Deep Transfer Learning for Classification of Late Gadolinium Enhancement Cardiac MRI Images into Myocardial Infarction, Myocarditis, and Healthy Classes: Comparison with Subjective... by Amani Ben Khalifa, Manel Mili, Mezri Maatouk, Asma Ben Abdallah, Mabrouk Abdellali, Sofiene Gaied, Azza Ben Ali, Yassir Lahouel, Mohamed Hedi Bedoui, Ahmed Zrig

“…To compare the VGG16 model’s performance in feature extraction, various pre-trained base models were evaluated: VGG19, DenseNet121, DenseNet201, MobileNet, InceptionV3, and InceptionResNetV2. The Support Vector Machine (SVM) classifier was evaluated and compared to MLP for the classification task. …”
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Evidence linking phthalate exposure to alterations of hematologic parameters in Chinese children: A cross-sectional study by Mei-Ting Wei, Ying Wen, Zhu-Xia Zhang, Xiu-Ju Liu, Feng-Xiang Wei, Wei-Qiang Liu, Li Zhou, Ding-Yan Chen, Yao Yao

“…To evaluate the connections between mPAEs and hematologic indices, both individually and in combination, several analytical approaches were used, including the generalized linear model (GLM), the Bayesian kernel machine regression (BKMR) model, and the quantile g-computation (QGC) model. …”
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Identification of climate-sensitive disease incidences in vietnam: A longitudinal retrospective analysis of infectious disease rates between 2014 and 2022 by Cuong Quoc Hoang, Quang Phuong Huynh Nguyen, Thao Phuong Huynh Nguyen, Hieu Trung Nguyen, Linh Thuy Hoang, Giang Huong Vu, Woong-Ki Kim, Hai Duc Nguyen

“…Method: We conducted a retrospective longitudinal study spanning 108 consecutive months from 2014 to 2022 in Can Tho, Vietnam to identify common infectious diseases (excluding tuberculosis, HIV, and COVID-19) and their associations with climate change and determine which common diseases presented concurrently with the COVID-19 period using multivariate linear regression, receiver operating characteristic (ROC) curve analysis, Bayesian kernel machine regression (BKMR) and orthogonal partial least squares discriminant analysis. …”
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Risk factors affecting polygenic score performance across diverse cohorts by Daniel Hui, Scott Dudek, Krzysztof Kiryluk, Theresa L Walunas, Iftikhar J Kullo, Wei-Qi Wei, Hemant Tiwari, Josh F Peterson, Wendy K Chung, Brittney H Davis, Atlas Khan, Leah C Kottyan, Nita A Limdi, Qiping Feng, Megan J Puckelwartz, Chunhua Weng, Johanna L Smith, Elizabeth W Karlson, Regeneron Genetics Center, Penn Medicine BioBank, Gail P Jarvik, Marylyn D Ritchie

“…Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. …”
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Multi-Feature Driver Variable Fusion Downscaling TROPOMI Solar-Induced Chlorophyll Fluorescence Approach by Jinrui Fan, Xiaoping Lu, Guosheng Cai, Zhengfang Lou, Jing Wen

“…Using the Random Forest (RF) model, we downscaled SIF data from 0.05° to 1 km based on invariant spatial scaling theory, focusing on the winter wheat growth cycle. Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R² = 0.931, RMSE = 0.052 mW/m²/nm/sr, and MAE = 0.031 mW/m²/nm/sr for 2018–2019 and R² = 0.926, RMSE = 0.058 mW/m²/nm/sr, and MAE = 0.034 mW/m²/nm/sr for 2019–2020. …”
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The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency by Md. Faiyazuddin, Syed Jalal Q. Rahman, Gaurav Anand, Reyaz Kausar Siddiqui, Rachana Mehta, Mahalaqua Nazli Khatib, Shilpa Gaidhane, Quazi Syed Zahiruddin, Arif Hussain, Ranjit Sah

“…The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. …”
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Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) by Gerald McGwin, Linda M Zangwill, Nicholas Evans, Shannon McWeeney, Cecilia S Lee, Bhavesh Patel, Jeffrey C Edberg, Cynthia Owsley, Aaron Lee, Cecilia Lee, Sally L Baxter, Michael Snyder, Samantha Hurst, Nicole Ehrhardt, Christopher Chute, Dawn S Matthies, Julia P Owen, Amir Bahmani, Sally Baxter, Edward Boyko, Aaron Cohen, Jorge Contreras, Garrison Cottrell, Virginia de Sa, Jeffrey Edberg, Irl Hirsch, Michelle Hribar, T.Y. Alvin Liu, Bonnie Maldenado, Sara Singer, Bradley Voytek, Joseph Yracheta, Linda Zangwill

“…Introduction Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approaches to study salutogenesis (transitioning from T2DM to health resilience). …”
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Edge-centric connectome-genetic markers of bridging factor to comorbidity between depression and anxiety by Zhiyi Chen, Yancheng Tang, Xuerong Liu, Wei Li, Yuanyuan Hu, Bowen Hu, Ting Xu, Rong Zhang, Lei Xia, Jing-Xuan Zhang, Zhibing Xiao, Ji Chen, Zhengzhi Feng, Yuan Zhou, Qinghua He, Jiang Qiu, Xu Lei, Hong Chen, Shaozheng Qin, Tingyong Feng

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A Case Study on Multi-Real-Option-Integrated STO-PF Models for Strengthening Capital Structures in Real Estate Development by Jung Kyu Park, Jun Bok Lee, Young Mee Ahn, Ga Young Yoo

“…Additionally, in-depth research is necessary to integrate emerging technologies, such as artificial intelligence and machine learning, into multi-real-option-based financial platforms. …”
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Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study by İbrahim Sarbay, Göksu Bozdereli Berikol, İbrahim Ulaş Özturan

“…OpenAI’s ChatGPT is a supervised and empowered machine learning-based chatbot. The aim of this study was to determine the performance of ChatGPT in emergency medicine (EM) triage prediction. …”
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Automated on-site broiler live weight estimation through YOLO-based segmentation by Mahmoud Y. Shams, Wael M. Elmessery, Awad Ali Tayoush Oraiath, Ahmed Elbeltagi, Ali Salem, Pankaj Kumar, Tamer M. El-Messery, Tarek Abd El-Hafeez, Mohamed F. Abdelshafie, Gomaa G. Abd El-Wahhab, Ibrahim S. El-Soaly, Abdallah Elshawadfy Elwakeel

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Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells by Tian Xu, Qin Xu, Ran Lu, David N. Oakland, Song Li, Liwu Li, Christopher M. Reilly, Xin M. Luo

“…They have increasingly gained recognition for their novel roles in the immune system, especially under autoimmune conditions. Conventional machine learning approaches such as principal component analysis have been employed in single-cell RNA sequencing (scRNA-seq) analysis to characterize DNT cells. …”
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Point-of-care lung ultrasonography for early identification of mild COVID-19: a prospective cohort of outpatients in a Swiss screening center by Olivier Hugli, Jean-Yves Meuwly, Noémie Boillat-Blanco, Pierre-Nicolas Carron, Siméon Schaad, Thomas Brahier, Mary-Anne Hartley, Jean-Baptiste Cordonnier, Luca Bosso, Tanguy Espejo, Olivier Pantet

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Social media discourse and internet search queries on cannabis as a medicine: A systematic scoping review. by Christine Mary Hallinan, Sedigheh Khademi Habibabadi, Mike Conway, Yvonne Ann Bonomo

“…It also demonstrates the need for the development of a systematic approach for evaluating the quality of social media studies and highlights the utility of automatic processing and computational methods (machine learning technologies) for large social media datasets. …”
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Screening of necroptosis-related genes and evaluating the prognostic capacity, clinical value, and the effect of their copy number variations in acute myeloid leukemia by Dake Wen, Ru Yan, Lin Zhang, Haoyang Zhang, Xuyang Chen, Jian Zhou

“…Methods Necroptosis-related differentially expressed genes (NRDEGs) were identified after intersecting differentially expressed genes (DEGs) from the Gene Expression Omnibus(GEO) database with NRGs from GeneCards, the Molecular Signatures Database (MSigDB) and literatures. Machine learning was applied to obtain hub-NRDEGs. …”
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The Impact of Dermal Characteristics on Low-Level Laser Power Measurement in Postmortem Zoological Species by Faith Ramsey, Michelle Borsdorf, John Ladner, Anne White, Tara M. Harrison

“…This could necessitate adjustment of machine settings for therapeutic effect in different species, though further studies would be warranted to determine the extent to which this would be necessary. …”
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TransRAUNet: A Deep Neural Network with Reverse Attention Module Using HU Windowing Augmentation for Robust Liver Vessel Segmentation in Full Resolution of CT Images by Kyoung Yoon Lim, Jae Eun Ko, Yoo Na Hwang, Sang Goo Lee, Sung Min Kim

“…<b>Method:</b> As a segmentation method, UNet is widely used as a baseline, and a multi-scale block or attention module has been introduced to extract context information. In recent machine learning efforts, not only has the global context extraction been improved by introducing Transformer, but a method to reinforce the edge area has been proposed. …”
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Tropical Cyclone Size Prediction and Development of An Error Correction Method by Guo Ruichen, Xu Jing, Wang Yuqing

“…Based on this relationship, a machine learning model, XGBoost, is used to develop an R17 size correction scheme that incorporate initial and forecast intensity, inner-core and outer-core sizes, and initial errors as predictors to estimate and correct model-predicted size errors. …”
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Your brain on art, nature, and meditation: a pilot neuroimaging study by Beatrix Krause-Sorio, Sergio Becerra, Prabha Siddarth, Stacey Simmons, Taylor Kuhn, Helen Lavretsky

“…The blocks included (1) nature videos, (2) AI-generated digital art (“machine hallucinations” by Refik Anadol), and (3) videos of NASA Webb-produced images of galactic nebulas. …”
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Identification and Verification of Novel Biomarkers Involving Rheumatoid Arthritis with Multimachine Learning Algorithms: An In Silicon and In Vivo Study by Fucun Liu, Juelan Ye, Shouli Wang, Yang Li, Yuhang Yang, Jianru Xiao, Aimin Jiang, Xuhua Lu, Yunli Zhu

“…Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. …”
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