The importance of biochemical and ultrasound markers in prenatal diagnosis: current methods and development perspectives by Adam Torbicki, Piotr Gaworek, Michał Korpalski, Krzysztof Pawlikowski, Marek Żygłowicz, Dominik Augustyn, Maria Pawluczyk, Mateusz Marciniak, Alicja Trybuła

“…Continuous refinement of diagnostic algorithms and broader access to non-invasive testing are key directions for future development, aiming to improve both prenatal care quality and patient safety. …”
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Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics by Gianni S.S. Liveraro, Maria E.S. Takahashi, Fabiana Lascala, Luiz R. Lopes, Nelson A. Andreollo, Maria C.S. Mendes, Jun Takahashi, José B.C. Carvalheira

“…Body composition radiomics were integrated with clinicopathological factors using machine learning (ML) algorithms trained for patient outcome prediction. We compared results using Random Forest, Logistic Regression and Boosted Decision Tree algorithms. …”
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Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models. …”
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Improving synergistic drug combination prediction with signature-based gene expression features in oncology by Mozhgan Mozaffarilegha, Sajjad Gharaghani

“…Despite these advancements, most algorithms primarily rely on drug-specific features, such as chemical structures, with limited incorporation of functional drug information and cellular content features.Methods:We propose a novel approach that integrates Drug Resistance Signatures (DRS) as a biologically informed representation of drug information. …”
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Improved Key Microbial Biomarker Discovery Using Ensemble Statistical Methods by Walter Pirovano, Yashjit Gangopadhyay, Mirna Lilian Baak, Christiaan Arie de Leeuw, Radhika Bongoni, Eline Suzanne Klaassens

“…The framework integrates machine learning (ML) algorithms and statistical methods to determine the most relevant microbial biomarkers and signatures that explain variation in the microbial abundance counts and metadata classes based on predefined metrics. …”
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A systematic literature review on the role of artificial intelligence in citizen science by Germain Abdul-Rahman, Andrej Zwitter, Noman Haleem

“…However, challenges such as data quality variability, algorithmic opacity, and scalability constraints persist. …”
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Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques by Abbas Jafar, Myungho Lee

“…However, the performance of previous automated approaches has often been hindered by suboptimal feature selection and algorithms’ “black-box” nature, which adversely affect their interpretability and clinical applicability. …”
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Clinical case of secondary osteoporosis in patient with ulcerative colitis by L. V. Zhuravlyova, Yu. K. Sikalo, M. O. Oliinyk

“…The described clinical case demonstrates a need for further population-based prospective studies to create diagnostic and therapeutic algorithms. These algorithms could help provide personalized therapy to patients with gastrointestinal diseases in group of osteoporosis and fractures risk at a young age.…”
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Performance Comparison of Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) in Verifying Material Orientation by Eldio Utama, Eko Rudiawan Jamzuri

“…The images were processed using the Inception V3 Convolutional Neural Network (CNN) to extract relevant image features, which were then classified using SVM and kNN algorithms. …”
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Learning Analytics: A Data Mining and Machine Learning Perspective by Salam Ullah Khan, Kifayat Ullah, Mahvash Arsalan Lodhi, Sadaqat Ali Khan Bangash

“… Tremendous proliferation in data generation in the past few years has paved the way for new research and the development of new and improved techniques and algorithms in different fields of science and education. …”
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Generative AI for drug discovery and protein design: the next frontier in AI-driven molecular science by Uddalak Das

“…Generative artificial intelligence (AI) has emerged as a disruptive paradigm in molecular science, enabling algorithmic navigation and construction of chemical and proteomic spaces through data-driven modeling. …”
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Improvement of an Edge-IoT Architecture Driven by Artificial Intelligence for Smart-Health Chronic Disease Management by William Alberto Cruz Castañeda, Pedro Bertemes Filho

“…Experimental results in a case study on glucose prediction noninvasively show that the architecture senses and acquires data that capture relevant characteristics. The study also establishes a baseline of twelve regression algorithms to assess the non-invasive glucose prediction performance regarding the mean squared error, root mean squared error, and r-squared score, and the catboost regressor outperforms the other models with 218.91 and 782.30 in MSE, 14.80 and 27.97 in RMSE, and 0.81 and 0.31 in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>, respectively, on training and test sets. …”
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State-of-the-Art on IoV-Based Deep Learning Framework for Enhanced Driving Behavior Recognition: Recent Progress, Technology Updates, Challenges, and Future Direction by Hongguang Li, Shafrida Sahrani, Mahidur R. Sarker, Yinglin Xiao

“…However, these advanced algorithms still face several challenges. We analyze relevant literature from 2015 to 2024, to uncover key trends and gaps in the development of applications for identifying dangerous driving behaviors, with a primary focus on optimizing integration to improve the accuracy and real-time capability of driving behavior prediction. …”
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The new era of artificial intelligence in neuroradiology: current research and promising tools by Fabíola Bezerra de Carvalho Macruz, Ana Luiza Mandetta Pettengil Dias, Celi Santos Andrade, Mariana Penteado Nucci, Carolina de Medeiros Rimkus, Leandro Tavares Lucato, Antônio José da Rocha, Felipe Campos Kitamura

“…We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. …”
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Detection of Mycotoxins in Cereal Grains and Nuts Using Machine Learning Integrated Hyperspectral Imaging: A Review by Md. Ahasan Kabir, Ivan Lee, Chandra B. Singh, Gayatri Mishra, Brajesh Kumar Panda, Sang-Heon Lee

“…This review explores hyperspectral imaging (HSI) integrated with machine learning (ML) algorithms as a promising approach for detecting and quantifying mycotoxins in cereal grains and nuts. …”
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Modelling Tyre-Road Noise with Data Mining Techniques by Elisabete Fraga FREITAS, Joaquim TINOCO, Francisco SOARES, Jocilene COSTA, Paulo CORTEZ, Paulo PEREIRA

“…The data modelling took into account three learning algorithms and three metrics to define the best predictive model. …”
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Machine learning approaches in the therapeutic outcome prediction in major depressive disorder: a systematic review by Veronica Atemnkeng Ntam, Tatjana Huebner, Michael Steffens, Catharina Scholl

“…Therefore, applying machine learning (ML) algorithms for therapeutic outcome prediction on the basis of individual patient data has become a promising approach to tailor the treatment strategy in MDD. …”
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Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model by Binqing Cai, Zhukai Ye, Shiwei Chen, Xun Liang

“…To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. The proposed model consists of three modules: first, a path information collection module preprocessing the video data to capture relevant operational path information; second, a path safety risk evaluation module employing You Only Look Once version 8 (YOLOv8) instance segmentation to identify potential risk factors along the operational path, e.g., potential drop zones and the positions of nearby workers; and finally, a path planning module utilizing an improved Dynamic Window Approach for tower cranes (TC-DWA) to avoid risky areas and optimize the operational path for enhanced safety. …”
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A Conceptual Framework for Planning Road Digital Twins by Munkhbaatar Buuveibaatar, Ioannis Brilakis, Matt Peck, George Economides, Wonhee Lee

“…To achieve this, we reviewed the relevant literature defining DT planning. We also examined stakeholders’ relevant guidelines and documents from national bodies that roadmap the road DT planning process to understand the scope and identify knowledge gaps. …”
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Electrocardiographic parameters and features of ventricular arrhythmias in various arrhythmogenic cardiomyopathy forms in the pediatric population: a systematic review and meta-ana... by D. Yu. Alekseeva, O. А. Kofeynikova, D. I. Marapov, E. S. Vasichkina

“…However, despite the multiparametric approach, there are certain limitations of the presented algorithms for disease establishment, especially in children. …”
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