Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images by Jiameng Lu, Xinyi Liu, Xiaoqing Ji, Yunxiu Jiang, Anli Zuo, Zihan Guo, Shuran Yang, Haiying Peng, Fei Sun, Degan Lu

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Machine learning and deep learning in medicine and neuroimaging by Iván Sánchez Fernández, Jurriaan M. Peters

“…Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without being explicitly programmed. Deep learning algorithms learn by processing the data with increasing levels of abstraction in each layer. …”
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Integrating Gut Microbiome and Metabolomics with Magnetic Resonance Enterography to Advance Bowel Damage Prediction in Crohn’s Disease by Huang L, Meng J, Lin S, Peng Z, Zhang R, Shen X, Zheng W, Zheng Q, Wu L, Wang X, Wang Y, Mao R, Sun C, Li X, Feng ST

“…The optimal Xtreme Gradient Boosting Classifier (XGBC) model integrating three microbial genera, six fecal metabolites, three serum metabolites, and three MRE features achieved superior performance (AUC 0.857 and 0.829 in the derivation and external validation cohorts, respectively). …”
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Perceptions of, Barriers to, and Facilitators of the Use of AI in Primary Care: Systematic Review of Qualitative Studies by Héctor Martínez-Martínez, Julia Martínez-Alfonso, Belén Sánchez-Rojo-Huertas, Valeria Reynolds-Cortez, Andrea Turégano-Chumillas, Victoria A Meseguer-Ruiz, Shkelzen Cekrezi, Vicente Martínez-Vizcaíno

“…We identified four analytical themes using thematic synthesis: (1) change in the physician-patient relationship, highlighting concerns about loss of empathy, connection, and trust; (2) AI as a partner for efficient time and information management, including its potential to improve workflow and decision-making, alongside skepticism about increased workload; (3) data as the cornerstone of AI development, reflecting concerns about data privacy, quality, bias, and corporate responsibility; and (4) barriers to and facilitators of AI in primary care, emphasizing equity, accessibility, and stakeholder co-design. …”
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Association between hemoglobin glycation index and the risk of cardiovascular disease in early-stage cardiovascular-kidney-metabolic syndrome: evidence from the China health and re... by Huiyi Liu, Shuai Mao, Shuai Mao, Yunzhang Zhao, Yunzhang Zhao, Lisha Dong, Lisha Dong, Yifan Wang, Yifan Wang, Chao Lv, Tong Yin, Tong Yin

“…Using k-means clustering analysis, the participants were classified according to the values of HGI measured at baseline and 3 years later, respectively. The primary outcome was self-reported CVD during the follow-up of at least 3 years. …”
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A Review of Integrated Optimization Method of Batch Planning and Scheduling for Steelmaking-Continuous Casting-Hot Rolling Production Under Uncertain Environment Based on Lagrangia... by Liangliang Sun, Xirang Hao, Yunpeng Li, Jinghan Xue, Natalia M. Matsveichuk, Yuri N. Sotskov, Qichun Zhang

“…Considering the uncertain environment and the integration of SM-CC-HR, this paper summarizes the research status of previous scholars from three aspects: mathematical modeling, model optimization, and algorithm optimization based on the Lagrangian framework. …”
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Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

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A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer by Tingting Ma, Tingting Ma, Tingting Ma, Tingting Ma, Tingting Ma, Mengran Zhao, Mengran Zhao, Mengran Zhao, Mengran Zhao, Mengran Zhao, Xiangli Li, Xiangchao Song, Xiangchao Song, Xiangchao Song, Xiangchao Song, Lingwei Wang, Lingwei Wang, Lingwei Wang, Lingwei Wang, Zhaoxiang Ye, Zhaoxiang Ye, Zhaoxiang Ye, Zhaoxiang Ye

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Deep learning-based prediction of individualized Real-time FSH doses in GnRH agonist long protocols by Na Kong, Yu Xia, Zhilong Wang, Hui Zhang, Liyan Duan, Yingchun Zhu, Chenyang Huang, Guijun Yan, Jie Mei, Wujun Li, Haixiang Sun

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Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension by Zunfeng Fu, Jing Wang, Wenyi Shen, Yanqing Wu, Jiajun Zhang, Yan Liu, Chongqiang Wang, Yanlin Shen, Ye Zhu, Weifu Zhang, Chunju Lv, Lin Peng

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A road generalization method using graph convolutional network based on mesh-line structure unit by Tianyuan Xiao, Tinghua Ai, Dirk Burghardt, Pengcheng Liu, Min Yang, Aji Gao, Bo Kong, Huafei Yu

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Admission testing for higher education: A multi-cohort study on the validity of high-fidelity curriculum-sampling tests. by A Susan M Niessen, Rob R Meijer, Jorge N Tendeiro

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Applications of Artificial Intelligence in the Study and Diagnosis of Orbital Pathologies: State of the Art and Future Directions by Luca Michelutti, Alessandro Tel, Massimo Robiony, Edoardo Agosti, Tamara Ius, Giuseppe Catapano, Caterina Gagliano, Marco Zeppieri

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Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models by Ibrahim Abdulwahab, Sulaiman Haruna Sulaiman, Umar Musa, Ibrahim Abdullahi Shehu, Abdullahi Kakumi Musa, Ismaila Mahmud, Mohammed Musa, Abdullahi Abubakar, Abdulrahman Olaniyan

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Maternal plasma cell-free RNA as a predictive test for fetal lung maturation by Sean W. D. Carter, Kay Yi Michelle Seah, Si En Poh, Winston Koh, Haruo Usuda, Erin L. Johnson, Yusaku Kumagai, Tsukasa Takahashi, Lara J. Monteiro, Reyna Peñailillo, Gino Nardocci, Hannah R. S. Watson, Masatoshi Saito, Mahesh A. Choolani, Sebastián E. Illanes, Matthew W. Kemp

“…Delivery and ventilation data were analyzed with ANOVA, Tukey HSD, and Dunnett T3 tests. A Random Forest algorithm identified genes that separated mature from immature fetal lung subgroups and determined AUC values for maternal and fetal cell-free RNA (cfRNA) feature sets to predict fetal lung maturation. …”
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Global high-resolution fire-sourced PM_2.5 concentrations for 2000–2023 by Y. Hu, C. Tian, X. Yue, Y. Lei, Y. Cao, R. Xu, Y. Guo

“…Globally, the average fire-sourced [PM<span class="inline-formula">_2.5</span>] is estimated to be 2.04 <span class="inline-formula">µg m⁻³</span> with GFED4.1s and 3.96 <span class="inline-formula">µg m⁻³</span> with QFED2.5. …”
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Prediksi Stok dan Pengaturan Tata Letak Barang Menggunakan Kombinasi Algoritma Triple Exponential Smoothing dan FP-Growth by Kristoko Dwi Hartomo, Sri Yulianto Prasetyo, Rahmat Abadi Suharjo

“…Data processing in this research is called data mining using FP-Growth and Triple Exponential Smoothing algorithms. FP-Growth algorithm to find out user behavior patterns can be used to develop Triple Exponential Smoothing decisions and algorithms which are forecasting algorithms for inventory items. …”
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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors by Elham Raiesi Nafchi, Pedram Fadavi, Sepideh Amiri, Susan Cheraghi, Maryam Garousi, Mansoureh Nabavi, Iman Daneshi, Marzieh Gomar, Malihe Molaie, Ali Nouraeinejad

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Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children by Jintuo Zhou, Yongjin Xie, Ying Liu, Peiguang Niu, Huajiao Chen, Xiaoping Zeng, Jinhua Zhang

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AN EFFECTIVE APPROACH TO FACE RECOGNITION WITH ARTIFICIAL INTELLIGENCE AND THE INTERNET OF THINGS USING NVIDIA JETSON NANO by Ngoc Giau Pham, Nhien Loc Bui, Quang Hien Tran, Le Thanh Hai Tong, Hong Ngoc Tran

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