Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review by Umile Giuseppe Longo, Alberto Lalli, Guido Nicodemi, Matteo Giuseppe Pisani, Alessandro De Sire, Pieter D'Hooghe, Ara Nazarian, Jacob F. Oeding, Balint Zsidai, Kristian Samuelsson

“…Studies with insufficient data regarding the output variable used to assess the reliability of the ML model, those applying deterministic algorithms, unrelated topics, protocol studies, and other systematic reviews were excluded from the final synthesis. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Due to long-term exposure to complex and variable environmental conditions, concrete dam's structural safety faces numerous challenges. …”
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Investigation into the prognostic factors of early recurrence and progression in previously untreated diffuse large B-cell lymphoma and a statistical prediction model for POD12 by Ke Lian, Wenyao Zhu, Zhihui Hu, Fang Su, CaiXia Xu, Hui Wang

“…A prediction method for the characteristic variables of POD12 risk is proposed using the CNN-LSTM deep learning model based on chaotic time series. …”
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EvapoDeep: A Dual Deep Learning Framework Utilizing GNSS Data for Evapotranspiration Modeling and Predictive Analysis by Saeed Ebrahimi, Saeid Haji-Aghajany, Yazdan Amerian, Melika Tasan

“…Previous studies have demonstrated that calibrating the TH model with additional variables can improve its accuracy; however, these efforts have been largely preliminary. …”
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Estimating Winter Wheat Canopy Chlorophyll Content Through the Integration of Unmanned Aerial Vehicle Spectral and Textural Insights by Huiling Miao, Rui Zhang, Zhenghua Song, Qingrui Chang

“…Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. …”
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DM_CorrMatch: a semi-supervised semantic segmentation framework for rapeseed flower coverage estimation using UAV imagery by Jie Li, Chengyong Zhu, Chenbo Yang, Quan Zheng, Binhui Wang, Jingmin Tu, Qian Zhang, Sheng Liu, Xinfa Wang, Jiangwei Qiao

“…However, the irregular and variable morphology of rapeseed inflorescences presents significant challenges in segmentation. …”
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Prognosis of COVID-19 Using Artificial Intelligence: A Systematic Review and Meta-analysis by Saeed Reza Motamedian, Negin Cheraghi, Sadra Mohaghegh, Elham Babadi Oregani, Mahrsa Amjadi, Parnian Shobeiri, Niusha Solouki, Nikoo Ahmadi, Yassine Bouchareb, Arman Rahmim

“…The specificity of 69%, 89% and 89% were reported for the aforementioned variables. Conclusion: Based on the included articles, machine learning and deep learning methods used for the prognosis of COVID-19 patients using radiomic features from CT or CXR images can help clinicians manage patients and allocate resources more effectively. …”
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Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea by Hyeongmok Lee, Go-Eun Kim, Woo-Jin Shin, Yuyoung Lee, Sanghee Park, Kwang-Sik Lee, Jina Jeong, Seung-Ik Park, Sungwook Choung

“…., lithology, tectonic settings) and geochemical compositions, are used as input variables for training a feedforward deep neural network. …”
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Predicting mucosal healing in Crohn’s disease: development of a deep-learning model based on intestinal ultrasound images by Li Ma, Yuepeng Chen, Xiangling Fu, Jing Qin, Yanwen Luo, Yuanjing Gao, Wenbo Li, Mengsu Xiao, Zheng Cao, Jialin Shi, Qingli Zhu, Chenyi Guo, Ji Wu

“…Key Points Response to medication treatment is highly variable among patients with CD. High-resolution IUS images of the intestinal wall may hide significant characteristics for treatment response. …”
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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising by Samy Abd El-Nabi, Ahmed F. Ibrahim, El-Sayed M. El-Rabaie, Osama F. Hassan, Naglaa F. Soliman, Khalil F. Ramadan, Walid El-Shafai

“…Moreover, a detailed sensitivity analysis of data augmentation strategies reveals that techniques such as rotation and horizontal flip substantially enhance the model’s generalization across variable visual inputs. The system also demonstrates improved robustness under real-world black-box scenarios and adversarial conditions. …”
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Advanced attention-driven deep learning architectures for multi-depth soil temperature prediction by Safwan Mohammed, Sana Arshad, Akasairi Ocwa, Main Al-Dalahmeh, Ashraf ALDabbas, Muhammad Manhal Alzoubi, Attila Vad, Endre Harsányi

“…This research aimed to analyze and predict the dynamic relationship of multi depth soil temperature (SDT) at (5 cm, 10 cm, 20 cm, and 50 cm) with meteorological variables using Bi-wavelet coherence and deep learning models. …”
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Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features by Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei

“…One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. …”
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A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers by Yadong Yao, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Miao Song, Qiang Li, Jie Li

“…A low-order polynomial was applied to separate the reflected signals, extracting parameters such as phase, frequency, amplitude, and effective reflector height. Auxiliary variables, including the Normalized Microwave Reflection Index (NMRI), cumulative rainfall, and daily average evaporation, were used to further improve inversion accuracy. …”
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Study on the inversion and spatiotemporal variation mechanism of soil salinization at multiple depths in typical oases in arid areas: A case study of Wei-Ku Oasis by Jinming Zhang, Jianli Ding, Zihan Zhang, Jinjie Wang, Xu Zeng, Xiangyu Ge

“…Taking the Wei-Ku Oasis, a typical arid region oasis, as an example, this study uses Landsat remote sensing imagery as the data source, incorporating soil salinity field measurements over a decade, employing the Bootstrap Soft Shrinkage(BOSS) algorithm to select feature variables, and building soil salinity inversion models at various depths through a Convolutional Neural Networks and Long Short-Term Memory networks (CNN-LSTM) framework. …”
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Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Given the complex clinical characteristics of T2DM-FT patients, traditional statistical methods are often insufficient to effectively analyze nonlinear relationships among multiple variables. Machine learning techniques have garnered widespread attention due to their advantages in modeling high-dimensional, heterogeneous data.ObjectiveThis study was to evaluate the predictive capability of a support vector machine (SVM) model based on serum NRG4 combined with a convolutional neural network (CNN) and long short-term memory network (LSTM)-based ultrasound feature classification (SVM-CNN+LSTM) model for predicting the occurrence of FT in patients with T2DM.MethodsStudied 500 T2DM patients (60 with FT, 440 without), and 200 healthy controls. …”
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Tree Species Detection and Enhancing Semantic Segmentation Using Machine Learning Models with Integrated Multispectral Channels from PlanetScope and Digital Aerial Photogrammetry i... by Arun Gyawali, Mika Aalto, Tapio Ranta

“…For semantic segmentation, the CatBoost model with 20 bands outperformed other models, achieving 85% accuracy, 80% Kappa, and 81% MCC, with CHM, EVI, NIRPlanet, GreenPlanet, NDGI, GNDVI, and NDVI being the most influential variables. These results indicate that a simple boosting model like CatBoost can outperform more complex CNNs for semantic segmentation in young forests.…”
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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…The study focuses on three bacterial strains including Pseudomonas pseudoalcaligenes CECT 5344, Pseudomonas putida KT2440, and Escherichia coli ATCC 25,922 cultured in Luria Bertani (LB) and M63 media, across varying initial pH levels, time intervals, and bacterial cell concentrations (OD600). Key input variables for the models included bacterial type, culture medium type, initial pH, time (hours), and bacterial cell concentration, all critical to pH dynamics. …”
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NeoPred: dual-phase CT AI forecasts pathologic response to neoadjuvant chemo-immunotherapy in NSCLC by Rui Wang, Guan Wang, Ying Huang, Yu Jiang, Zhigang Li, Chao Yang, Yuan Zhang, Hengrui Liang, Jianxing He, Zhichao Liu, Hongxu Liu, Jia Zhang, Hong Yu, Guangjian Zhang, Hongshen Deng, Zeping Yan, Wenhai Fu, Jianqi Zheng, Runchen Wang, Houlu Xiao, Zhenlin Chen, Xiaomin Ge, Pingwen Yu, Junke Fu, Bohao Liu, Chudong Wang, Yuechun Lin, Linchong Huang, Fei Cui

“…Three 3-dimensional convolutional neural networks (pre-treatment CT, pre-surgical CT, dual-phase CT) were developed; the best-performing dual-phase model (NeoPred) optionally integrated clinical variables. …”
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Hadron Identification Prospects with Granular Calorimeters by Andrea De Vita, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Ralf Keidel, Jan Kieseler, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Kylian Schmidt, Pietro Vischia, Joseph Willmore

“…Additionally, the results highlight the importance of shower radius, energy fractions, and timing variables in distinguishing particle types. The XGBoost model demonstrated computational efficiency and interpretability advantages over deep learning for tabular data structures, while achieving similar classification performance. …”
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Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions by John Adeoye, Yuxiong Su

“…Tabular-to-2D image data transformation was achieved by creating a feature matrix from encoded labels of the input variables arranged according to their correlation coefficient. …”
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