Protocol and research program of the European registry and biobank for interstitial lung diseases (eurILDreg) by Ekaterina Krauss, Silke Tello, Jennifer Naumann, Sandra Wobisch, Clemens Ruppert, Stefan Kuhn, Poornima Mahavadi, Raphael W. Majeed, Philippe Bonniaud, Maria Molina-Molina, Athol Wells, Nik Hirani, Carlo Vancheri, Simon Walsh, Matthias Griese, Bruno Crestani, Andreas Guenther, on behalf of further eurILDreg investigators, RARE-ILD investigators

“…Drawn against this background, experts across pediatric and adult ILD fields have joined forces in the RARE-ILD initiative to pioneer novel non-invasive diagnostic algorithms and biomarkers. Collaborating with the RARE-ILD consortium, the eurILDreg aims to comprehensively describe different ILDs, analyze genetically defined forms across age groups, create innovative diagnostic and therapeutic biomarkers, and employ artificial intelligence for data analysis. …”
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Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring by Qing-bo ZHOU, Qiang-yi YU, Jia LIU, Wen-bin WU, Hua-jun TANG

“…However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes. …”
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Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers by Mariah Batool, Oluwafemi Sanumi, Jasna Jankovic

“…This review begins by explaining the fundamental concepts of artificial intelligence and introducing commonly used artificial intelligence-based algorithms in a simplified and clearly comprehensible way, establishing a foundational knowledge base for further discussion. …”
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Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding by Jiayi Su, Xiangfeng Kong, Wenliang Wang, Qian Xie, Chengming Wang, Bie Tan, Jing Wang

“…Eleven statistical and machine learning (ML) regression algorithms were employed, incorporating stratified sampling and the recursive feature elimination method for feature selection. …”
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Differing Effects of Alcohol Use on Epigenetic and Brain Age in Adult Children of Parents with Alcohol Use Disorder by Jamie L. Scholl, Kami Pearson, Kelene A. Fercho, Austin J. Van Asselt, Noah A. Kallsen, Erik. A. Ehli, Kari N. Potter, Kathleen A. Brown-Rice, Gina L. Forster, Lee A. Baugh

“…We examined structural brain differences and applied machine learning algorithms to predict biological brain and DNA methylation ages to investigate differences and determine any accelerated aging between these groups. …”
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Characterization of immune microenvironment associated with medulloblastoma metastasis based on explainable machine learning by Fengmao Zhao, Xiangjun Liu, Jingang Gui, Hailang Sun, Nan Zhang, Yun Peng, Ming Ge, Wei Wang

“…Methods ML models were constructed and validated to predict prognosis and metastasis in MB patients. Eight algorithms were evaluated, and the optimal model was selected. …”
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The Evolution of Ophthalmological Healthcare System in Premature Children by A. V. Tereshhenko, I. G. Trifanenkova, M. S. Tereshhenkova, Yu. A. Yudina, S. V. Isaev, P. L. Volodin, N. N. Yudina, A. A. Vydrina, Yu. A. Sidorova, E. V. Erohina, V. V. Shaulov

“…The effective management and treatment algorithms for the patients with active ROP had not been implemented yet. …”
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Kun–Qi birth cohort (KQBC) study for the incidence, aetiology and risk factors of chronic kidney disease in Chinese children: a cohort profile by Yan Ma, Xingdong Chen, Jie Wei, Li Guo, Jian Huang, Li Xie, Lei Chen, Ping Lu, Wei Sun, Wei Shi, Feng Ding, Liping Zhang, Yue Zhang, Linlin Zhang, Rui Song, Yi Huang, Jun Shen, Hong Xu, Zhigang Chen, Qian Shen, Ying Ma, Yiwen Zhang, Hongxia Li, Yihui Zhai, Biao Xu, Xian Zhou, Jun Sun, Bing Wu, Rui Su, Xiaoyan Yu, Jianhong Zhu, Yihong Huang, Ling Gong, Nana Wu, Jingjing Sun, Tianyi Liu, He Yuan, Yiwen Shen, Jiaojiao Liu, Xia Han, Juanjuan Wu, Yinghua Sun, Suxiang Xu, Qinyi Zhang, Na Xie, Jianfeng Ji, Weizhi Mo, Yuanxin Liu, Xinghua Shi, Wanshan Qiu, Jiapeng Mi, Juhua Yan, Minghui Zhu, Pei Feng, Wenqian Sheng, Mingqin Tai, Chunqin Zhou, Qian Jin, Chengyu Gu, Xiujuan Huang, Qinfang Qian, Yixin Cao, Haochen Shen, Ziwen Xue, Yucui Ma, Juan Tian, Yingdan Zhang, Weiyuan Zhuang, Kaiqun Yang, Xiaohui Shen, Yanwei Gu, Beibei Shi, Chenhong Pan, Hongmei Zhou, Xiuchen Zhang, Meihua Tang, Kaixuan Gu, Hongping Liu, Yuanxian Li

“…We aim to develop multimodal risk prediction models by integrating genomics and artificial intelligence algorithms. The KQBC also enables prospective cohort studies addressing extensive research questions.Trial registration number NCT06018831.…”
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Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning by Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan

“…These methods aimed to minimise performance discrepancies across ethnic groups while ensuring robust model performance.ResultsThe RF model outperformed other state-of-the-art models, achieving an area under the curve of 0.759 for males and 0.756 for females, a false negative rate of 0.224 for males and 0.229 for females, and a balanced accuracy of 0.690 for males and 0.689 for females. Bias mitigation algorithms reduced disparities in prediction performance across ethnic groups, with the threshold optimiser yielding the most notable improvements. …”
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Automatic recognition and differentiation of pulmonary contusion and bacterial pneumonia based on deep learning and radiomics by Tie Deng, Junbang Feng, Xingyan Le, Yuwei Xia, Feng Shi, Fei Yu, Yiqiang Zhan, Xinghua Liu, Chuanming Li

“…Four machine learning algorithms including Decision Trees, Logistic Regression, Random Forests and Support Vector Machines(SVM) were using to built the models. …”
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Establishment of a prognostic model based on ER stress-related cell death genes and proposing a novel combination therapy in acute myeloid leukemia by Minghui Wang, Huajian Xian, Xiaoli Xia, Wenjie Zhang, Zixuan Huang, Chaoqun Lu, Yuling Zheng, Yixin Wang, Shufeng Xie, Renyao Pan, YaoYifu Yu, Ruiheng Wang, Huijian Zheng, Guorui Huang, Han Liu

“…Methods We analyzed RNA sequencing and clinical data from 887 AML patients across 4 cohorts to develop an ER stress-related cell death index (ERCDI) using 10 machine-learning algorithms with 117 unique combinations. Survival and time-dependent Receiver Operating Characteristic Curve (ROC) analyses were performed to assess the model’s efficacy. …”
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Association between pro-inflammatory diet and fecal incontinence: a large population-based study by Haiyang Wang, Haiyang Wang, Zihan Liu, Zihan Liu, Xingfu Lu, Enyu Luan, Enyu Luan, Tingting Dong, Tingting Dong, Can Li, Can Li, Yanni Lv, Erkang Wu, Tao Shen, Tao Shen

“…Meanwhile, we identified key dietary factors for FI using multiple machine learning algorithms. Finally, we assessed the mediation role of inflammatory indicators on the association of key dietary factors with FI through mediation analysis.ResultsAfter adjustment for potential confounding variables, our results showed the highest tertile exhibited dramatically increasing prevalence of FI compared to the lower tertile (OR 1.27, 95% CI 1.06–1.53), suggesting a positive association between DII and FI. …”
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Multiclass Fault Diagnosis in Power Transformers Using Dissolved Gas Analysis and Grid Search-Optimized Machine Learning by Andrew Adewunmi Adekunle, Issouf Fofana, Patrick Picher, Esperanza Mariela Rodriguez-Celis, Oscar Henry Arroyo-Fernandez, Hugo Simard, Marc-André Lavoie

“…To address these limitations, this study proposes a unified multiclass classification model that integrates traditional gas ratio features with supervised machine learning algorithms to enhance fault diagnosis accuracy. The performance of six machine learning classifiers was systematically evaluated using training and testing data generated through four widely recognized gas ratio schemes. …”
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Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images by Saeideh Maleki, Nicolas Baghdadi, Hassan Bazzi, Cassio Fraga Dantas, Dino Ienco, Yasser Nasrallah, Sami Najem

“…Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. …”
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Integrated machine learning and single-cell RNA sequencing reveal COL4A2 and CXCL6 as oxidative stress-associated biomarkers in periodontitis by Siyu Sun, Siyu Sun, Jing Ren, Jing Ren, Xiujuan Zeng, Yanbin Chen, Qianbing Zhou, Junying Yang, Shan Chen

“…Key genes linked to oxidative stress in periodontitis were identified through a comprehensive analytical approach, including differential expression analysis, weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), and functional enrichment analyses (GO and KEGG). Machine learning algorithms were subsequently employed to refine the selection of key genes. …”
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Identification of SMYD2 as a candidate diagnostic and prognostic biomarker for gastric cancer by Sichao Wang, Chuanxi Zhao, Dongmei Li, Qingzhi Liu, Cuiping Mao, Shanshan Ding, Shujun Zhang, Wenjing Shang

“…The association between SMYD2 and the tumor immune microenvironment in GC was evaluated using CIBERSORT, ESTIMATE, and TIDE algorithms. Functional characterization of SMYD2 was performed via SMYD2 knockdown in GC cells.ResultsMost HMEs were up-regulated in GC tissues and exhibited relatively high mutation frequencies. …”
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Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability by Emily Diaz Badilla, Ignasi Cos, Claudio Sampieri, Berta Alegre, Isabel Vilaseca, Simone Balocco, Petia Radeva

“…Materials and Methods:: We employed the RADCURE dataset clinical information to train six Machine Learning algorithms, evaluating them via cross-validation for grid search hyper-parameter tuning and feature selection as well as a final performance measurement on a 20% sample test set. …”
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Exploring the relationship between dietary patterns and health-related quality of life among Iranian adult population: Tehran lipid and glucose study by Mahdieh Niknam, Somayeh Hosseinpour-Niazi, Sara Jalali-Farahani, Parisa Amiri, Parvin Mirmiran, Firoozeh Hosseini-Esfahani, Elaheh Ainy, Fereidoun Azizi

“…The physical component summary (PCS) and mental component summary (MCS) scores of HRQoL were calculated using the appropriate scoring algorithms. Dietary patterns were identified using factor analysis. …”
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Artificial Intelligence in Pediatric Orthopedics: A Comprehensive Review by Andrea Vescio, Gianluca Testa, Marco Sapienza, Filippo Familiari, Michele Mercurio, Giorgio Gasparini, Sergio de Salvatore, Fabrizio Donati, Federico Canavese, Vito Pavone

“…For developmental dysplasia of the hip, deep learning algorithms demonstrated high diagnostic performance in radiographic interpretation. …”
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Repeatability and agreement of three biometers measuring corneal keratometry and astigmatism in eyes with cataract and high myopia by Haisheng Zhou, Tong Bao, Shuchan Li, Yunlei Pang, Qinglei Shao, Lifeng Wang, Lihua Yin, Jinli Wang, Na Hu, Yuanyuan Qi, Bozhou Zhang

“…Further research is needed to refine measurement algorithms and optimize clinical accuracy.…”
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