Growth dynamics of splenic artery aneurysms: morphology, comorbidities, and vascular anatomical factors by Ahmet Tanyeri, Aygün Katmerlikaya, Rıdvan Akbulut, Mehmet Burak Çildağ

“…Abstract Background Splenic artery aneurysms (SAAs) are the most common visceral artery aneurysms, often detected incidentally due to the widespread use of cross-sectional imaging. …”
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Advancing Mental Health Care: A Comprehensive Review of Digital Tools and Technologies for Enhancing Diagnosis, Treatment, and Wellness by Muhammad Khalid Anser, Agha Amad Nabi, Ishfaq Ahmad, Muhammad Moinuddin Qazi Abro, Khalid Zaman

“…Wearable technology may detect heart rate, blood pressure, and sleep length, which may reveal mental health difficulties. …”
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Deep learning in microbiome analysis: a comprehensive review of neural network models by Piotr Przymus, Krzysztof Rykaczewski, Adrián Martín-Segura, Jaak Truu, Enrique Carrillo De Santa Pau, Mikhail Kolev, Mikhail Kolev, Irina Naskinova, Aleksandra Gruca, Alexia Sampri, Alexia Sampri, Marcus Frohme, Alina Nechyporenko, Alina Nechyporenko

“…These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. …”
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A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions by Raman Kumar, Raman Kumar, Sarvesh Garg, Rupinder Kaur, M. G. M. Johar, Sehijpal Singh, Sehijpal Singh, Soumya V. Menon, Pulkit Kumar, Pulkit Kumar, Ali Mohammed Hadi, Shams Abbass Hasson, Jasmina Lozanović

“…To systematically investigate this field, the literature is organized into five thematic categories such as “Heart Disease Detection and Diagnostics,” “Machine Learning Models and Algorithms for Healthcare,” “Feature Engineering and Optimization Techniques,” “Emerging Technologies in Healthcare,” and “Applications of AI Across Diseases and Conditions.” …”
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CSTFNet: A CNN and Dual Swin-Transformer Fusion Network for Remote Sensing Hyperspectral Data Fusion and Classification of Coastal Areas by Dekai Li, Harold Neira-Molina, Mengxing Huang, Syam M.S., Yu Zhang, Zhang Junfeng, Uzair Aslam Bhatti, Muhammad Asif, Nadia Sarhan, Emad Mahrous Awwad

“…In recent years, the convolutional neural network (CNN) framework and transformer models have demonstrated strong performance in HSI classification, especially in applications requiring precise change detection and analysis. However, due to the high dimensionality of HSI data and the complexity of spectral-spatial feature extraction, achieving accurate results in coastal areas remains challenging. …”
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Surface Clay and Mineral Exploration Using Hyperspectral Imaging: Advanced Techniques for Geospatial Analysis by S. Raut, N. Andure, A. Kadam, A. Dongare, A. Fulari, G. M. Sayyad, S. B. Sayyad, S. K. Shaikh, S. Sawant

“…Results demonstrate that this integrated approach—combining HSI with MNF, PPI, and SAM algorithms significantly enhances the accuracy and precision of clay and mineral detection, specifically identifying clay kaolinite, illite, saponite, and hematite, along with their spatial distribution within the study area. …”
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Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach by Mar Roca, Chengfa Benjamin Lee, Avi Putri Pertiwi, Alina Blume, Isabel Caballero, Gabriel Navarro, Dimosthenis Traganos

“…Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km2 of seagrass habitat extent. …”
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Phase Demodulation of Short-Cavity Fabry–Perot Interferometric Acoustic Sensors With Two Wavelengths by Hao Liao, Ping Lu, Li Liu, Shun Wang, Wenjun Ni, Xin Fu, Deming Liu, Jiangshan Zhang

“…A signal-to-noise ratio (SNR) of ∼53 dB is obtained at 80 Hz. These features make the sensor especially suitable for noise detection.…”
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Evaluation Value of Blood Biomarker Tests for Efficacy of EGFR-TKI in Advanced NSCLC Treatment by Rui FAN, Yonghui WU, Zhan GU, Yanbin PENG, Lixin WANG

“…CTCs were counted by enrichment-staining-computational algorithm to analyze malignant features, while ctDNA was assessed using digital PCR. …”
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Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy for Lithium Metal Battery Degradation via Machine Learning by Qianli Si, Shoichi Matsuda, Yasunobu Ando, Toshiyuki Momma, Yoshitaka Tateyama

“…Conversely, high‐frequency features related to charge transfer resistance play a dominant role in the KP detection. …”
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Integrating Otsu Thresholding and Random Forest for Land Use/Land Cover (LULC) Classification and Seasonal Analysis of Water and Snow/Ice by Xuexia Sun, Xiaoyao Li, Bingxiang Tan, Jian Gao, Lei Wang, Shimei Xiong

“…Accurate land use/land cover (LULC) classification and the detection of seasonal dynamics are crucial for effective environmental monitoring and resource management. …”
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Number of intraepithelial lymphocytes and presence of a subepithelial band in normal colonic mucosa differs according to stainings and evaluation method by Anne-Marie Kanstrup Fiehn, Peter Johan Heiberg Engel, Ulla Engel, Dea Natalie Munch Jepsen, Thomas Blixt, Julie Rasmussen, Signe Wildt, Wojciech Cebula, Andreea-Raluca Diac, Lars Kristian Munck

“…Sixty-one patients without diarrhea referred to screening colonoscopy due to a positive feacal blood test and presenting with endoscopically normal mucosa were included. Basic histological features, number of IELs, and thickness of a subepithelial band was manually evaluated and a deep learning-based algorithm was developed to digitally determine the number of IELs in each of the two compartments; surface epithelium and cryptal epithelium, and the density of lymphocytes in the lamina propria compartment. …”
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Assessing of a Radiation Therapy Sessionʼs Duration at the Stage of Pre-Radiation Preparation by M. N. Piatkevich, E. V. Titovich

“…The scope of this study was to create a mathematical model for calculating of a radiation therapy session duration during the pre-radiation planning stage.For dosimetric planning of radiation treatment the authors provided a formula and an algorithm for determining of a patientʼs irradiation session duration. …”
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Changes in objective characteristics in brain electrical activity in newborns as a function of birth weight by Anastasiya E. Runnova, Yuliya A. Zhuravleva, Evgeniy N. Egorov, Evgeniya E. Drozhdeva

“…The aim of the present study was to detect characteristic features of oscillatory electrical activity of the brain in the first day of postnatal life depending on the weight of newborns. …”
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The Use of LiDAR Terrain Data in Characterizing Surface Roughness and Microtopography by Kristen M. Brubaker, Wayne L. Myers, Patrick J. Drohan, Douglas A. Miller, Elizabeth W. Boyer

“…The availability of light detection and ranging data (LiDAR) has resulted in a new era of landscape analysis. …”
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Method and application of stability prediction model for rock slope by Yun Qi, Chenhao Bai, Xuping Li, Hongfei Duan, Wei Wang, Qingjie Qi

“…Abstract Slope instability is a prevalent dynamic disaster encountered in the construction of geotechnical engineering projects. Intelligent detection and early warning systems serve as crucial measures for preventing and controlling slope instability. …”
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Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting. by Josefine Freiberg, Roshan A Welikala, Jens Rovelt, Christopher G Owen, Alicja R Rudnicka, Miriam Kolko, Sarah A Barman, FOREVER consortium

“…<h4>Purpose</h4>To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non-experts.…”
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Machine learning modeling of cancer treatment-related cardiac events in breast cancer: utilizing dosiomics and radiomics by Sefika Dincer, Muge Akmansu, Oya Akyol

“…Machine learning models were optimized using the Tree-based Pipeline Optimization Tool (TPOT), identifying the gradient-boosted classification as the best-performing algorithm. Feature selection was conducted using gradient-boosted recursive feature elimination. …”
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Depression Recognition Using Daily Wearable-Derived Physiological Data by Xinyu Shui, Hao Xu, Shuping Tan, Dan Zhang

“…We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. …”
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Enhancing LiDAR Mapping with YOLO-Based Potential Dynamic Object Removal in Autonomous Driving by Seonghark Jeong, Heeseok Shin, Myeong-Jun Kim, Dongwan Kang, Seangwock Lee, Sangki Oh

“…In this study, we propose an enhanced LiDAR-based mapping and localization system that utilizes a camera-based YOLO (You Only Look Once) algorithm to detect and remove dynamic objects, such as vehicles, from the mapping process. …”
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