Patient-specific prostate segmentation in kilovoltage images for radiation therapy intrafraction monitoring via deep learning by Adam Mylonas, Zeyao Li, Marco Mueller, Jeremy T. Booth, Ryan Brown, Mark Gardner, Andrew Kneebone, Thomas Eade, Paul J. Keall, Doan Trang Nguyen

“…Typically, fiducial markers are implanted as a surrogate for the tumour position due to the low radiographic contrast of soft tissues in kilovoltage (kV) images. …”
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Lessons Learned From Co-Designing Educational Programs for Student and Practicing Healthcare Professionals in Nursing Homes: A Participatory Qualitative Study by Irene J.M. Muller-Schoof, Marjolein E. A. Verbiest, Miranda Snoeren, Katrien G. Luijkx

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Distributed Decision Making for Electromagnetic Radiation Source Localization Using Multi-Agent Deep Reinforcement Learning by Jiteng Chen, Zehui Zhang, Dan Fan, Chaoqun Hou, Yue Zhang, Teng Hou, Xiangni Zou, Jun Zhao

“…To address these challenges, we propose the Multi-UAV Reconnaissance Proximal Policy Optimization (MURPPO) algorithm based on a distributed reinforcement learning framework, which utilizes an independent decision making mechanism and collaborative positioning method with multiple UAVs to achieve high-precision detection and localization of radiation sources. …”
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AI Under Attack: Metric-Driven Analysis of Cybersecurity Threats in Deep Learning Models for Healthcare Applications by Sarfraz Brohi, Qurat-ul-ain Mastoi

“…In this paper, we provide a comprehensive analysis of key attack vectors, including adversarial attacks, such as the gradient-based Fast Gradient Sign Method (FGSM), evasion attacks (perturbation-based), and data poisoning, which threaten the reliability of DL models, with a specific focus on breast cancer detection. …”
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Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs by Michael Myers, Michael D. Brown, Sarkhan Badirli, George J. Eckert, Diane Helen-Marie Johnson, Hakan Turkkahraman

“…The highest prediction accuracy within the 2 mm or 2° clinical thresholds was achieved for maxilla to cranial base angle (80%), lower incisor position (75%), and maxilla to mandible angle (70%). …”
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SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems by Umesh Kumar Lilhore, Yogesh Kumar Sharma, Sarita Simaiya, Roobaea Alroobaea, Abdullah M. Baqasah, Majed Alsafyani, Afnan Alhazmi

“…This study introduces SkinEHDLF, an innovative deep-learning model that enhances skin cancer classification. …”
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Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos by Shadi Jaradat, Mohammed Elhenawy, Huthaifa I. Ashqar, Alexander Paz, Richi Nayak

“…The zero-shot learning method performed better, achieving an accuracy of 81.2% and an F1-score of 81.9%. …”
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A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

“…This method centered on the initial meter gas production index and integrated multiple machine-learning algorithms. …”
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Image-based and textbook-based virtual reality training on operational skills among junior residents: a proof of concept study by Wan-Ni Lin, Hai-Hua Chuang, Yi-Ping Chao, Li-Jen Hsin, Chung-Jan Kang, Tuan-Jen Fang, Hsueh-Yu Li, Li-Ang Lee

“…However, the impact of IBVR on learning outcomes requires further investigation. This study aims to assess the efficacy of IBVR compared to textbook-based VR (TBVR) in teaching operational skills to junior residents. …”
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Data-Enabled Intelligence in Complex Industrial Systems Cross-Model Transformer Method for Medical Image Synthesis by Zebin Hu, Hao Liu, Zhendong Li, Zekuan Yu

“…More specifically, the proposed method captures locality feature via local discriminator based on CNN and utilizes long-range dependencies to learning global feature through global discriminator based on transformer architecture. …”
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Binocular Vision-Based Target Detection Algorithm by Huiguo Zhang

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Using Artificial Intelligence for Detecting Diabetic Foot Osteomyelitis: Validation of Deep Learning Model for Plain Radiograph Interpretation by Francisco Javier Álvaro-Afonso, Aroa Tardáguila-García, Mateo López-Moral, Irene Sanz-Corbalán, Esther García-Morales, José Luis Lázaro-Martínez

“…Objective: To develop and validate a ResNet-50-based deep learning model for automatic detection of osteomyelitis (DFO) in plain radiographs of patients with diabetic foot ulcers (DFUs). …”
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DSGRec: dual-path selection graph for multimodal recommendation by Zihao Liu, Wen Qu

“…Although methods based on graph convolutional networks (GCNs) have achieved notable success, they still face two key limitations: (1) the narrow interpretation of interaction information, leading to incomplete modeling of user behavior, and (2) a lack of fine-grained collaboration between user behavior and multi-modal information. …”
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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI by M Nisha, T Kannan, K Sivasankari

“…To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. …”
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Building virtual communities of practice in post-editing training: A mixed-method quasi-experimental study by Lyu Wang, Xiangling Wang

“…Students' perceptions of the VCoP method were generally positive, showing its usability.…”
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Integration of an Audiovisual Learning Resource in a Podiatric Medical Infectious Disease Course: Multiple Cohort Pilot Study by Garrik Hoyt, Chandra Shekhar Bakshi, Paramita Basu

“…These results contribute to the discourse on innovative educational methods and highlight the potential of multimedia-based learning resources to enrich medical curricula. …”
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Multi-scale sparse convolution and point convolution adaptive fusion point cloud semantic segmentation method by Yuxuan Bi, Peng Liu, Tianyi Zhang, Jialin Shi, Caixia Wang

“…By prioritizing the importance of input feature positions, this module enhances the sparse learning performance of critical feature information. …”
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The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity by James E. Galvin, Michael J. Kleiman, Heather M. Harris, Paul W. Estes

“…The CARM differentiated between individuals with amyloid and without amyloid by PET (p < 0.001, Cohen’s d = 0.618) and blood-based biomarkers (p’s < 0.001). Amyloid positivity and cognitive impairment increased across four CARM thresholds (p < 0.001). …”
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Unveiling spatiotemporal evolution and driving factors of ecosystem service value: interpretable HGB-SHAP machine learning model by Xiangming Xu, Xinyi Zhang, Linghua Qin, Rui Li

“…Furthermore, the driving factors of ESV were explored using the explainable machine learning method.ResultsThe findings are as follows: (1) The net ESV of the Gangjiang Upstream Basin (GUB) has undergone a decline from 1990 to 2000, with climate regulation and hydrological regulation collectively accounting for approximately 50% of all functions. (2) A mere 0.69% of the areas exhibited an increase in the level of ESV, while 11.19% demonstrated a decline by 2020, based on the grid scale. …”
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Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context by Bekan Kitaw, Chera Asefa, Firew Legese

“…Feature selection employed filter methods based on mutual information, and F-score was used to assess anemia severity prediction across four classes. …”
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