André Wescley Oliveira de Aguiar

This study aimed to compare an annotation‐free deep learning (DL) model with human evaluators for detecting periapical bone rarefactions (PBRs) on panoramic radiographs (PRs) and to assess the clinical acceptability of AI‐supported dental imaging. A mixed‐methods diagnostic validation was conducted using 238 PRs, equally distributed between images with ( n = 119) and without ( n = 119) PBRs. Model performance was compared with that of four calibrated dentists: two oral and maxillofacial radiologists and two endodontists. The reference standard was based on internationally accepted radiographic criteria applied by calibrated researchers under expert supervision, with final classifications verified by the principal investigator. Diagnostic performance was assessed using standard diagnostic metrics, chance‐corrected agreement statistics and receiver operating characteristic analysis, while clinical acceptability was explored through questionnaires and thematically analysed semi‐structured interviews. At the sensitivity‐oriented decision threshold of 0.27, the DL model achieved a sensitivity of 0.924, a specificity of 0.109, an accuracy of 0.517, a precision of 0.509 and an F 1‐score of 0.657. Threshold‐independent binary discrimination was moderate (ROC‐AUC = 0.726). Human evaluators showed more balanced profiles, with accuracy ranging from 0.655 to 0.752 and F 1‐scores from 0.654 to 0.779. Professional perception was generally favourable, although responses varied among evaluators. The annotation‐free DL model showed high sensitivity and moderate discrimination for PBR detection. Although low specificity limits autonomous use, the findings support annotation‐free learning as a potentially scalable strategy, warranting further training with larger and more heterogeneous datasets.

Background: Artificial intelligence (AI) applied to magnetic resonance imaging (MRI) may improve detection of cervical lymph node metastases in oral squamous cell carcinoma (OSCC) but is heterogeneous. Methods: A systematic review identified observational studies (from 2000) evaluating AI‐based MRI in adults with histopathologically confirmed OSCC. Risk of bias was assessed with QUADAS‐AI. Diagnostic performance was synthesized using a hierarchical bivariate model. Publication bias and certainty of evidence were assessed using Deeks' test and GRADE. Results: Twelve studies were included; seven datasets (548 participants) were meta‐analyzed. Pooled sensitivity was 0.72 (95% CI: 0.62–0.80) and specificity 0.79 (95% CI: 0.73–0.83), with AUC 0.82 and diagnostic odds ratio 9.42. Heterogeneity is mainly related to threshold effects. No significant publication bias was detected ( p = 0.536). Evidence certainty was low. Conclusions: AI‐assisted MRI shows moderate diagnostic performance. Multicenter validation is required before clinical implementation. Clinical Relevance: AI‐supported MRI may serve as an adjunctive tool to improve preoperative risk stratification of cervical lymph node metastasis in OSCC.

Artificial intelligence (AI) models for diagnostic imaging face reproducibility challenges due to inconsistent reporting. Existing guidelines also lack specificity for imaging-based AI diagnostics, particularly regarding clinical usability and technical transparency. To address these gaps, the Completeness, Learnability, Applicability, Interpretability, Reproducibility, and Evaluation (CLAIRE) framework was developed as a practical reporting aid by a multidisciplinary team of clinicians and AI experts. CLAIRE was retrospectively validated on a subset of 10 imaging studies selected by theoretical saturation in medical and dental imaging. Internal validation demonstrated high reliability, with inter-rater agreement improving from Cohen’s κ 0.286 to 0.987 (p < 0.01) after calibration, alongside a mean intra-rater reliability of 0.997 after a six-month washout period. This process yielded a 15-item structured checklist for standardising AI reporting, supported by an objective scoring system for quality categorisation and an editorial reference guide to facilitate systematic appraisal by reviewers and editors. CLAIRE aims to enhance clinician accessibility through plain-language technical summaries and assessments of real-world applicability. This proposal provides a unified and practical structure that improves reporting consistency, supports systematic assessment, and strengthens both reproducibility and clinical translation of AI-based imaging models.

Objectives: To evaluate deep learning (DL)-based models for detecting periapical bone rarefaction (PBRs) in panoramic radiographs (PRs), analysing their feasibility and performance in dental practice. Methods: A search was conducted across seven databases and partial grey literature up to November 15, 2024, using Medical Subject Headings and entry terms related to DL, PBRs, and PRs. Studies assessing DL-based models for detecting and classifying PBRs in conventional PRs were included, while those using non-PR imaging or focusing solely on non-PBR lesions were excluded. Two independent reviewers performed screening, data extraction, and quality assessment using the Quality Assessment of Diagnostic Accuracy Studies-2 tool, with conflicts resolved by a third reviewer. Results: Twelve studies met the inclusion criteria, mostly from Asia (58.3%). The risk of bias was moderate in 10 studies (83.3%) and high in 2 (16.7%). DL models showed moderate to high performance in PBR detection (sensitivity: 26%-100%; specificity: 51%-100%), with U-NET and YOLO being the most used algorithms. Only one study (8.3%) distinguished Periapical Granuloma from Periapical Cysts, revealing a classification gap. Key challenges included limited generalization due to small datasets, anatomical superimpositions in PRs, and variability in reported metrics, compromising models comparison. Conclusion: This review underscores that DL-based has the potential to become a valuable tool in dental image diagnostics, but it cannot yet be considered a definitive practice. Multicentre collaboration is needed to diversify data and democratize those tools. Standardized performance reporting is critical for fair comparability between different models. Advances in knowledge: This study represents the first critical synthesis on this theme, examining a group of lesions with complex manifestations that have been neglected in comparable technological development studies, where research focus has usually been limited to radicular cysts. We identified gaps in classification tasks, insufficient use of ethnically diverse and heterogeneous datasets, and the need for multicentric studies. The variability in data reporting prevents transparent comparisons, even precluding our planned meta-analysis. Consequently, we emphasize the necessity for standardized reporting protocols similar to PRISMA for systematic reviews or STARD for diagnostic or prognostic studies, particularly since accuracy metrics remain inadequately documented while critically important.