Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence by Marco Zeppieri, Lorenzo Gardini, Carola Culiersi, Luigi Fontana, Mutali Musa, Fabiana D’Esposito, Pier Luigi Surico, Caterina Gagliano, Francesco Saverio Sorrentino

“…Aims: The purpose of this study is to examine the current uses of AI in the early diagnosis, treatment, and detection of glaucoma while highlighting the advantages and drawbacks of different AI models and algorithms. …”
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AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review by Nusrat Jahan Sarna, Farzana Ahmed Rithen, Umme Salma Jui, Sayma Belal, Al Amin, Tasnim Kabir Oishee, A. K. M. Muzahidul Islam

“…By analyzing vast datasets, AI can uncover hidden fraud patterns and dynamically adapt to emerging threats. …”
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Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records by Noorul Husna Abd Rahman, Khairunnisa Hasikin, Nasrul Anuar Abd Razak, Ayman Khallel Al-Ani, D. Jerline Sheebha Anni, Prabu Mohandas

“…This study proposes a data-driven machine-learning model for predicting medical device failure. …”
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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

“…We compared data imputation methods through density plots, and conducted δ-adjusted sensitivity analysis. …”
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Incorporating food plant distributions as important predictors in the habitat suitability model of sumatran orangutan (Pongo abelii) in Gunung Leuser National Park, Indonesia by Salmah Widyastuti, Wanda Kuswanda, M. Hadi Saputra, Hendra Helmanto, Nunu Anugrah, U. Mamat Rahmat, Rudianto Saragih Napitu, Andrinaldi Adnan, Iskandarrudin

“…Expanding monitoring efforts with advanced technologies, such as thermal drones, can improve data collection in remote areas. …”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

“…This review highlights recent developments in AI-driven stress detection, predictive modeling, remote sensing, and machine learning frameworks that support precision horticulture and climate-smart agriculture.…”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…Machine learning models including Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boost Classifier (GBC), Multilayer Perceptron (MLP), and 1D Convolutional Neural Network (1DCNN) were developed and evaluated. …”
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SECONDGRAM: Self-conditioned diffusion with gradient manipulation for longitudinal MRI imputation by Brandon Theodorou, Anant Dadu, Mike Nalls, Faraz Faghri, Jimeng Sun

“…These studies play a pivotal role in understanding neurodegenerative diseases like Alzheimer’s disease and Parkinson’s disease, yet the limited availability of paired longitudinal imaging datasets significantly restricts advanced machine learning applications. …”
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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

“…The Brier Score loss for ILPD dataset is 0.032 and 0.031 for the BUPA Liver Disorder Dataset.DiscussionThe research work highlights the potential of data balancing techniques and Ensemble models to improve predictive accuracy in liver disease diagnosis.…”
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Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models by Sandeep Reddy, Supriya Roy, Kay Weng Choy, Sourav Sharma, Karen M Dwyer, Chaitanya Manapragada, Zane Miller, Joy Cheon, Bahareh Nakisa

“…Methods: This study developed explainable machine learning models leveraging pathology data to accurately predict CKD trajectory, targeting improved prognostic capability even in early stages using limited datasets. …”
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Using Andersen's behavioral model of health care utilization in a decentralized program to examine the use of antenatal care in rural western Ethiopia. by Habtamu Tolera, Tegegne Gebre-Egziabher, Helmut Kloos

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Enhancing Corn Image Resolution Captured by Unmanned Aerial Vehicles With the Aid of Deep Learning by Emilia Alves Nogueira, Bruno Moraes Rocha, Gabriel da Silva Vieira, Afonso Ueslei da Fonseca, Juliana Paula Felix, Antonio Oliveira-Jr, Fabrizzio Soares

“…These techniques can aid in the early detection of pests, diseases, and nutritional deficiencies, facilitating faster and more effective interventions in corn crops. …”
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Increased mobilization of mesenchymal stem cells in patients with acute respiratory distress syndrome undergoing extracorporeal membrane oxygenation. by Christian Patry, Thalia Doniga, Franziska Lenz, Tim Viergutz, Christel Weiss, Burkhard Tönshoff, Armin Kalenka, Benito Yard, Jörg Krebs, Thomas Schaible, Grietje Beck, Neysan Rafat

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Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis by Yuanbo Bi, Dongrui Li, Ruochen Pang, Chengxv Du, Da Li, Xiaoyv Zhao, Haitao Lv

“…BackgroundEarly diagnosis can significantly improve survival rate of Pancreatic ductal adenocarcinoma (PDAC), but due to the insidious and non-specific early symptoms, most patients are not suitable for surgery when diagnosed. …”
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Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…The presented gridded dataset is published at <span class="uri">https://doi.org/10.6084/m9.figshare.25376695</span> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx49">Mandal et al.…”
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Low-dimensional multiscale fast SAR image registration method by Jiamu Li, Wenbo Yu, Zijian Wang, Jiaxin Xie, Xiaojie Zhou, Yabo Liu, Zhongjun Yu, Meng Li, Yi Wang

“…These descriptors are usually high-dimensional, resulting in increased computational complexity. …”
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A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof... by José Pablo Martínez Barbero, Francisco Javier Pérez García, David López Cornejo, Marta García Cerezo, Paula María Jiménez Gutiérrez, Luis Balderas, Miguel Lastra, Antonio Arauzo-Azofra, José M. Benítez, Antonio Jesús Láinez Ramos-Bossini

“…This study aimed to develop and evaluate a machine learning model that integrates radiomics features and T2*-weighted Dynamic Susceptibility Contrast MRI perfusion (DSC MRI) parameters to improve diagnostic accuracy in distinguishing these entities. …”
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Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study by Ruifang Hu, Xianping Liu, Yong Zhang, Clement Arthur, Dongguang Qin

“…Direct laryngoscopy and imaging modalities are conventional diagnostic methods. …”
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AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector by Şenda Yıldırım, Ahmet Deniz Yücekaya, Mustafa Hekimoğlu, Meltem Ucal, Mehmet Nafiz Aydin, İrem Kalafat

“…This change signifies a significant drop in income for automakers and their certified service networks. …”
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Early Diagnosis of Knee Osteoarthritis With a Natural Language Processing–Driven Approach Based on Clinician Notes: Development and Validation Study by Narathip Thanyakunsajja, Kulsawasd Jitkajornwanich, Shan Xu, Donghee Shin, Pattama Charoenporn

“…However, diagnosis through such data is often time-consuming. …”
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