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  1. 38381

    Deep Learning for Discrimination of Early Spinal Tuberculosis from Acute Osteoporotic Vertebral Fracture on CT by Liu W, Wang J, Lei Y, Liu P, Han Z, Wang S, Liu B

    Published 2025-01-01
    “…Wenjun Liu,1 Jin Wang,2 Yiting Lei,1 Peng Liu,3 Zhenghan Han,1 Shichu Wang,1 Bo Liu1 1Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People’s Republic of China; 2College of Medical Informatics, Chongqing Medical University, Chongqing, People’s Republic of China; 3Department of Orthopedics, Daping Hospital, Army Medical University, Chongqing, People’s Republic of ChinaCorrespondence: Bo Liu, Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People’s Republic of China, Tel +8613996065698, Email boliu@hospital.cqmu.edu.cnBackground: Early differentiation between spinal tuberculosis (STB) and acute osteoporotic vertebral compression fracture (OVCF) is crucial for determining the appropriate clinical management and treatment pathway, thereby significantly impacting patient outcomes.Objective: To evaluate the efficacy of deep learning (DL) models using reconstructed sagittal CT images in the differentiation of early STB from acute OVCF, with the aim of enhancing diagnostic precision, reducing reliance on MRI and biopsies, and minimizing the risks of misdiagnosis.Methods: Data were collected from 373 patients, with 302 patients recruited from a university-affiliated hospital serving as the training and internal validation sets, and an additional 71 patients from another university-affiliated hospital serving as the external validation set. …”
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    Technological-Based Interventions in Cancer and Factors Associated With the Use of Mobile Digital Wellness and Health Apps Among Cancer Information Seekers: Cross-Sectional Study by Ogochukwu Juliet Ezeigwe, Kenechukwu Obumneme Samuel Nwosu, Oladipo Kunle Afolayan, Akpevwe Amanda Ojaruega, Jovita Echere, Manali Desai, Modupe Olajumoke Onigbogi, Olajumoke Ope Oladoyin, Nnenna Chioma Okoye, Pierre Fwelo

    Published 2025-02-01
    “…In the final adjusted model, participants with household incomes ≥US $50,000 had 49% higher adjusted odds of using these apps than those with incomes <US $50,000 (adjusted odds ratio [aOR]=1.49, 95% CI 1.02-2.14). College graduates and those with higher educational levels were avid users compared to those with a high school diploma or less (aOR=1.87, 95% CI 1.30-2.67). …”
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