Showing 1,841 - 1,860 results of 5,583 for search '"Radiology"', query time: 0.09s Refine Results
  1. 1841

    Evaluation of Radiomorphometric Indices of Panoramic Images of Mandible to Estimate Gender and Approximate Age by Seyed Hosein Razavi, Ali Drafshi, Fatemeh Ezoddini, Khalil Sarikhani, Mohammad Khodabakhsh

    Published 2024-11-01
    “…This study aimed to evaluate radiomorphometric indices of mandibular bone on panoramic radiographs for estimating gender and approximate age.Methods and Materials: In this descriptive-cross-sectional study a total 180 panoramic radiographs were retrieved from the Oral and Maxillofacial Radiology Department of Yazd Dental School. Seven different indices were measured: upper ramus breadth (URB), lower ramus breadth (LRB), Condylar ramus height (CH), Projective ramus height (PRH), Coronoid ramus height (CRH), the Gonial angle (GA) and Antegonial notch (AN). …”
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    Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review by M. A. D. Buser, J. K. van derRest, M. H. W. A. Wijnen, R. R. deKrijger, A. F. W. van derSteeg, M. M. van denHeuvel‐Eibrink, M. Reismann, S. Veldhoen, L. Pio, M. Markel

    Published 2025-01-01
    “…ABSTRACT Background Medical images play an important role in diagnosis and treatment of pediatric solid tumors. The field of radiology, pathology, and other image‐based diagnostics are getting increasingly important and advanced. …”
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  8. 1848

    Rosai-Dorfman Disease Presenting With FDG-Avid Breast Masses and Axillary Lymph Nodes on PET-CT in a Patient With Recent Diagnosis of Endometrial Carcinoma: A Diagnostic Dilemma by Jennifer Kudja-Rennick, Pai Raghav, Cedric Pluguez-Turull, Katherine Drews-Elger, Cristina Hoyos

    Published 2025-01-01
    “…This case highlights the unique characteristics of RDD with an atypical clinical presentation suspicious for breast cancer both clinically and radiologically.…”
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  13. 1853

    Panoramic radiographic features for machine learning based detection of mandibular third molar root and inferior alveolar canal contact by A. Canberk Ulusoy, Tuğçe Toprak, M. Alper Selver, Pelin Güneri, Betül İlhan

    Published 2025-02-01
    “…Abstract This study uses machine learning (ML) to elucidate the contact relationship between the mandibular third molar (M3M) and the inferior alveolar canal (IAC), leading to three major contributions; (1) The first publicly accessible PR image dataset with semantic annotations for 1,478 IACs and M3Ms from 1,010 patients is introduced, which includes challenging cases, such as false positive contacts, with CBCT images as the gold standard, (2) Established radiological indicators for M3M-IAC contact were extracted as features using digital image processing, and these features were used as inputs for various ML methods. …”
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