{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:15:23Z","timestamp":1790194523272,"version":"4.1.0"},"reference-count":59,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T00:00:00Z","timestamp":1761436800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T00:00:00Z","timestamp":1761436800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100019783","name":"National Agency for Research and Development","doi-asserted-by":"publisher","award":["11230101"],"award-info":[{"award-number":["11230101"]}],"id":[{"id":"10.13039\/100019783","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int Endodontic J"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To evaluate the performance of an artificial intelligence (AI) system in diagnosing pulp conditions in adult patients, using a modified Wolters diagnostic classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>\n                      A cross\u2010sectional study was conducted. Data from 200 teeth of 200 patients were collected. A clinical evaluation was performed, and a dataset containing 21 diagnostic attributes was compiled. A modified Wolters classification was used for more objective and reproducible diagnostic criteria. AI models (Decision Tree (DT), Support Vector Machine (SVM), Multi\u2010Layer Perceptron (MLP), and XGBoost) were trained using a\n                      <jats:italic>K<\/jats:italic>\n                      \u2010fold cross\u2010validation approach. Hyperparameters were optimised, and model performance was assessed through precision, recall, F1\u2010score and area under the receiver operating curves (AUCs). The final metrics were calculated over several iterations. To estimate model variability, bootstrap resampling was applied. Additionally, attribute importance was analysed using the permutation method.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The diagnoses were distributed in normal pulp (NP) (21.5%), initial pulpitis (IP) (13%), mild pulpitis (MIP) (21.5%), moderate pulpitis (MP) (25%) and severe pulpitis (SP) (19%). During validation, the models that achieved the highest performance were XGBoost (mean F1\u2010score (0.85)) and SVM (mean F1\u2010score (0.85)). Regarding ROC curve analysis, both models demonstrated AUC values exceeding 0.93 for all diagnostic classes. SVM reached perfect AUCs for SP and near\u2010perfect values for NP (0.98), while XGBoost achieved a perfect AUC for NP and values above 0.90 for all other classes. Bootstrap analysis confirmed that XGBoost has the lowest variability and the best average values for precision (0.81), recall (0.81) and F1\u2010score (0.80). On the other hand, pain to cold stimulus emerged as the most relevant attribute for diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The AI\u2010based system demonstrated significant potential for achieving accurate pulp diagnoses, contributing to more objective diagnostic decisions. Pain intensity in response to thermal stimuli and spontaneous pain was a key feature that allowed correct classification, highlighting its relevance in the diagnostic process.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/iej.70055","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T04:37:46Z","timestamp":1761539866000},"page":"271-287","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence Models for Diagnosing Pulpitis in Adults Using a Modified Wolters Classification: A Diagnostic Accuracy Study"],"prefix":"10.1111","volume":"59","author":[{"given":"Claudia","family":"Brizuela","sequence":"first","affiliation":[{"name":"Universidad de los Andes  Santiago Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Pablo","family":"Ferrada","sequence":"additional","affiliation":[{"name":"Universidad de los Andes  Santiago Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda Ignacia","family":"Valencia","sequence":"additional","affiliation":[{"name":"Universidad de los Andes  Santiago Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolina","family":"Cabrera","sequence":"additional","affiliation":[{"name":"Universidad de los Andes  Santiago Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Manuel","family":"Saavedra","sequence":"additional","affiliation":[{"name":"Universidad de los Andes  Santiago Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Crist\u00f3bal","family":"Loyola","sequence":"additional","affiliation":[{"name":"Universidad de Chile  Santiago Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,26]]},"reference":[{"issue":"7","key":"e_1_2_11_2_1","article-title":"Artificial Intelligence in Dentistry: Past, Present, and Future","volume":"14","author":"Agrawal P.","year":"2022","journal-title":"Cureus"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9751564"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.52142\/omujecm.38.si.dent.18"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.52037\/eads.2023.0004"},{"key":"e_1_2_11_6_1","unstructured":"American Association of Endodontists. n.d.\u201cAmerican Association of Endodontists\u2013Endodontists: The Root Canal Specialists.\u201dhttps:\/\/www.aae.org\/."},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.joen.2021.06.003"},{"key":"e_1_2_11_8_1","article-title":"Statistics Notes: Bootstrap Resampling Methods","volume":"350","author":"Bland J. 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