MACHINE LEARNING TECHNIQUES FOR RETINOPATHY DETECTION IN DIABETIC PATIENTS by Ajay Kushwaha, Ahankari Sachin Suresh, Chennoju Phanindra, Anil Kumar Sahu, Devanand Bhonsle, Yamini Chouhan

“…Conventional techniques for identifying retinopathies depend on ophthalmologists manually examining retinal pictures, a laborious process prone to human error. By using cutting-edge algorithms and artificial intelligence (AI) to evaluate retinal images, automated image analysis presents a promising option that makes retinopathies in diabetes patients quickly, accurately, and scalable to identify. …”
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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Timely detection and intervention are crucial in preventing severe health outcomes. …”
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An AI-Based Digital Scanner for <i>Varroa destructor</i> Detection in Beekeeping by Daniela Scutaru, Simone Bergonzoli, Corrado Costa, Simona Violino, Cecilia Costa, Sergio Albertazzi, Vittorio Capano, Marko M. Kostić, Antonio Scarfone

“…The results highlighted the high repeatability of the measurements (R² ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. …”
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An innovative model based on machine learning and fuzzy logic for tracking lower limb exercises in stroke patients by Utpal Chandra Das, Ngoc Thien Le, Timporn Vitoonpong, Chalermdej Prapinpairoj, Kawee Anannub, Wasan Akarathanawat, Aurauma Chutinet, Nijasri Charnnarong Suwanwela, Pasu Kaewplung, Surachai Chaitusaney, Watit Benjapolakul

“…The model facilitates real-time evaluation of rehabilitation progress by clinicians, with the lowest observed error rate of $$0.34^\circ$$ of angle measurement. …”
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Patient-specific fluid–structure simulations of anomalous aortic origin of right coronary arteriesCentral MessagePerspective by Michael X. Jiang, MD, MEng, Muhammad O. Khan, PhD, Joanna Ghobrial, MD, Ian S. Rogers, MD, Gosta B. Pettersson, MD, PhD, Eugene H. Blackstone, MD, Alison L. Marsden, PhD

“…After we tuned the distal coronary resistances to achieve a stress flow rate triple that at rest, the simulations adequately matched the measured iFRs (r = 0.85, root-mean-square error = 0.04). The intramural lumen remained narrowed with simulated stress and resulted in lower iFRs without needing external compression from the pulmonary root. …”
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Culturally adapted developmental outcome measure for Aboriginal and Torres Strait Islander children: study protocol for the validation of the ASQ-STEPS by Patricia Eadie, Anita D’Aprano, Francesca Lami, Dan Cloney, Samantha Simpson, Cassie Boyle, Leah Lindrea-Morrison, Raelene Brunette, Emma Stubbs, Cuc Nguyen, Isabel Brookes

“…Introduction Early childhood education and intervention programmes can improve the developmental outcomes for priority groups of children. …”
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Effectiveness of Community Smoking Prevention Education in Enhancing Adolescent’s Knowledge in Banyuwangi Indonesia by Azimatul Karimah, Soetjipto, Izzatul Fithriyah, Sheila Maryam Gautama, Agustina Sjenny, Adila Taufik Syamlan, Nindy Adhilah, Dina Faizatur Rahmah, Williana Suwirman, Virzi Aliyyah Rahma, Teisha Jediya Videlia Marantika, Rifat Nurwita Kusumaningtyas, Andyani Pratiwi, Rizka Solehah

“…Eventually, the standard error mean for the pre-test and post-test is 2.073. …”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. …”
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Automatic smart brain tumor classification and prediction system using deep learning by Qurat Ul Ain Ishfaq, Rozi Bibi, Abid Ali, Faisal Jamil, Yousaf Saeed, Rana Othman Alnashwan, Samia Allaoua Chelloug, Mohammed Saleh Ali Muthanna

“…Early detection and diagnosis allow for timely intervention, potentially preventing the tumor from reaching an advanced stage. …”
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Beyond the homozygous paradigm: symptomatic partial biotinidase deficiency in a heterozygous child—first case report from Nepal by Jagdish Kunwar, Bijay Kunwar, Anup Ghimire, Aramva Bikram Adhikari, Binay Aryal, Sachchu Thapa, Bina Prajapati Manandhar

“…MRI, EEG was done thinking of the inborn error of metabolism. A whole exome sequencing identified a heterozygous pathogenic variant in the BTD gene (c.38_44delinsTCC, p.Cys13Phefs*36), and retrospective enzyme assay confirmed partial biotinidase deficiency (3.20 nmol/min/mL; ~25% of mean normal activity). …”
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Protocol for a single-arm feasibility trial of virtual family-centered rounds: increasing opportunities for family engagement among caregivers with language preference other than E... by Adrienne E. Hoyt-Austin, Erika N. Zerda, Daniel J. Tancredi, James P. Marcin, Audriana Ketchersid, Elva T. Horath, Trevor R. Bushong, Daniel S. Merriott, Patrick S. Romano, Kristin R. Hoffman, Jennifer L. Rosenthal

“…Exploratory outcomes include parent attendance, length of hospitalization of the infant, human milk feeding, frequency of medical error, parent-reported experience, parental comfort with their child’s care, and parental quality of life will be collected. …”
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Stress can be detected during emotion-evoking smartphone use: a pilot study using machine learning by Lydia Helene Rupp, Akash Kumar, Misha Sadeghi, Lena Schindler-Gmelch, Marie Keinert, Bjoern M. Eskofier, Bjoern M. Eskofier, Matthias Berking

“…IntroductionThe detrimental consequences of stress highlight the need for precise stress detection, as this offers a window for timely intervention. However, both objective and subjective measurements suffer from validity limitations. …”
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Smartphone screen time reduction improves mental health: a randomized controlled trial by Christoph Pieh, Elke Humer, Andreas Hoenigl, Julia Schwab, Doris Mayerhofer, Rachel Dale, Katja Haider

“…Significant group differences (p ≤ .05) were found post-intervention (t1) for depressive symptoms (Mean Difference (MD) = 2.11, Standard Error (SE) = 0.63, 95% Confidence Interval (CI) [0.87, 3.36]), sleep quality (MD = 2.59, SE = 0.97, 95% CI [0.66, 4.51]), well-being (MD = -1.54, SE = 0.68, 95% CI [.-2.89, -0.18]), and stress (MD = 6.91, SE = 3.48, 95% CI [0.01, 13.81]). …”
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Safety analysis of Yttrium-90 resin microsphere selective internal radiation therapy on malignant liver tumors by Jia CAI, Shiwei TANG, Rongli LI, Mingxin KONG, Hongyan DING, Xiaofeng YUAN, Yuying HU, Ruimei LIU, Xiaoyan ZHU, Wenjun LI, Haibin ZHANG, Guanwu WANG

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Comparison of aggregate and individual participant data approaches to meta-analysis of randomised trials: An observational study. by Jayne F Tierney, David J Fisher, Sarah Burdett, Lesley A Stewart, Mahesh K B Parmar

“…We extracted or estimated hazard ratios (HRs) and standard errors (SEs) for survival from trial reports and compared these with IPD equivalents at both the trial and meta-analysis level. …”
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Proactive synergistic control of PM2.5 and ozone through urban planning: longitudinal data analysis of 274 Chinese cities from 2005 to 2020 by Sha Li, Bin Zou, Ning Liu, Chenhao Xue, Shenxin Li, Yulong Wang, Yong Xu

“…Validation across 274 Chinese cities (2005–2020) showed the model’s robust predictive performance, achieving coefficients of determination (R2) of 0.88 and 0.89 for PM2.5 and O3, and root mean squared errors (RMSE) of 5.09 μg/m3 and 4.71 μg/m3 for PM2.5 and O3, respectively. …”
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The role of artificial intelligence in vascular care by Nehaar Nimmagadda, BS, Edouard Aboian, MD, Sharon Kiang, MD, Uwe Fischer, MD

“…Results: AI applications in vascular care have demonstrated high accuracy in image interpretation, enhanced risk prediction for postoperative outcomes, and greater precision in robotic-assisted interventions. Machine learning models have improved workflow efficiency, reduced diagnostic errors, and enabled early identification of vascular pathology. …”
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Quantifying Suicide Risk in Prostate Cancer: A SEER-Based Predictive Model by Jiaxing Du, Fen Zhang, Weinan Zheng, Xue Lu, Huiyi Yu, Jian Zeng, Sujun Chen

“…Additionally, potential coding errors and reporting biases may affect the accuracy of the results. …”
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Clinical Competency in Anesthesia Nursing Education: A Descriptive Cross-Sectional Study at Jundishapur University of Medical Sciences by Ali Khalafi, Sajjad Choopani, Nooshin Sarvi-Sarmeydani

“… Background: Clinical competence is an essential attribute for anesthesia nurses, as it directly influences patient safety, minimizes medical errors, and enhances surgical outcomes. In Iran, ensuring the clinical competence of anesthesia nurses is vital to addressing the growing demands of modern healthcare. …”
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Conceptualizing Clinicians&#x2019; Trust in Artificial Intelligence as a Function of Their Expertise, Workload, Patient Outcome, Diagnosis Difficulty, and AI Accuracy: A Systems Th... by Hamid Shamszare, Zaira Chaudhry, Manijeh Berenji, Avishek Choudhury

“…This may be attributed to their enhanced ability to detect AI errors, allowing for more calibrated and resilient trust. …”
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