Artificial Intelligence–Enabled ECG Screening for LVSD in LBBB by Hak Seung Lee, MD, Sooyeon Lee, MD, Sora Kang, MS, Ga In Han, MS, Ah-Hyun Yoo, MS, Jong-Hwan Jang, PhD, Yong-Yeon Jo, PhD, Jeong Min Son, MD, Min Sung Lee, MD, MS, Joon-myoung Kwon, MD, MS, Kyung-Hee Kim, MD, PhD

“…Conclusions: Our findings indicate that a broad AI-ECG model reliably detects LVSD in LBBB patients, and transfer learning offers modest improvements without requiring curated LBBB data sets. Evaluating algorithms in representative clinical populations is essential.…”
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A Cross-Stage Focused Small Object Detection Network for Unmanned Aerial Vehicle Assisted Maritime Applications by Gege Ding, Jiayue Liu, Dongsheng Li, Xiaming Fu, Yucheng Zhou, Mingrui Zhang, Wantong Li, Yanjuan Wang, Chunxu Li, Xiongfei Geng

“…The CFSD-UAVNet model was evaluated on the publicly available SeaDronesSee maritime dataset and compared with other cutting-edge algorithms. The experimental results showed that the CFSD-UAVNet model achieved an mAP@50 of 80.1% with only 1.7 M parameters and a computational cost of 10.2 G, marking a 12.1% improvement over YOLOv8 and a 4.6% increase compared to DETR. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…These enhanced features are then used as inputs for advanced machine-learning algorithms. Unlike traditional models, this feature extraction enhances classification accuracy and robustness. …”
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Predicting 30-Day Venous Thromboembolism Following Total Joint Arthroplasty: Adjusting for Trends in Annual Length of Stay by Johnathan R. Lex, MBChB, MASc, Robert Koucheki, MD, MEng, Aazad Abbas, MD, Jesse I. Wolfstadt, MD, MSc, FRCSC, FAAOS, Alexander S. McLawhorn, MD, MBA, Bheeshma Ravi, MD, PhD, FRCSC

“…Predictive models (logistic regression, random forest, and XGBoost) were trained and tested based on year of surgery with different oversampling algorithms used to address data imbalance. Results: A total of 498,314 patients were included, with 0.88% developing a VTE within 30 days. …”
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Performance of ChatGPT-3.5 and ChatGPT-4 in the field of specialist medical knowledge on National Specialization Exam in neurosurgery by Maciej Laskowski, Marcin Ciekalski, Marcin Laskowski, Bartłomiej Błaszczyk, Marcin Setlak, Piotr Paździora, Adam Rudnik

“…Conclusions: ChatGPT-4 shows improved accuracy over ChatGPT-3.5, likely due to advanced algorithms and a broader training dataset, highlighting its better grasp of complex neurosurgical concepts.…”
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A visualized bibliometric analysis on remote sensing monitoring of methane emissions in coal mines by CUI Ximin, GUO Wei, SHI Guobin, LI Qingqing, YAO Hansi, ZHANG Yuhuan, ZHAO Yuling, RIENOW Andreas

“…This study therefore suggests areas for future research, such as the precision and efficiency of remote sensing monitoring, intelligent data processing and algorithms, multi-platform integration and fusion, and international collaboration and data sharing.…”
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Laser-Induced Breakdown Spectroscopy Quantitative Analysis Using a Bayesian Optimization-Based Tunable Softplus Backpropagation Neural Network by Xuesen Xu, Shijia Luo, Xuchen Zhang, Weiming Xu, Rong Shu, Jianyu Wang, Xiangfeng Liu, Ping Li, Changheng Li, Luning Li

“…Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. …”
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Accuracy of warm ischemia time measurement using a surgical intelligence software in partial nephrectomies: A validation study by Archan Khandekar, Joao G. Porto, Jean C. Daher, Pedro F. S. Freitas, Dotan Asselman, Maritza M. Suarez, Mark L. Gonzalgo, Dipen J. Parekh, Sanoj Punnen

“…The platform utilized computer vision algorithms to detect clamp placement and removal, enabling precise WIT measurement. …”
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Associations of accelerated biological ageing with incident dementia, cognitive functions and brain structure: a prospective cohort study based on UK Biobank by Xiao Ren, Xiaowei Zheng, Yonghong Zhang, Zhengbao Zhu, Mengyao Shi, Pinni Yang, Lulu Sun, Ruirui Wang, Yiqun Li, Wenyang Han, Minglan Jiang, Yiming Jia

“…We measured biological age from clinical traits using the Klemera-Doubal method Biological Age (KDM-BA) and PhenoAge algorithms. Cox models were applied to evaluate the risk of dementia. …”
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Improving T2D machine learning-based prediction accuracy with SNPs and younger age by Cynthia AL Hageh, Andreas Henschel, Hao Zhou, Jorge Zubelli, Moni Nader, Stephanie Chacar, Nantia Iakovidou, Haralampos Hatzikirou, Antoine Abchee, Siobhán O’Sullivan, Pierre A. Zalloua

“…Results: The inclusion of genomic data modestly improved model performance across all algorithms in the discovery dataset. Clinical features such as family history of T2D and hypertension consistently ranked as top features. …”
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Prehospital critical care beyond advanced life support for out-of-hospital cardiac arrest: A systematic review by Adam J. Boulton, Rachel Edwards, Andrew Gadie, Daniel Clayton, Caroline Leech, Michael A. Smyth, Terry Brown, Joyce Yeung

“…Prehospital critical care was defined as any provider with enhanced clinical competencies beyond standard advanced life support algorithms and dedicated dispatch to critically ill patients. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…The results show that: first, spatial structural parameters C and U have a certain contribution to the forest growth, and C and U can explain 21.5&#x0025;, 15.2&#x0025;, and 9.3&#x0025; of the variance in DBH, H, and CW growth models, respectively; second, CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance; third, based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018 to 2022 and found that DBH and H&#x0027;s fitting performance in measured and predicted data was highly consistent with <italic>R</italic>² and root-mean-square error (RMSE) of 86.8&#x0025;, 2.06 cm in DBH and 79.2&#x0025;, 1.11 m in H, but CW&#x0027;s <italic>R</italic>² and RMSE of 72.2&#x0025;, 0.65 m caused crowding (C) inconsistency.…”
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Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated function enabl... by Rachid Tahzima, Rachid Tahzima, Rachid Tahzima, Rachid Tahzima, Justine Charon, Adrian Diaz, Kris De Jonghe, Sebastien Massart, Thierry Michon, Wim Vranken, Wim Vranken, Wim Vranken, Wim Vranken

“…In this review, we highlight how biophysics-inspired prediction tools combined with advanced deep learning algorithms, such as AlphaFold2 (AF2), can help efficiently infer the conformational heterogeneity and dynamics of RdRps. …”
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Harmonizing ground and UAV hyperspectral data: A novel spectral correction method for maximizing estimation models and datasets of ground hyperspectral by Zhonglin Wang, Pengxin Deng, Kairui Chen, Ying Xiong, Feng Yang, Cheng Wang, Zhixin Li, Biao Li, Yongjian Sun, Zongkui Chen, Zhiyuan Yang, Jun Ma

“…Estimation models of canopy nitrogen content (CNC) were developed using machine learning algorithms with non-imaging hyperspectral, hyperspectral images, and corrected hyperspectral datasets. …”
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An International Longitudinal Natural History Study of Patients With Danon Disease: Unique Cardiac Trajectories Identified Based on Sex and Heart Failure Outcomes by Kimberly N. Hong, Emily Eshraghian, Tarek Khedro, Alessia Argirò, Jennifer Attias, Garrett Storm, Melina Tsotras, Tanner Bloks, Isaiah Jackson, Elijah Ahmad, Sharon Graw, Luisa Mestroni, Quan M. Bui, Jonathan Schwartz, Stuart Turner, Eric D. Adler, Matthew Taylor

“…Correlations between structural cardiac dysfunction and disease progression may permit risk stratification, refinement of treatment algorithms, and inform therapeutic trial design. Registration URL: https://clinicaltrials.gov/; Unique Identifier: NCT03766386.…”
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Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models: A GNSS perspective by Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai, Yuyan Wang, Xiaolong Mi, Tong Liu, Duojie Weng

“…This temporal and spatial inhomogeneity of accuracy and accuracy degradation are related to AI algorithms and attributes of training data, etc., but these characteristics have not been thoroughly explored and analyzed. …”
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Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning by Xianchang Zeng, Lingyun Wei, Lu Lv, Di Wu, Yingying Shen, Xinliang Lu, Xianghui Kong, Zhijian Cai, Jianli Wang, Jianli Wang

“…Single-cell RNA sequencing was further employed to evaluate the heterogeneity of immune cells. Machine learning algorithms were utilized to construct and identify diagnostic markers for LUAD, which were then validated by receiver operating characteristic (ROC) curve analysis. …”
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Enhancing forest biodiversity indicators in inventories through harmonized protocols by Moreno-Fernández D, Breidenbach J, Cañellas I, Chirici G, D’Amico G, Ferretti M, Giannetti F, Puliti S, Schnell S, Shackleton R, Skudnik M, Alberdi I

“…Once the bird and mammal data have been collected, advanced algorithms could facilitate and enhance the efficiency of the analyses. …”
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Integrative analysis identifies IL-6/JUN/MMP-9 pathway destroyed blood-brain-barrier in autism mice via machine learning and bioinformatic analysis by Cong Hu, Heli Li, Jinru Cui, Yunjie Li, Feiyan Zhang, Hao Li, Xiaoping Luo, Yan Hao

“…Through integrative analysis combining differential gene expression profiling with three machine learning algorithms - Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and RandomForest combined with eXtreme Gradient Boosting (XGBoost) - we identified four hub genes, with JUN emerging as a core regulator. …”
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Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics by Xuni Xu, Xue Jiang, Haoran Jiang, Xiaoye Yuan, Mengjing Zhao, Yuqi Wang, Gang Chen, Gang Li, Yuxia Duan

“…After performing univariate cox regression and the least absolute shrinkage and selection operator (LASSO) algorithms to extract radiological features, the Rad-score was calculated through a Cox proportional hazards regression model and a random survival forest (RSF) model. …”
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