A machine learning tool for identifying metastatic colorectal cancer in primary care by Eliya Abedi, Marcela Ewing, Elinor Nemlander, Jan Hasselström, Annika Sjövall, Axel C. Carlsson, Andreas Rosenblad

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gamUnet: designing global attention-based CNN architectures for enhanced oral cancer detection and segmentation by Jinyang Zhang, Hongxin Ding, Hongxin Ding, Runchuan Zhu, Weibin Liao, Weibin Liao, Junfeng Zhao, Junfeng Zhao, Min Gao, Xiaoyun Zhang

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Recent Advancements in Breast Cancer Detection: A Holistic Review of Microwaves, Ultrasound, and Photo-Acoustic Imaging Techniques by Rachida Boulerbah, Abdelhalim Chaabane, Ibraheem Al-Naib, Abdulrahman S. M. Alqadami, Djelloul Aissaoui, Hussein Attia

“…Breast Cancer Detection (BCD) presents persistent challenges in clinical practice, with patients often experiencing quite high mortality risks due to diagnostic inaccuracies driven by imaging complexities and variable breast densities. …”
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Learning-based early detection of post-hepatectomy liver failure using temporal perioperative data: a nationwide multicenter retrospective study in ChinaResearch in context by Kai Wang, Qian Yang, Kang Li, Shanhua Tang, Baoluhe Zhang, Xiangyun Liao, Shunda Du, Wenguang Fu, Zhiwei Li, Huanwei Chen, Haorong Xie, Pengxiang Huang, Jieyuan Li, Qiuting Wang, Haiqing Liu, Zhiwei Huang, Pheng Ann Heng, Xueshuai Wan, Chuanjiang Li, Weixin Si

“…Summary: Background: Post-hepatectomy liver failure (PHLF), defined as acute liver failure following hepatectomy, remains a major complication for postoperative mortality lacking early detection approaches. This study aimed to leverage cutting-edge artificial intelligence (AI) techniques for extensive temporal feature analysis using perioperative data, to advance the detection of PHLF to the first 24 h after surgery. …”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…For these illnesses to be managed and treated effectively, prompt and correct diagnosis is essential, yet it often presents a challenge due to the subjective nature of visual examination and the variability in clinical presentations. The field of dermatology has seen a change in recent years due to the convergence of artificial intelligence and medicine, which has produced creative methods for computer-aided diagnostics. …”
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Automated Detection and Biomarker Identification Associated with the Structural and Functional Progression of Glaucoma on Longitudinal Color Fundus Images by Iyad Majid, Zubin Mishra, Ziyuan Chris Wang, Vikas Chopra, Dale Heuer, Zhihong Jewel Hu

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Artificial Intelligence in Fetal and Pediatric Echocardiography by Alan Wang, Tam T. Doan, Charitha Reddy, Pei-Ni Jone

“…Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. …”
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Artificial Intelligence Applications in Rehabilitation Services by Betül Akalın, Mehmet Beşir Demirbaş

“…The use of artificial intelligence in rehabilitation services will provide flexible structuring of variables such as time, intensity, continuity and speed of treatment for the healthcare provider and the patient, objective data contribution with reliable and valid user detection hardware, real-time feedback, and real-life simulation. …”
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Comparison of Machine Learning Algorithms to Predict Down Syndrome During the Screening of the First Trimester of Pregnancy by Eduardo Alonso, Andoni Beristain, Jorge Burgos, Ibai Gurrutxaga

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Machine learning based calculation of refractive index of polyethylene glycol polymer by Walid Abdelfattah, Munthar Kadhim Abosaoda, Hardik Doshi, H.S. Shreenidhi, Manoranjan Parhi, Devendra Singh, Prabhjot Singh, Bilakshan Purohit, Kamal Kant Joshi, Ahmad Abumalek

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Variable Selection Algorithm for Explaining Anomalies in Real-World Regenerative Thermal Oxidizers by Min-Ji Seo, Myung-Ho Kim

“…Using the most influencing variables based on our method, XAI correctly identified the main variables without considering unnecessary variables. …”
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Generative Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease by Kwang-Sig Lee, Eun Sun Kim

“…This study reviews the recent progress of generative artificial intelligence for gastrointestinal disease (GID) from detection to diagnosis. …”
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A Dynamic Kalman Filtering Method for Multi-Object Fruit Tracking and Counting in Complex Orchards by Yaning Zhai, Ling Zhang, Xin Hu, Fanghu Yang, Yang Huang

“…With the rapid development of agricultural intelligence in recent years, automatic fruit detection and counting technologies have become increasingly significant for optimizing orchard management and advancing precision agriculture. …”
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Artificial Intelligence in Healthcare: Innovation and Impact in Medical Practice by Amelia-Maria ISAC, Corina VERNIC

“… Background: Artificial Intelligence (AI) is transforming medical healthcare by improving diagnostics, optimizing treatment plans, and enhancing patient outcomes. …”
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Artificial intelligence in hospital infection prevention: an integrative review by Rabie Adel El Arab, Zainab Almoosa, May Alkhunaizi, May Alkhunaizi, Fuad H. Abuadas, Joel Somerville, Joel Somerville

“…BackgroundHospital-acquired infections (HAIs) represent a persistent challenge in healthcare, contributing to substantial morbidity, mortality, and economic burden. Artificial intelligence (AI) offers promising potential for improving HAIs prevention through advanced predictive capabilities.ObjectiveTo evaluate the effectiveness, usability, and challenges of AI models in preventing, detecting, and managing HAIs.MethodsThis integrative review synthesized findings from 42 studies, guided by the SPIDER framework for inclusion criteria. …”
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A Novel You Only Listen Once (YOLO) Deep Learning Model for Automatic Prominent Bowel Sounds Detection: Feasibility Study in Healthy Subjects by Rohan Kalahasty, Gayathri Yerrapragada, Jieun Lee, Keerthy Gopalakrishnan, Avneet Kaur, Pratyusha Muddaloor, Divyanshi Sood, Charmy Parikh, Jay Gohri, Gianeshwaree Alias Rachna Panjwani, Naghmeh Asadimanesh, Rabiah Aslam Ansari, Swetha Rapolu, Poonguzhali Elangovan, Shiva Sankari Karuppiah, Vijaya M. Dasari, Scott A. Helgeson, Venkata S. Akshintala, Shivaram P. Arunachalam

“…The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. …”
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Exploring the impact of gender-related variables on health measures and perceived stress by Maria Picó-Pérez, Maria Picó-Pérez, Maria Picó-Pérez, Marisa S. Coelho, Marisa S. Coelho, Rita Vieira, Rita Vieira, Rita Vieira, Mafalda Machado-Sousa, Mafalda Machado-Sousa, Mafalda Machado-Sousa, Pedro Morgado, Pedro Morgado, Pedro Morgado

“…Regarding stress, it was positively associated with discrimination and work strain, while it was negatively associated with social support, emotional intelligence and risk-taking. Finally, discrimination and work strain were positively associated with mental health worsening and activity limitations, while social support was negatively associated with mental health worsening.DiscussionIn conclusion, by approaching gender from a multidimensional perspective we detected specific factors influencing health outcomes, showing that the relational aspects of gender are particularly relevant for mental health.…”
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Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management by Georgios I. Barkas, Ilias E. Dimeas, Ourania S. Kotsiou

“…This systematic scoping review evaluates the current landscape of artificial intelligence (AI) and machine learning (ML) applications in sepsis care, focusing on early detection, personalized antibiotic management, and resistance forecasting. …”
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Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm by Wenliang Zhu, Guanming Qiu

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Detection of Driving Distractions and Their Impacts by Arian Shajari, Houshyar Asadi, Sebastien Glaser, Adetokunbo Arogbonlo, Shady Mohamed, Lars Kooijman, Ahmad Abu Alqumsan, Saeid Nahavandi

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