Use of artificial intelligence in the detection of primary prostate cancer in multiparametric MRI with its clinical outcomes: a protocol for a systematic review and meta-analysis by Yi Zhao, Alex Freeman, Mark Emberton, Daniel Kelly, Joseph M Norris, Alex Kirkham, Hayley C Whitaker, Maya Thomas, Benjamin Scott S Simpson, Sanjana Murali

“…Artificial intelligence (AI) is being integrated into clinical practice to support diagnostic and therapeutic imaging analysis to overcome these concerns. …”
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SARE: an optimized method for detection functional regulatory elements in genome by Mohammadjavad Hosseinpoor

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CNN-based remote dental diagnosis model for caries detection with grad-CAM by Donghyeok Kim, Jangkyum Kim, Seong Gon Choi

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LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection by Jyoti Parashar, Rituraj Jain, Mahesh K. Singh, Ashwani Kumar, Premananda Sahu, Kamal Upreti

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Fruit Detection Methods Based on Deep Learning in Agricultural Planting: A Systematic Literature Review by Xinyu Gong, Qiufeng Wu

“…With the continued advancement of agricultural intelligence, automation, and mechanization, artificial intelligence-driven fruit detection technology has developed rapidly. …”
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Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion by Rocío Aznar-Gimeno, Jose Luis Perez-Lasierra, Pablo Pérez-Lázaro, Irene Bosque-López, Marina Azpíroz-Puente, Pilar Salvo-Ibáñez, Martin Morita-Hernandez, Ana Caren Hernández-Ruiz, Antonio Gómez-Bernal, María de la Vega Rodrigalvarez-Chamarro, José-Víctor Alfaro-Santafé, Rafael del Hoyo-Alonso, Javier Alfaro-Santafé

“…This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. …”
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Artificial Intelligence and Employment by Chokri TERZI

“… Purpose: The purpose of this paper is to revisit the relationship between artificial intelligence (AI) and employment for eleven sectors of Tunisian economy in the period 2010-2022. …”
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Evaluating AI-Based Mitosis Detection for Breast Carcinoma in Digital Pathology: A Clinical Study on Routine Practice Integration by Clara Simmat, Loris Guichard, Stéphane Sockeel, Nicolas Pozin, Rémy Peyret, Magali Lacroix-Triki, Catherine Miquel, Arnaud Gauthier, Marie Sockeel, Sophie Prévot

“…However, this task is time-consuming and labor-intensive, and suffers from high variability between pathologists. <b>Methods</b>: To assist pathologists in routine diagnostics, we developed an artificial intelligence (AI)-based tool that uses whole slide images (WSIs) to detect mitoses, identify mitotic hotspots, and assign mitotic scores according to the Elston and Ellis grading system. …”
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Wheat Soil-Borne Mosaic Virus Disease Detection: A Perspective of Agricultural Decision-Making via Spectral Clustering and Multi-Indicator Feedback by Xue Hou, Chao Zhang, Yunsheng Song, Turki Alghamdi, Majed Aborokbah, Hui Zhang, Haoyue La, Yizhen Wang

“…The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. …”
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Lightweight detection of cotton leaf diseases using StyleGAN2-ADA and decoupled focused self-attention by Henghui Mo, Linjing Wei

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Machine Learning for Chronic Kidney Disease Detection from Planar and SPECT Scintigraphy: A Scoping Review by Dunja Vrbaški, Boban Vesin, Katerina Mangaroska

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Morphological Background-Subtraction Modeling for Analyzing Traffic Flow by Erik-Josué Moreno-Mejía, Daniel Canton-Enriquez, Ana-Marcela Herrera-Navarro, Hugo Jiménez-Hernández

“…Today, these systems can analyze various aspects, such as urban traffic, citizen behavior, and the detection of unusual activities. Most intelligent systems utilize photo sensors to gather information and assess situations. …”
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Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases by Roshan Kumar, V Srirama, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga

“…Large datasets of clinical variables are analyzed by machine learning (ML) to find patterns and co-relations that human clinicians might not be able to predict immediately. …”
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Deep learning-based approach to third molar impaction analysis with clinical classifications by Yunus Balel, Kaan Sağtaş

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LVI-PathNet: Segmentation-classification pipeline for detection of lymphovascular invasion in whole slide images of lung adenocarcinoma by Anna Timakova, Vladislav Ananev, Alexey Fayzullin, Egor Zemnuhov, Egor Rumyantsev, Andrey Zharov, Nicolay Zharkov, Varvara Zotova, Elena Shchelokova, Tatiana Demura, Peter Timashev, Vladimir Makarov

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A simple and user-friendly machine learning model to detect osteoporosis in health examination populations in Southern Taiwan by Wei-Chin Huang, I-Shu Chen, Hsien-Chung Yu, Chi-Shen Chen, Fu-Zong Wu, Chiao-Lin Hsu, Pin-Chieh Wu

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Acoustic Fault Diagnosis Method for Rotating Machinery Based on Collaborative Perception Information Aggregation Guidance Network by Chaofan Li, Fan Zhang, Qichen Wang, Yufei Han, Tianrui Li, Fei Teng, Cai Yi, Yongmeng Wu

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Integrated neural network framework for multi-object detection and recognition using UAV imagery by Mohammed Alshehri, Tingting Xue, Tingting Xue, Ghulam Mujtaba, Yahya AlQahtani, Nouf Abdullah Almujally, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…The integration of advanced models, YOLOv11 for detection, HRNet for segmentation, Deep SORT for tracking, CSRNet for counting, LSTM for trajectory prediction, and Vision Transformers for classification enables the framework to maintain high accuracy even under challenging conditions like occlusion, variable lighting, and scale variations.DiscussionThe outcomes show that the chosen deep learning system is powerful enough to deal with the challenges of aerial vehicle analysis and gives reliable and precise results in all the aforementioned tasks. …”
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AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training by Ali Berkol, Nergis Pervan Akman, Nesil Bor

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Locating Building Change via Adaptive Frequency Enhancement by Lei Lu, Yuejie Li, Fei Yang, Haixiong Li, Guoqiang Wang, Kun Xie

“…This paper seeks to explore a computational intelligence approach for tackling the above challenges. …”
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