stage kidney disease (ESKD) and chronic limb-threatening ischemia (CLTI) after no procedural treatment, primary amputation, endovascular treatment, and open surgery. ESKD and CLTI commonly cooccur and limited prior work has demonstrated poor outcomes including one-year survival despite treatment. We conducted a retrospective national cohort study of United States Renal Data System data from January 1, 2016 to December 31, 2019 to determine mortality, major postoperative complications, and other outcomes. We performed an exploratory analysis comparing two-year survival by treatment using propensity matching. Of 1,876,652 records with a CLTI diagnosis, we identified 3,908 patients with ESKD and an incident CLTI diagnosis. Mean age at CLTI diagnosis was 65.7 years and 2,405 (61.5%) were male. 2,696 (69.0%) had no procedural treatment, 609 (15.6%) had major limb amputation, 439 (11.2%) had endovascular treatment, and 164 (4.2%) had open surgery. There was 44.9% mortality at one year, along with 41.8% major postoperative complications and 52.6% readmissions at 90 days. Comparing two-year survival, we found no differences between the amputation and endovascular cohorts (P=0.08) and between endovascular and open (P=.06). There was superior two-year survival in the open surgery cohort compared to the amputation cohort (P=0.002). Patients living with both ESKD and CLTI experience poor outcomes irrespective of treatment. Exploratory analyses demonstrated that two-year survival among the three principal procedural treatments was similar except for superior survival among patients undergoing open therapy compared to primary amputation.