Deep learning for video-based assessment of endotracheal intubation skills by Jean-Paul Ainam, Erim Yanik, Rahul Rahul, Taylor Kunkes, Lora Cavuoto, Brian Clemency, Kaori Tanaka, Matthew Hackett, Jack Norfleet, Suvranu De

“…A 1D convolutional model enhanced with a cross-view attention module then uses AE features to make assessments. …”
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Design and Implementation of a High-Throughput Digital Microfluidic System Based on Optimized YOLOv8 Object Detection by Ming Cao, Wufeng Duan, Zuwei Huang, Huihong Liang, Fanrong Ai, Xianming Liu

“…For droplet recognition, the YOLOv8 model was optimized through the integration of GAM_Attention and EMA mechanisms, which strengthen feature extraction capabilities and detection performance. …”
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Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network by Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

“…Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was evaluated on a SARS-CoV-2 CT scan dataset, demonstrating superior performance across all metrics compared to baseline models. …”
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Machine learning for optimal parameter prediction in free space continuous-variable quantum key distribution by Dong Chen, Cheng Jin, Liu Ao, Chen Lanjian, Chen YuJie, Yin Peng, Wu TianYi

“…Hence, this optimization approach is infeasible for satellite-based CV-QKD due to satellites’ low power feature. In this paper, a neural network model that directly predicting the optimal modulation variance in nearly real time is proposed for free space gaussian modulated CV-QKD protocols. …”
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Optimized Two-Stage Anomaly Detection and Recovery in Smart Grid Data Using Enhanced DeBERTa-v3 Verification System by Xiao Liao, Wei Cui, Min Zhang, Aiwu Zhang, Pan Hu

“…Key innovations include (1) a balanced loss function combining focal loss (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> = 0.65, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula> = 1.2), Dice loss (weight = 0.5), and contrastive learning (weight = 0.03) to reduce over-rejection by 73.4%; (2) an ensemble verification strategy using multithreshold voting, achieving 91.2% accuracy; (3) optimized sample weighting prioritizing missed positives (weight = 10.0); (4) comprehensive feature extraction, including frequency domain and entropy features; and (5) integration of a generative time series model (TimER) for high-precision recovery of tampered data points. …”
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Homotherapy for heteropathy of chronic kidney disease and oligoasthenozoospermia through regulating SIRT1/NF-κB pathway by Shenqi pills by Shuo Huang, Qihan Luo, Xinyue Li, Yiming Liu, Jiale Wei, Sichen Wang, Ping Qiu, Changyu Li, Changyu Li

“…Both conditions share features of yang deficiency, including fatigue, cold intolerance, and weakness. …”
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Diagnostic Value of F-FDG PET/CT Radiomics in Lymphoma: A Systematic Review and Meta-Analysis by Chaoying Liu MD, Jun Zhao PhD, Heng Zhang PhD, Xinye Ni PhD

“…Introduction Various machine learning models and features have been proposed for lymphoma diagnosis using 18 F-fluorodeoxyglucose ( 18 F-FDG) PET/CT radiomics. …”
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A multimodal fusion system predicting survival benefits of immune checkpoint inhibitors in unresectable hepatocellular carcinoma by Jun Xu, Tengfei Wang, Junjun Li, Yong Wang, Zhangxiang Zhu, Xiao Fu, Junjie Wang, Zhenglin Zhang, Wei Cai, Ruipeng Song, Changlong Hou, Li-Zhuang Yang, Hongzhi Wang, Stephen T. C. Wong, Hai Li

“…Using retrospective multicenter data (n = 859), the MMF combining an ensemble deep learning (Ensemble-DL) model with clinical variables achieved strong external validation performance (C-index: OS = 0.74, PFS = 0.69), outperforming radiomics (29.8% OS improvement), mRECIST (27.6% OS improvement), clinical benchmarks (C-index: OS = 0.67, p = 0.0011; PFS = 0.65, p = 0.033), and Ensemble-DL (C-index: OS = 0.69, p = 0.0028; PFS = 0.66, p = 0.044). …”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…To statistically synthesize performance and efficacy results, more reviews are needed. …”
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Elevated CD4⁺/CD8⁺ ratio and D-dimer as diagnostic biomarkers for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis versus infectious mononucleosis in children: a re... by Yun Cheng, Wenjia Tong, Wangqiang Li, Yuanyuan Duan, Jingjing Jin, Biquan Chen

“…These parameters, along with clinical and laboratory features, were compared between the EBV-IM and EBV-HLH groups. …”
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Breaking Barriers in Orthodontics: An Experimental Study on How Stabilization Discs Improve Mini-Implant Outcomes by Tinela Panaite, Cristian Liviu Romanec, Mihnea Iacob, Carina Balcos, Carmen Savin, Nicolae Daniel Olteanu, Raluca-Maria Vieriu, Chehab Alice, Irina Nicoleta Zetu

“…<b>Methods</b>: A finite element analysis (FEA) model was created for a commercially available mini-implant (2.0 mm in diameter and 12 mm in length). …”
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Adaptive zero velocity correction method for fiber optic inertial navigation system in coal mining roadheader by Qinghua MAO, Qing ZHOU, Jianquan CHAI, Yanzhang CHEN, Wenjuan YANG, Xusheng XUE

“…Secondly, the time-frequency domain features of IMF components are extracted, and the principal component analysis method is used to reduce the dimension to reduce the complexity of the diagnostic model and the difficulty of data analysis. …”
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Glycan dysregulation as one of major metabolic subtypes is associated with TERC overexpression and poor outcomes in cervical cancer by Yanlei Dong, Xinyuan Zhang, Jingjie Zhao, Qingzhen Hou, Yunhai Yu, Yu Wu, Xing Shi, Lina Wang, Dawei Xu

“…The association of metabolic groups with clinical characteristics, telomere maintenance and somatic alterations was assessed to define molecular features of each subtype. Finally, the metabolomic analyses were carried out to directly measure metabolites in tumors and their non-tumorous adjacent tissues (NTs) from 10 CC patients using ultra performance liquid chromatography-mass spectrometry (UPLC-MS).ResultsThe analysis of 2752 metabolism-related gene expression in TCGA 304 CC tumors showed a significant expression heterogeneity of these genes. …”
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Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative‌ brain metastasis risk in non-small cell lung cancer by Ye Niu, Hao-Bo Jia, Xue-Meng Li, Wen-Juan Huang, Ping-Ping Liu, Le Liu, Zeng-Yao Liu, Qiu-Jun Wang, Yuan-Zhou Li, Shi-Di Miao, Rui-Tao Wang, Ze-Xun Duan

“…Multivariate logistic regression identified independent BM predictors, which were incorporated into a nomogram. Model performance was assessed via area under the receiver operating characteristic curve (AUC), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). …”
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An auxiliary diagnostic strategy for distinguishing Guillain–Barré syndrome and chronic inflammatory demyelinating polyneuropathy: combining platelet-to-lymphocyte ratio and cerebr... by Simin Song, Yunfei Bai, Haoran Mu, Jianru Xiao, Wei Li, Yuying Zhao, Chuanzhu Yan, Jinfan Zheng, Caijing Wang, Qinzhou Wang

“…Binary logistic regression was performed to identify risk factors and establish a prediction model. …”
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The transfer learning of uncertainty quantification for industrial plant fault diagnosis system design by J. Blair, O. Amin, B. D. Brown, S. McArthur, A. Forbes, B. Stephen

“…The performance and confidence in fault detection and diagnostic systems can be undermined by data pipelines that feature multiple compounding sources of uncertainty. …”
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Deep Residual Network With Integrated StarDist Nuclei Segmentation for Papillary Thyroid Cancer Identification: A Pathologist-Inspired Approach by Nabila Husna Shabrina, Dadang Gunawan, Mia Rizkinia, Agnes Stephanie Harahap, Mohammad Ikhsan, Rifai Chai, Maria Francisca Ham

“…Our findings confirmed that incorporating nuclei segmentation significantly enhances classification performance and model interpretability. The proposed model enhances feature localization by integrating nucleus segmentation with Deep Residual Networks, yielding a practical and efficient solution for histopathological PTC analysis.…”
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A knowledge graph attention network for the cold‐start problem in intelligent manufacturing: Interpretability and accuracy improvement by Ziye Zhou, Yuqi Zhang, Shuize Wang, David San Martin, Yongqian Liu, Yang Liu, Chenchong Wang, Wei Xu

“…Only the SteelKGAT model accurately captures the feature trend, thereby offering correct guidance in product tuning, which is of practical significance for new product development (NPD). …”
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Clouds Detection in Polar Icy Terrains: A Deformable Attention-Based Deep Neural Network for Multispectral Polar Scene Parsing by Shaojin Dong, Cailan Gong, Yong Hu, Long Cheng, Yang Wang, Fuqiang Zheng

“…Experimental results demonstrate that our proposed model outperforms baseline models primarily due to superior feature extraction. …”
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WGCAMNet: Wasserstein Generative Adversarial Network Augmented and Custom Attention Mechanism Based Deep Neural Network for Enhanced Brain Tumor Detection and Classification by Fatema Binte Alam, Tahasin Ahmed Fahim, Md Asef, Md Azad Hossain, M. Ali Akber Dewan

“…Additionally, a Gaussian filter and normalization were applied during preprocessing to reduce noise, highlighting its superior accuracy and edge preservation by comparing its performance to Median and Bilateral filters. The classifier model combines parallel feature extraction from modified InceptionV3 and VGG19 followed by custom attention mechanisms for effectively capturing the characteristics of each tumor type. …”
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