{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T07:18:05Z","timestamp":1790061485879,"version":"4.0.1"},"reference-count":35,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Dentistry"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To provide an automated risk assessment for lingual plate perforation (LPP) and mandibular canal injury (MCI) in dental implants. Also, to reduce interoperator variability in risk classification and eliminate manual measurements during implant planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A dataset of 896 CBCT\u2010implant records was used for training our model (DentaRisk\u2010Net). CBCT images were processed using ResNet\u201018 encoders for each implant site view. Patient\u2010specific metadata was processed through a fully connected network. These modalities were fused using a gated fusion mechanism (GFM). Deep metric learning (DML) was employed to enhance distinguish of risk classes. The model was trained on two risk tasks: bone plate (BP) and mandibular canal (MC) tasks. Classification and regression metrics (accuracy, precision, recall,\n                      <jats:italic>F<\/jats:italic>\n                      1\u2010score,\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      , and ablation studies) were used to evaluate performance. True assessment rate (TAR) was used to assess the model\u2019s agreement with human annotators. Grad\u2010CAM visualizations were conducted for qualitative evaluation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The model achieved an accuracy exceeding 0.87 in detecting risk in implant sites, with precision and recall above 0.84. The GFM increased\n                      <jats:italic>F<\/jats:italic>\n                      1\u2010scores by 3%\u20135% and\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      by 5%\u20138% for both risk tasks. The model achieved high performance for the Safe and Risk classes, whereas the Caution class was comparatively lower in BP and MC. TAR \u22641 values demonstrated acceptable alignment with human (BP:\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.63; MC:\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.72). Grad\u2010CAM visualizations confirmed the model\u2019s ability for feature localization.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By integrating imaging and metadata through DML and GFM, the model attempted to reduce interoperator variability in decision\u2010making and enhance risk\u2010classified assessment in the treatment planning process.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/ijod\/5599213","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T14:49:45Z","timestamp":1772981385000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Metric Learning and Multimodal Gated Fusion Framework for AI\u2010Driven Risk Assessment of Lingual Plate Perforation and Mandibular Canal Injury in Posterior Mandible Implants"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9741-8808","authenticated-orcid":false,"given":"Khulood Ali","family":"Al-Taezi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4686-6307","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulrahman","family":"Al-Badwi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed Ali","family":"Al-taezi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Al-Habib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0286-004X","authenticated-orcid":false,"given":"Chunbo","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1563\/aaid-joi-D-18-00227"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdent.2013.12.014"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12903-022-02696-z"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/ID.0b013e3181effa92"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-3913(05)80154-4"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.adaj.2015.04.027"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jds.2020.10.013"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40729-022-00441-3"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108635"},{"key":"e_1_2_12_10_2","doi-asserted-by":"crossref","unstructured":"Mohammad-RahimiH. 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