Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning by Yubo Wang, Chunyu Jiang, Yi qi Xing, Linxuan Zou, Mingzhi Song, Xueling Qu, Zhuqiang Jia, Lin Zhao, Xin Han, Junwei Zong, Shouyu Wang

“…Intergroup comparisons were conducted to analyze the differences between these two groups. Logistic regression analyses, 3 kinds of machine learning algorithms, a predictive model and nomogram was formulated to estimate the risk of DFU occurrence among diabetic patients with lower extremity ASO. …”
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Changing the Paradigm for Tractography Segmentation in Neurosurgery: Validation of a Streamline-Based Approach by Silvio Sarubbo, Laura Vavassori, Luca Zigiotto, Francesco Corsini, Luciano Annicchiarico, Umberto Rozzanigo, Paolo Avesani

“…A paired <i>t</i>-test was performed on the irregularity measurement to compare segmentations achieved with the two approaches. Qualitative differences were evaluated through visual inspection. …”
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Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease by Everest Castaneda, Everest Castaneda, Elissa Chesler, Erich Baker

“…By adding a connectivity-based method, network rankings of similarity and relationships are explored between classes of SUDs and CVD.MethodsGraph spectral clustering's utility is evaluated relative to commonly used network algorithms for discernment between two distinct co-occurring disorders and capacity to rank pathways based on their distinctiveness. …”
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Validation of Sea Surface Temperature From GK-2A Geostationary Satellite and Error Reduction Considering Impact of Satellite Zenith Angle by Kyung-Ae Park, Hye-Jin Woo, Stephane Saux Picart, Anne O'Carroll, Eunha Sohn, HuiTae Joo, Joon-Soo Lee, Joon-Yong Yang

“…This study evaluates the accuracy of sea surface temperature (SST) data produced by Korea's second geostationary satellite, GK-2A, over its first four years of operation (2019&#x2013;2023). …”
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Comparing Short-Term Outcomes of Ventral Hernia Repair Using Heavyweight Non-Woven Polypropylene Mesh With Heavyweight Knitted Polypropylene Mesh by Aldo Fafaj, Lucas R. A. Beffa, Clayton C. Petro, Ajita S. Prabhu, Benjamin T. Miller, Li-Ching Huang, Ryan C. Ellis, Sara M. Maskal, Nir Messer, Sergio Mazzola Poli de Figueiredo, Michael J. Rosen

“…A propensity score model and matching algorithms were implemented to address potential treatment-choice bias. …”
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AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters by Oana-Maria Butnaru, Monica Tatarciuc, Ionut Luchian, Teona Tudorici, Carina Balcos, Dana Gabriela Budala, Ana Sirghe, Dragos Ioan Virvescu, Danisia Haba

“…<i>Backgrounds and Objectives:</i> This study aimed to evaluate the reliability of AI-assisted dental–periodontal diagnoses compared to diagnoses made by senior specialists, specialists, and general dentists. …”
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Predicting nosocomial pneumonia of patients with acute brain injury in intensive care unit using machine-learning models by Junchen Pan, Zhen Yue, Jing Ji, Yongping You, Liqing Bi, Yun Liu, Xinglin Xiong, Genying Gu, Ming Chen, Shen Zhang

“…The training set revealed the superior and robust performance of the XGBoost with the highest AUC value (0.956), while the Random Forest and Adaptive Boost had the highest AUC value (0.883) in validation set.ConclusionMachine learning models can effectively predict the risk of nosocomial pneumonia infection in patients with acute brain injury in the ICU. Despite differences in populations and algorithms, the models we constructed demonstrated reliable predictive performance.…”
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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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Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration by Maoqiang Lin, Maoqiang Lin, Shaolong Li, Yabin Wang, Yabin Wang, Guan Zheng, Guan Zheng, Fukang Hu, Fukang Hu, Qiang Zhang, Qiang Zhang, Pengjie Song, Haiyu Zhou, Haiyu Zhou

“…Subsequently, four machine learning algorithms (SVM-RFE, Random Forest, XGB, and GLM) were used to select the method to construct the diagnostic model. …”
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SpaGAN: A spatially-aware generative adversarial network for building generalization in image maps by Zhiyong Zhou, Cheng Fu, Robert Weibel

“…It takes a representative cGAN, pix2pix, as the backbone, and modifies two modules: In the U-Net-based generator, an atrous spatial pyramid pooling (ASPP) module replaces the conventional convolutional module to extract multi-scale features of buildings of varying sizes and shapes; in the PatchGAN-based discriminator, a signed distance map (SDM) module is used to capture the fine-grained shape difference for discrimination. The proposed network was comprehensively evaluated with a synthetic and a real-world dataset. …”
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Capturing spatiotemporal variation in salt marsh belowground biomass, a key resilience metric, through geoinformatics by Kyle D. Runion, Deepak R. Mishra, Merryl Alber, Mark A. Lever, Jessica L. O'Connell

“…We used the BERM machine learning algorithms to evaluate how variables relating to biological, climatic, hydrologic, and physical attributes covaried with these BGB observations. …”
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Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

“…Several machine learning algorithms, including Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM), were evaluated for predictive accuracy. …”
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Infrared Small Target Detection Based on Double Gray-Values Descend Angle Contrast Measure by Song Guan, Dali Zhou, Xiaodong Wang, Pengji Zhou

“…Finally, by defining a double gray-values descend angle and calculating its tangent quotient, further enhancement of targets is achieved alongside suppression of additional background interference and salt noise. Experimental evaluations were conducted on four publicly available datasets, comparing our proposed approach with seven existing algorithms. …”
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P-68 LIVGUARD, A DEEP NEURAL NETWORK FOR CIRRHOSIS DETECTION IN LIVER ULTRASOUND (USD) IMAGES by DIEGO ARUFE, Pablo Gomez del Campo, Ezequiel Demirdjian, Carlos Galmarini

“…Conflict of interest: No Introduction and Objectives: Differents ultrasound (USD) signs have been described for the diagnosis of cirrhosis. …”
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Screening and validation of diagnostic markers for keloids via bioinformatics analysis by Ze Wang, Bo Hu, Wenfei Li, Tengxiao Ma, Lei Li

“…The aim of this study was to screen diagnostic markers of KD via bioinformatics methods and evaluate their clinical application value. Methods: The GSE44270, GSE145725, GSE7890 and GSE83286 datasets were analyzed in combination with difference analysis and weighted gene coexpression network analysis (WGCNA) and machine learning algorithms, candidate genes related to KD were screened and verified via receiver operating characteristic (ROC) curves and external datasets. …”
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19 Predicting daily PM2.5 in Mexico City: A hybrid spatiotemporal modeling approach by Mike He, Ellen Ren, Iván Gutiérrez-Avila, Itai Kloog

“…We employed machine-learning-based approaches (random forest and gradient boosting algorithms) to downscale satellite measurements and incorporate local sources, then utilized a generalized additive model (GAM) to geographically weight predictions from the initial models. …”
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Comparison of Cuff Leak Test, Laryngeal Ultrasonography, and Videolaryngoscopy for the prediction of post-extubation stridor by Rubina Khullar Mahajan, Apoorva Gupta, Parshotam Lal Gautam, Gunchan Paul, Vikalp Khatri

“…Cuff leak volume (CLV), leak volume fraction ratio (LVFR), and airway column width difference (ACWD) were noted. The grade of peri-laryngeal edema was noted with VL. …”
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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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Modern fixation techniques versus traditional tension band wiring for olecranon fractures: a systematic review and meta-analysis of functional outcomes, healing time, and complicat... by Chengjing Wang, Changqing Li

“…Larger standardized trials are needed to confirm these preliminary conclusions and refine evidence-based treatment algorithms.…”
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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…This study aimed to develop and validate a predictive model for postoperative pneumonia in surgical patients using nine machine learning methods.ObjectiveOur study aims to develop and validate a predictive model for POP in surgical patients using nine machine learning algorithms. By evaluating the performance differences among these machine learning models, this study aims to assist clinicians in early prediction and diagnosis of POP, providing optimal interventions and treatments.MethodsRetrospective data from electronic medical records was collected for 264 patients diagnosed with postoperative pneumonia and 264 healthy control surgical patients. …”
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