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—six (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.

Background: Apical periodontitis (AP) is a common inflammatory condition of the periapical tissues, most often associated with persistent endodontic infection. Conventional two-dimensional radiography may underestimate AP because of anatomical superimposition and limited sensitivity. Cone-beam computed tomography (CBCT) allows three-dimensional visualization of periapical structures and has been increasingly used in epidemiological research. Objective: This study aimed to evaluate the prevalence and distribution of apical periodontitis, with particular emphasis on apical periodontitis associated with root canal-treated teeth (AP-RCT), in a Saudi subpopulation using CBCT imaging. Methods: This retrospective cross-sectional study analyzed CBCT scans of Saudi patients obtained for routine diagnostic purposes between 2017 and 2021. Apical periodontitis was identified using standardized radiographic criteria requiring the presence of periapical radiolucency in more than one imaging plane. Demographic and clinical variables were recorded. Descriptive statistics were used to estimate prevalence. Associations between demographic factors and AP-RCT counts were evaluated using multivariable negative binomial regression. Regional tooth distribution was analyzed using generalized estimating equation models accounting for within-participant clustering. Results: A total of 320 CBCT scans were analyzed. Apical periodontitis was detected in 231 participants (72.2%) and in 667 teeth (8.3% of examined teeth). Of the affected teeth, 457 (68.5%) were associated with root canal treatment. The mean number of AP-RCT per participant was 1.36 ± 1.81 (median: 1; IQR: 0–2). Multivariable analysis identified age as the only significant predictor of AP-RCT. Compared with individuals aged 21–30 years, higher AP-RCT rates were observed in the 31–40-year and 41–50-year age groups, while participants ≤20 years showed lower rates. Tooth-level analysis demonstrated higher AP-RCT prevalence in maxillary premolars, maxillary molars, and mandibular molars, whereas mandibular anterior teeth showed the lowest prevalence. Conclusions: Apical periodontitis, particularly AP-RCT, was frequently observed in this Saudi subpopulation when assessed using CBCT. Age and tooth location were the primary determinants of disease distribution. These findings provide population-level epidemiological data on the prevalence and anatomical distribution of apical periodontitis in root canal-treated teeth. Clinical Significance: CBCT-based epidemiological assessment enables detailed evaluation of the distribution of apical periodontitis in dentate populations and may assist in characterizing disease patterns in anatomically complex regions, without implying comparative diagnostic accuracy or treatment outcome assessment.

Background/Objectives: Successful endodontic treatment relies on a precise understanding of root canal morphology and effective disinfection of the entire canal system. Anatomical variations, such as additional canals, canal merging, and intercanal communications, may affect clinical outcomes. This retrospective CBCT study evaluated the root and canal morphology of mandibular first molars in a Saudi population, with specific focus on the prevalence and configuration patterns of middle mesial canals (MMCs) and isthmuses in the mesial root. Methods: A total of 552 CBCT scans taken between 2017 and 2021 were screened. After applying strict eligibility criteria, 167 scans from Saudi patients containing fully erupted mandibular first molars with completely formed apices were included, yielding 322 teeth. Teeth with prior root canal treatment or distorted scans were excluded. Root canal configurations were classified according to Weine’s system. The presence of MMCs, isthmuses, intercanal distance, and bilateral symmetry was recorded. Descriptive and comparative analyses were performed using Fisher’s exact test, chi-square test, Kruskal–Wallis test, and McNemar’s test. Results: Most mandibular first molars (75.5%) had two roots, whereas 24.5% exhibited two distinct distal roots. Mesial roots predominantly demonstrated Weine type II (50.9%) and type III (48.1%) configurations, while distal roots were mainly type I (69.3%). MMCs were rare (0.98%). Isthmuses were highly prevalent (82%) and occurred significantly more often in type III mesial roots (72.3%) compared with type II (1.2%) (p < 0.0001). Bilateral symmetry was substantial in both mesial and distal roots (86.8% and 88.4%, respectively). Conclusions: Middle mesial canals were uncommon, whereas isthmuses were frequently observed in the mesial roots of mandibular first molars among Saudi individuals. The high prevalence of isthmuses underscores the need to anticipate intercanal communications during endodontic treatment. CBCT evaluation enhances detection of such complex anatomical features and supports improved diagnostic accuracy and treatment planning.