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    <title>DSpace Community:</title>
    <link>https://ir.ymlib.yonsei.ac.kr/handle/22282913/169025</link>
    <description />
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        <rdf:li rdf:resource="https://ir.ymlib.yonsei.ac.kr/handle/22282913/211453" />
        <rdf:li rdf:resource="https://ir.ymlib.yonsei.ac.kr/handle/22282913/211534" />
        <rdf:li rdf:resource="https://ir.ymlib.yonsei.ac.kr/handle/22282913/210387" />
        <rdf:li rdf:resource="https://ir.ymlib.yonsei.ac.kr/handle/22282913/211077" />
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    <dc:date>2026-04-18T12:44:26Z</dc:date>
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  <item rdf:about="https://ir.ymlib.yonsei.ac.kr/handle/22282913/211453">
    <title>Immediate dental implants in jaw reconstructions show high accuracy with mean deviations under 2mm: A cohort study</title>
    <link>https://ir.ymlib.yonsei.ac.kr/handle/22282913/211453</link>
    <description>Title: Immediate dental implants in jaw reconstructions show high accuracy with mean deviations under 2mm: A cohort study
Authors: Kim, Hyounmin; Joo, Euntae; Kim, Jinyoung; Kim, Hyung Jun; Nam, Woong; Kim, Dongwook; 김현민
Abstract: The "Jaw in a Day (R)" approach enables simultaneous functional and aesthetic mandibular reconstruction through immediate dental implant placement in osseous free flaps. This retrospective cohort study evaluated the accuracy of immediate dental implant placement during mandibular reconstruction with fibula free flap by measuring deviations between virtual surgical plans and actual implant positions. Ten patients who underwent fibula free flap reconstruction with immediate dental implant placement using virtual surgical planning were analyzed from a single center between April 2023 and January 2024. Thirty-two dental implants were assessed were assessed in 10 patients (mean age 59.0 f 15.3 years, 70 % male). Mean coronal deviation was 1.99 f 0.91 mm, apical deviation was 1.64 f 0.87 mm, and angular deviation was 3.98 f 0.96 degrees. Clinical factors including fibula segment length, number of bone segments, Brown classification, surgical approach, and tumor type did not significantly influence implant accuracy (all p &gt; 0.05). Mixed-effects linear models revealed high intraclass correlation for linear deviations (ICC 55-58 %) but not angular deviation (ICC 0.2 %). Virtual surgical planning combined with the Jaw in a Day (R) procedure demonstrates reliable implant accuracy for single-stage mandibular reconstruction. The provisional "floating prosthesis" compensates for surgical errors and guides precise implant positioning, supporting the clinical viability of immediate dental rehabilitation in mandibular reconstruction.</description>
    <dc:date>2026-05-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://ir.ymlib.yonsei.ac.kr/handle/22282913/211534">
    <title>Clinical acceptability and repeatability of three-dimensional maxillary positioning following orthognathic surgical wafer placement: An in vitro study</title>
    <link>https://ir.ymlib.yonsei.ac.kr/handle/22282913/211534</link>
    <description>Title: Clinical acceptability and repeatability of three-dimensional maxillary positioning following orthognathic surgical wafer placement: An in vitro study
Authors: Choi, Eun-Hack Andrew; Shin, Yunji; Yu, Jae-Hun; Kim, Jeenyoung; Mangal, Utkarsh; Kim, Jun-Young; Choi, Sung-Hwan; 만갈웃커시
Abstract: Objectives: To quantify the three-dimensional (3D) deviation between planned and achieved maxillary positions after transfer using a surgical wafer under in vitro conditions, and assess clinical acceptability and repeatability for each workflow. Methods: A bracketed typodont mounted on a mannequin was used to define three groups-Conventional (alginate impressions/plaster casts/laboratory scanning), Trios (Trios 3 intraoral scanner (IOS)), and Prime (Primescan IOS)-each with 10 technical replicates. For each replicate, virtual surgical planning was performed, a wafer was designed and 3D-printed, and the maxilla was positioned; 3D deviation was quantified as: (1) point-based 3D positional deviation (three landmarks) and (2) matrix-based 3D translational and rotational deviations. Clinical acceptability was defined as the proportion of the 10 replicates per group with 3D deviation within predefined clinical tolerance limits (0.5 mm for positional/translational; 1.0 degrees for rotational). Repeatability was summarized by the repeatability standard deviation (sr) as specified in ISO 5725. Results: The Trios and Prime groups achieved 100% clinical acceptability across all 3D deviations. In the Conventional group, acceptability was 90% for the anterior landmark's positional and translational deviation, with all others achieving 100%. The sr was 0.13-0.20 mm (positional), 0.23 mm (translational), and 0.22 degrees (rotational) for the Conventional group; 0.04-0.13 mm, 0.06 mm, and 0.24 degrees for the Trios group; 0.04-0.10 mm, 0.11 mm, and 0.17 degrees for the Prime group. Conclusion: Under in vitro conditions, the demonstrated clinical acceptability and repeatability provide preliminary evidence supporting the clinical feasibility of a fully digital IOS-based workflow for wafer-mediated maxillary positioning. Clinical significance: An IOS-based workflow can be considered clinically feasible for wafer-mediated maxillary positioning without compromising accuracy, as evaluated in terms of clinical acceptability and repeatability, while potentially eliminating impression-taking and cast fabrication steps.</description>
    <dc:date>2026-04-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://ir.ymlib.yonsei.ac.kr/handle/22282913/210387">
    <title>Application of deep learning in evaluating the anatomical relationship between the mandibular third molar and inferior alveolar nerve: A scoping review.</title>
    <link>https://ir.ymlib.yonsei.ac.kr/handle/22282913/210387</link>
    <description>Title: Application of deep learning in evaluating the anatomical relationship between the mandibular third molar and inferior alveolar nerve: A scoping review.
Authors: Ahn, Suji; Kim, Min-Ji; Kim, Jun-Young; Park, Wonse
Abstract: Background: With advancements in deep learning-based dental imaging analysis, artificial intelligence (AI) models are increasingly being employed to assist in mandibular third molar surgery. However, a comprehensive overview of the clinical utility remains limited. This scoping review aimed to identify and compare deep learning models used in the radiographic evaluation of mandibular third molar surgery, with a focus on AI model types, key performance metrics, imaging modalities, and clinical applicability. Material and Methods: Following the PRISMA-ScR guidelines, a comprehensive search was conducted in the PubMed and Scopus databases for original research articles published between 2015 and 2024. Systematic reviews, editorial articles, and studies with insufficient datasets were excluded. Studies utilising panoramic radiographs and cone-beam computed tomography (CBCT) images for AI-based mandibular third molar analyses were included. The extracted data were charted according to the AI model types, performance metrics (accuracy, sensitivity, and specificity), dataset size and distribution, validation processes, and clinical applicability. Comparative performance tables and heat maps were utilised for visualisation. Results: Of the initial 948 articles, 16 met the inclusion criteria. Various convolutional neural network (CNN)-based models have been developed, with U-Net demonstrating the highest accuracy and clinical utility. Most studies employed panoramic and CBCT images, with U-Net outperforming other models in predicting nerve injury and evaluating extraction difficulty. However, substantial variations in dataset size, validation procedures, and performance metrics were noted, highlighting inconsistencies in model generalisability. Conclusions: Deep learning shows promising potential in the radiographic evaluation of mandibular third molars. To date, most studies have relied on two-dimensional images and focused on detection and segmentation, while predictive modeling and three-dimensional CBCT-based analysis are relatively limited. To enhance clinical utility, larger standardized datasets, transparent multi-expert annotation, task-specific benchmarking, and robust external/multicenter validation are needed. These measures will enable reliable pre-extraction risk prediction and support clinical decision-making.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="https://ir.ymlib.yonsei.ac.kr/handle/22282913/211077">
    <title>Evaluating differentiation grade as a guide for decision-making in elective neck dissection for early-stage oral squamous cell carcinoma: a population-based cohort study</title>
    <link>https://ir.ymlib.yonsei.ac.kr/handle/22282913/211077</link>
    <description>Title: Evaluating differentiation grade as a guide for decision-making in elective neck dissection for early-stage oral squamous cell carcinoma: a population-based cohort study
Authors: Chen, Junxu; Zou, Derong; Zan, Hao; Li, Xiangyu; Kim, Hyung Jun
Abstract: Background: Early-stage oral squamous cell carcinoma (OSCC) frequently poses a risk of occult lymph node metastasis, complicating neck management decisions. Elective neck dissection (END) is a critical strategy for mitigating this risk, though its criteria for selection continue to be debated. This study evaluated whether tumor differentiation can guide END decisions in early-stage OSCC. Methods: Patients with stage I/II OSCC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier and Cox regression analyses assessed cancer-specific survival (CSS) and overall survival (OS), comparing END and no END groups across tumor differentiation grades. Stratified analyses further examined the influence of tumor site and size on survival. Results: Among 10,396 early-stage OSCC patients, END did not improve survival in well-differentiated tumors but significantly improved CSS and OS in moderately and poorly differentiated/undifferentiated tumors. Interaction analyses revealed that tumor site and size significantly modified END&amp;apos;s survival benefit. Stratified analyses highlighted that END benefits patients with moderately or poorly differentiated/ undifferentiated tumors, but provides limited benefit for well-differentiated, small tumors, or those located in the floor of mouth. Conclusions: Tumor differentiation is a critical determinant of END&amp;apos;s survival benefit in early-stage OSCC. END should be considered for patients with moderately or poorly differentiated/undifferentiated tumors but may be unnecessary in well-differentiated cases.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
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