{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T09:35:58Z","timestamp":1793352958879,"version":"4.5.0"},"reference-count":86,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>The accuracy reported for detecting apical periodontitis using artificial intelligence (AI) is generally interpreted as accuracy against the disease; however, this is usually not the case. Compared with histopathology, single-view periapical radiography detects 16 to 27% of confirmed lesions and 38% with parallax, with a pooled digital sensitivity of 61.0% and a negative predictive value of 41.6%. Two blinded specialists reading 1717 radiographs disagreed on 22% of teeth. Detection depended on lesion size and site: simulated cancellous-bone lesions were invisible below 1 mm in incisors and 3 mm in molars. Of the 44 primary studies identified, 34 (77%) used a label that was a reading of the index image or was unascertainable; eight (18%) used an independent reference\u2014six (14%) suited to contemporaneous detection and two to later treatment outcomes. No cone-beam computed tomography (CBCT) input study had a reference independent of its volume. One commercial platform reported 92.3% sensitivity against same-image consensus and 47.9% against blinded CBCT. The reported accuracy is thus in agreement with a noisy rater; recognising this changes what meta-analyses pool. A model that reproduces human labels would have a lower sensitivity against disease than reported; for real-world models, the direction depends on error dependence that the study design cannot identify.<\/jats:p>","DOI":"10.3390\/diagnostics16193225","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T08:06:47Z","timestamp":1791274007000},"page":"3225","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reference Standards in Artificial Intelligence Detection of Apical Periodontitis: A Narrative Review of What Reported Accuracy Measures"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4179-0726","authenticated-orcid":false,"given":"Omar R.","family":"Alkhattab","sequence":"first","affiliation":[{"name":"Department of Endodontics, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7854-4357","authenticated-orcid":false,"given":"Abdullah","family":"Bokhary","sequence":"additional","affiliation":[{"name":"Department of Dental Public Health, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1111\/iej.12650","article-title":"Diagnostic accuracy of periapical radiography and cone beam computed tomography in detecting apical periodontitis using histopathological findings as a reference standard","volume":"50","author":"Kanagasingam","year":"2017","journal-title":"Int. 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