Transforming Bone Tunnel Evaluation in Anterior Cruciate Ligament Reconstruction: Introducing a Novel Deep Learning System and the TB-Seg Dataset by Ke Xie, Mingqian Yu, Jeremy Ho-Pak Liu, Qixiang Ma, Limin Zou, Gene Chi-Wai Man, Jiankun Xu, Patrick Shu-Hang Yung, Zheng Li, Michael Tim-Yun Ong

“…Evaluating bone tunnels is crucial for assessing functional recovery after anterior cruciate ligament reconstruction. …”
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The Comprehensive Effect of Depression, Anxiety, and Headache on Pain Intensity and Painkiller Use in Patients with Headache Analyzed by Unsupervised Clustering Using Machine Learn... by Jong-Ho Kim, Minha Ahn, Jong-Hee Sohn, Sung-Mi Hwang, Jae-Jun Lee, Young-Suk Kwon

“…The clusters demonstrated significant differences which can help better characterize patients based on their psychological and functional impact.…”
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Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients by Jin Feng, YunDe Li, ZiJun Huang, Yehang Chen, SenLiang Lu, RongLiang Hu, QingHui Hu, YuYao Chen, XiMiao Wang, Yong Fan, Jing He

“…IntroductionMotor imagery functional near-infrared spectroscopy (MI-fNIRS) offers precise monitoring of neural activity in stroke rehabilitation, yet accurate cross-subject classification remains challenging due to limited training samples and significant inter-subject variability. …”
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A non-sub-sampled shearlet transform-based deep learning sub band enhancement and fusion method for multi-modal images by Sudhakar Sengan, Praveen Gugulothu, Roobaea Alroobaea, Julian L. Webber, Abolfazl Mehbodniya, Amr Yousef

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Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment by A. Koraniany, G.R. Ansarifar

“…The reactor core of a functioning power plant usually contains hundreds of fuel assemblies. …”
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Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorectal cancer by Tianle Xue, Yunpeng Chen, Xiaomeng Li, Zhixiang Zhou, Qiyang Chen

“…An integrated machine learning (IML) model—utilizing 12 algorithms with 10-fold cross-validation—was constructed to pinpoint key diagnostic biomarkers. …”
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Advanced smart assistance with enhancing social interaction and daily activities for visually impaired individuals using deep learning with modified seagull optimization by Sana Alazwari, Hussah Nasser AlEisa, Mohammed Rizwanullah, Radwa Marzouk

“…The ADD-MSGOEL method is designed to enhance the social life and daily functioning of visually impaired people by accurately detecting damage and potential hazards in their surroundings. …”
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Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis by Yanwei Cheng, Haoran Peng, Qiao Chen, Lijun Xu, Lijie Qin

“…This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. …”
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A multi-robot collaborative manipulation framework for dynamic and obstacle-dense environments: integration of deep learning for real-time task execution by Afnan Ahmed Adil, Saber Sakhrieh, Jinane Mounsef, Noel Maalouf

“…The framework incorporates deep learning-based object detection (YOLOv2) to identify target objects using a simulated RGB-D camera mounted on the manipulator’s end-effector. …”
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Results of a Qualitative Exploratory Study: Under Which Conditions Do Very Old People Learn How to Adopt Digital Media? by Julian Wangler, Michael Jansky

“…From this theoretical standpoint, it is a challenge for older people to learn how to handle new media and integrate them into their current living situation. …”
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The relationship between peer relationships and learning engagement in Tibetan primary students: investigating the role of basic psychological needs and psychological capital as me... by Hui Du, Xin Xu, Cheng Cheng

“…The study also indicated that BPN and PC functioned as mediators in the connection between peer relationships and learning engagement. …”
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On-Demand Gait-Synchronous Electrical Cueing in Parkinson's Disease Using Machine Learning and Edge Computing: A Pilot Study by Ardit Dvorani, Constantin Wiesener, Christina Salchow-Hommen, Magdalena Jochner, Lotta Spieker, Matej Skrobot, Hanno Voigt, Andrea Kuhn, Nikolaus Wenger, Thomas Schauer

“…We demonstrate the system's functionality and safety during on-demand gait-synchronous electrical cueing in two patients, performing freezing of gait assessments. …”
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Development of CSOARG: a single-cell and multi-omics-based machine learning model for ovarian cancer prognosis and drug response prediction by Junyu Chen, Junyu Chen, Bin Guan, Bin Guan, Jihong Zhang, Xin Li, Xin Li, Jingyi Fang, Jingyi Fang, Wencai Guan, Qi Lu, Qi Lu, Qi Lu, Guoxiong Xu, Guoxiong Xu

“…Immunology analysis, functional enrichment, single-cell analysis, and therapeutic responses of ovarian cancer were conducted using the data from public databases. …”
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Technology-Enabled Workplace Learning Through Rethinking Electronic Health Records to Support Performance Feedback: Protocol for a Mixed Methods Study by Anna Janssen, Mia Nazir

“…It will also develop a conceptual design for EHRs that supports health care professional learning, which could be leveraged by developers of these technologies in future implementations. …”
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Strategies and tools to learn from work that goes well within healthcare patient safety practices: a mixed methods systematic review by Gørill Birkeli, Anne Karin Lindahl, Åse Marit Hammersbøen, Ellen Catharina Tveter Deilkås, Randi Ballangrud

“…Abstract Background Safety-II is a new approach to patient safety that is characterised by learning from work that goes well, including learning from success and work-as-done. …”
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Molecular features and diagnostic modeling of synovium- and IPFP-derived OA macrophages in the inflammatory microenvironment via scRNA-seq and machine learning by Chao Lin, Yue Wan, Yong Xu, Qingsong Zou, Xiaoxiao Li

“…This study investigates the functions of OA-macrophages and their clinical significance. …”
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Machine Learning-Driven Identification of Exosome-Related Genes in Head and Neck Squamous Cell Carcinoma for Prognostic Evaluation and Drug Response Prediction by Hua Cai, Liuqing Zhou, Yao Hu, Tao Zhou

“…A predictive model was produced by using machine learning algorithms (LASSO regression, SVM, and random forest) to find disease-specific feature genes. …”
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Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images by Fadwa Alrowais, Mohammed Alqahtani, Jahangir Khan, Achraf Ben Miled, Da’ad Albalawneh, Abdulwhab Alkharashi, Samah Al Zanin, Radwa Marzouk

“…This paper proposes an enhancing neurological disease diagnostics fusion of transfer learning for acute brain stroke prediction using facial images (ENDDFTL-ABSPFI) method. …”
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Identification and Validation of Circadian Rhythm‐Related Genes Involved in Intervertebral Disc Degeneration and Analysis of Immune Cell Infiltration via Machine Learning by Yongbo Zhang, Liuyang Chen, Sheng Yang, Rui Dai, Hua Sun, Liang Zhang

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Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents by Liduo Yin, Morgen VanderGiessen, Morgen VanderGiessen, Vinoth Kumar, Benjamin Conacher, Po-Chien Haku Chao, Michelle Theus, Erik Johnson, Kylene Kehn-Hall, Kylene Kehn-Hall, Xiaowei Wu, Hehuang Xie

“…Gene ontology and pathway analyses further identified neuropathologic features with specific gene product attributes and functions, shedding light on the fundamental biology of these neurological disorders. …”
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