Risk factors and prediction model of breast cancer-related lymphoedema in a Chinese cancer centre: a prospective cohort study protocol by Yue Wang, Xin Li, Ying Wang, Hongmei Zhao, Qian Lu, Yujie Zhou, Liyuan Zhang, Aomei Shen, Jingru Bian, Wanmin Qiang, Jingming Ye, Hongmeng Zhao, Yubei Huang, Zhongning Zhang, Peipei Wu

“…Traditional COX regression analysis and seven common survival analysis machine learning algorithms (COX, CARST, RSF, GBSM, XGBS, SSVM and SANN) will be employed for model construction and validation.Ethics and dissemination The study protocol was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-21124) and the Research Ethics Committee of Tianjin Medical University Cancer Institute and Hospital (bc2023013). …”
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MASFNet: Multi-level attention and spatial sampling fusion network for pine wilt disease trees detection by Dong Ren, Meng Li, Ziyu Hong, Li Liu, Jingfeng Huang, Hang Sun, Shun Ren, Pan Sao, Wenbin Wang, Jingcheng Zhang

“…However, due to the diversity of object information in UAV remote sensing images, most existing algorithms are prone to confusing the background environment and difficult to distinguish highly similar ground objects, resulting in a lot of false detections. …”
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Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model by Qingqing Lin, Qingqing Lin, Wenxiang Zhao, Wenxiang Zhao, Hailin Zhang, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Qinyun Ruan, Zhaoyang Qu, Zhaoyang Qu, Yimin Lin, Yimin Lin, Dajun Chai, Dajun Chai, Dajun Chai, Dajun Chai, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin, Xiaoyan Lin

“…For developing a predictive model for HF risk in AMI patients, the least absolute shrinkage and selection operator (LASSO) Regression was used to feature selection, and four ML algorithms including Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR) were employed to develop the model on the training set. …”
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Improved early detection of wheat stripe rust through integration pigments and pigment-related spectral indices quantified from UAV hyperspectral imagery by Anting Guo, Wenjiang Huang, Binxiang Qian, Kun Wang, Huanjun Liu, Kehui Ren

“…The early detection model for wheat stripe rust was developed using these parameters and machine learning algorithms. The results indicated selected pigments and SIs effectively distinguished stripe rust-infected wheat from healthy wheat at 7, 16, and 23 DPI. …”
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A 18F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma by Ping Yuan, Zhen-Hao Huang, Yun-Hai Yang, Fei-Chao Bao, Ke Sun, Fang-Fang Chao, Ting-Ting Liu, Jing-Jing Zhang, Jin-Ming Xu, Xiang-Nan Li, Feng Li, Tao Ma, Hao Li, Zi-Hao Li, Shan-Feng Zhang, Jian Hu, Yu Qi

“…For each sample, we extracted 428 PET/CT-based Radiomics features from the gross tumor volume (GTV) and cervical lymph node (CLN) delineated layer by layer and 256 PET/CT-based DL features from the maximum cross-section of GTV and CLN images We input these features into seven different machine learning algorithms and ultimately selected logistic regression (LR) as the model classifier. …”
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Artificial Intelligence in Cervical Cancer Screening: Opportunities and Challenges by Miriam Dellino, Marco Cerbone, Antonio d’Amati, Mario Bochicchio, Antonio Simone Laganà, Andrea Etrusco, Antonio Malvasi, Amerigo Vitagliano, Vincenzo Pinto, Ettore Cicinelli, Gerardo Cazzato, Eliano Cascardi

“…In this context, the advent of artificial intelligence and digital algorithms could represent new directions available to gynecologists and pathologists for the following: (i) the standardization of screening procedures, (ii) the identification of increasingly early lesions, and (iii) heightening the diagnostic accuracy of targeted biopsies and prognostic analysis of cervical cancer. …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…Among these, the Gradient Boosting model demonstrated superior predictive capability, overcoming both traditional regression methods and other machine learning algorithms as validated through the Taylor diagram and ranking system (i.e., r = 0.998, R² = 997, Root mean square error = 0.075, Variance Accounted For = 99.50%, Mean Absolute Percentage Error = 0.385%, Alpha 20 Index = 100, and performance index = 0.975). …”
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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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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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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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Seasonally flooded landscape connectivity and implications for fish in the Napo Moist Forest: A high-resolution mapping approach by Francisco Cuesta, Marco Calderón-Loor, Paulina Rosero, Marlon Calispa, Hedi Zisling, Yunierkis Pérez-Castillo, Gabriela Echevarría, Blanca Ríos-Touma

“…Using synthetic aperture radar data from Sentinel-1 combined with deep learning algorithms, we produced high-accuracy flood maps to assess landscape connectivity between rivers and floodplains. …”
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QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool by Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen, Linde Schoenmaker, Michiel Jespers, Olivier J. M. Béquignon, Marina Gorostiola González, Remco L. van den Broek, Andrius Bernatavicius, J. G. Coen van Hasselt, Piet. H. van der Graaf, Gerard J. P. van Westen

“…Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous. Finally, the last hurdle that researchers face is to ensure the reproducibility of their models and facilitate their transferability into practice. …”
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Knee4Life: Empowering Knee Recovery After Total Knee Replacement Through Digital Health Protocol by Maedeh Mansoubi, Phaedra Leveridge, Matthew Smith, Amelia Fox, Garry Massey, Sarah E. Lamb, David J. Keene, Paul Newell, Elizabeth Jacobs, Nicholas S. Kalson, Athia Haron, Helen Dawes

“…Using machine learning algorithms, computer vision can extract joint angles from video footage, offering a method to monitor knee range of motion in patients’ homes. …”
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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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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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Factors affecting the use of neurally adjusted ventilatory assist in the adult critical care unit: a clinician survey by Fiona Reid, Nicholas Hart, Louise Rose, John Smith, Clare Harris, Victoria Cornelius, Gerrard Francis Rafferty, Daniel Hadfield, Clare Finney, Bethany Penhaligon, Sian Saha, Harriet Noble, Philip Anthony Hopkins

“…In this context, high-quality training and usage algorithms are critically important to the design and of future trials, to clinician acceptance and to the clinical implementation and future success of NAVA.…”
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An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review by Luca Saba, Mahesh Maindarkar, Narendra N. Khanna, Anudeep Puvvula, Gavino Faa, Esma Isenovic, Amer Johri, Mostafa M. Fouda, Ekta Tiwari, Manudeep K. Kalra, Jasjit S. Suri

“…These results can promote several recommendations for developing unique, bias-free, and explainable AI algorithms for predicting ASCVD and stroke risks in patients with OSA.…”
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Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore by Chuen Seng Tan, Ian Yi Han Ang, Xin Quan Tan, Nabilah Rahman, Sheryl Hui Xian Ng, Srinath Sridharan, Sravan Ramachandran, Debby Dan Wang, Astrid Khoo, Sue-Anne Ee Shiow Toh

“…Subsequently we apply machine learning algorithms to predict which HUs will persist as PHUs, to inform future trials testing the effectiveness of interventions in reducing healthcare utilisation in PHUs.Design and setting This is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.Participants Patients who had at least one inpatient admission to the AMC between 2005 and 2013 were included in this study. …”
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Neuromorphic, physics-informed spiking neural network for molecular dynamics by Vuong Van Pham, Temoor Muther, Amirmasoud Kalantari Dahaghi

“…It also leverages the enhanced representation of real biological neural systems through spiking neural network integration with molecular dynamic physical principles, offering greater efficiency compared to conventional AI algorithms. NP-SNN integrates three core components: (1) embedding MD principles directly into the training, (2) employing best practices for training physics-informed ML systems, and (3) utilizing a highly advanced and efficient SNN architecture. …”
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A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning by Lina Pang, Qingxia Sun, Wenyue Wang, Mingjie Song, Ying Wu, Xin Shi, Xiaonan Shi

“…Prognostic signatures were developed using various machine learning algorithms based on marker genes linked to the identified cell subpopulations. …”
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