Objectives To review the effectiveness, safety, and determinants of cataract surgeries in eyes treated for retinoblastoma (RB) in the past at a tertiary eye hospital in central Saudi Arabia. Methods This was a retrospective cohort study. We included RB cases from 1983 to 2025 in Saudi Arabia that underwent cataract surgery and were monitored for visual gain, globe integrity, and complications. We assessed associations between demographics, RB treatment details, lens, surgical steps, complications, and visual gain. Results The cohort comprised 74 eyes of 70 patients. Grade D Rb was in 43 (58.1%) eyes. Rb treatment included cryotherapy (44.6%), systemic chemotherapy (81.5%), and transpupillary thermotherapy (23%). Posterior subcapsular lens opacity was in 60% of eyes. The median intervals from Rb treatment to cataract detection and cataract detection to cataract surgery were 38.5 and 25 months, respectively. Cataract was diagnosed in 50% of eyes 50 months after Rb treatment. Different Rb treatment modalities varied in the time interval between Rb treatment and cataract diagnosis. An intraocular lens was implanted in 66 eyes (89.2%). The E1 rate at a median follow-up of 111 months was 35% (95% CI 23.6–47.4). Severe visual impairment was reduced from 54.1% to 37.9%. The complication rate was 50%. [Posterior capsular opacification (21.6%) and corneal scar (18.9%). E1 was associated with Phacoemulsification + IOL (P = 0.02), complications (P = 0.003), recurrence of tumor (P = 0.04), and younger age (P = 0.04). Conclusions RB treatment-induced cataract, if operated on, improved vision in more than one-third of eyes, and these children could have the potential for low-vision rehabilitation nine years after cataract surgery. These children need lifelong monitoring for complications and RB recurrence.
Background: Artificial intelligence has revolutionized the field of dentistry. This systematic review evaluated the clinical performance of artificial intelligence (AI)-assisted cephalometric tracing compared with manual or expert digital tracing. Methods: We searched the PubMed/MEDLINE, Scopus, and Web of Science databases from inception to 20 May 2026. Studies directly comparing AI-assisted tracing with manual or expert digital tracing of the same lateral cephalograms and reporting at least one clinically relevant cephalometric outcome were included in the review. Two authors independently screened records. Risk of bias was assessed using an adapted QUADAS-2 framework. A quantitative meta-analysis was planned but not performed. Findings were synthesised narratively by measurement, platform, and workflow, with reporting informed by the Synthesis Without Meta-analysis (SWiM) guideline. Results: Twenty studies were included. Direct AI-versus-reference ICCs were reported in more than three studies, but their formulations and reference procedures varied. Small average differences sometimes coexisted with wide individual limits of agreement. The numerical evidence did not establish a consistent ranking of SNA, SNB, and ANB. Conclusions: AI-assisted tracing may be useful as a clinician-verified adjunct, but its performance depends on the measurement, platform, and workflow. The evidence does not establish uniform clinical interchangeability with manual or expert digital tracing.
Background: Patient trust may determine whether artificial intelligence (AI) is accepted in dental care, particularly when AI contributes to diagnosis or treatment planning. We aimed to synthesize patient-reported trust, confidence, acceptance, and preferences regarding dental AI and to identify factors associated with these outcomes. Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched from database inception to 2 June 2026. Two reviewers independently screened records and extracted data. Risk of bias was assessed with the revised JBI tool for analytical cross-sectional studies. Because outcomes and metrics were heterogeneous, findings were synthesized narratively. The review was registered in PROSPERO (CRD420261439049). Results: The final synthesis included eight studies comprising 3171 participants from five countries. Attitudes were generally favourable when AI supported rather than replaced dentists. In one study, 47.7% accepted AI-assisted interpretation of diagnostic data, whereas 10.8% trusted an entirely AI-generated diagnosis. Another study found that 84.9% supported AI analysis of dental images followed by dentist review. A fixed-order vignette study improved lesion recognition but did not significantly change trust in the dentist. Interpretation was limited by non-probability sampling, heterogeneous and often non-validated measures, incomplete control of confounding, and reporting inconsistencies. Conclusions: Available evidence suggests conditional acceptance of dental AI, particularly when dentists retain oversight. Confidence in the prevalence estimates of trust and acceptance is low; these findings do not establish that AI improves trust. More representative studies using validated, conceptually distinct outcome measures are required.
Abstract Background Forensic odontology contributes to human identification, age estimation, and medico-legal documentation, yet dental personnel’s preparedness in many regions is unclear. This study assessed the knowledge, attitudes, and practices (KAP) regarding forensic odontology and dental record-keeping among dental students, interns, and practitioners in the Aseer region, Saudi Arabia, and examined the associations among the three domains. Methods A cross-sectional survey was conducted among 73 dental personnel across multiple institutions in Abha and Khamis Mushait. Participants were recruited by convenience sampling; the study was designed as a preliminary, single-region assessment. A structured questionnaire assessed knowledge (six single-best-answer items), attitudes (four pro-field Likert items), and record-keeping practices (three adequacy items). Knowledge was categorised using Bloom’s cut-offs. Non-parametric tests (Mann–Whitney, Kruskal–Wallis) and Spearman correlations were used; internal consistency was assessed by Cronbach’s α. Results Overall knowledge was low (mean 40.6%; 50.7% poor, 9.6% good), with the weakest performance on record-retention standards (19.2% correct), cheiloscopy (23.3%), and adult age estimation (23.3%). Attitudes were consistently favourable (mean 14.8/20; α = 0.93) and did not differ across subgroups. Record-keeping practices were moderate (mean 5.7/9); only 19.2% reported appropriate record retention, and 43.8% had encountered a request for dental records, which were incomplete or unavailable in 17.8% of the sample. Knowledge correlated positively with attitudes (ρ = 0.45, p = 0.0004) and practices (ρ = 0.26, p = 0.042), whereas attitudes and practices were unrelated (ρ = 0.13, p = 0.33). Almost half of respondents (46.6%) reported no undergraduate forensic-odontology training. Conclusions In this preliminary single-region study, dental personnel in the Aseer region held favourable attitudes but demonstrate limited forensic-odontology knowledge and inconsistent record-keeping, with knowledge—not attitude—linked to practice. Integrating forensic odontology into dental curricula and continuing education, alongside standardised record-keeping systems, is needed to strengthen regional medico-legal and disaster-victim-identification preparedness.
BACKGROUND AND OBJECTIVE:Regardless of patient characteristics, current CT imaging techniques usually use standardised radiation settings, which could lead to needless radiation exposure, especially in smaller or lighter patients. Based on unique patient characteristics, this one-size-fits-all method falls short of optimizing the crucial balance between radiation dose and diagnostic image quality. The clinical problem addressed is the lack of evidence-based, patient-specific CT protocols that consider body habitus and sex-related anatomical differences to minimize radiation exposure while preserving diagnostic accuracy. This study aimed to quantify the relationships between radiation dose, as assessed by dose-length product (DLP), patient body weight, and sex across different CT scanner models, to develop individualised protocol recommendations for optimal radiation dose management. METHODS:A retrospective analysis was conducted on CT scan data of 900 adult patients who underwent head, chest, or abdomen-pelvis CT examinations across 10 different multi-detector CT scanners. The correlation between dose-length product (DLP) and body weight was assessed using Pearson's correlation coefficient. Mean dose-length product values were compared between males and females using an independent-samples t-test and across CT scanner models using a one-way ANOVA. Linear regression analysis was performed to establish predictive relationships between DLP and patient characteristics, with facility-specific correction factors developed for dose standardisation. Sex-specific regression models were created, and facility-specific correction factors were calculated to standardize dose comparisons across patient populations. RESULTS:We found a strong positive correlation between DLP and patient body weight (r = 0.61, p < 0.001), and this correlation varied across CT scanner models (r = 0.53-0.71). Mean dose-length product was markedly higher in male patients than in females (p = 0.002). A range of DLP differences was noted for the same examination type across multiple CT scanners (up to 46% difference in mean DLP). In some scanners, dose-length product values exceeded the recommended diagnostic reference levels. Mean DLP values were 758 ± 292, 524 ± 232, and 893 ± 419 mGy cm for head, chest, and abdomen-pelvis CT, respectively. Corresponding mean effective doses were 1.68 ± 0.58, 7.54 ± 2.98, and 14.05 ± 6.01 mSv. Sex-specific regression equations were established: Male dose-length product = -1134.33 + 27.13 × Weight; Female dose-length product = -1166.33 + 27.24 × Weight (both p < 0.001). Weight-stratified analysis showed DLP variations of 15-20% across weight categories (45-60 kg, 60-75 kg, 75-90 kg). After weight normalization, sex-adjusted DLP values showed 8.2% higher doses in males (p = 0.002). CONCLUSION:Major differences are found between men and women. This study showed a strong positive correlation between body weight and dose-length product, which indicates that patient characteristics have a major influence on radiation exposure during CT scans. The urgent need for dosage standardization is demonstrated by the significant discrepancies amongst scanners as well as the fact that 26% exceed diagnostic reference limits (DRLs). For the development of weight and sex-adjusted CT techniques, these quantitative correlations offer evidence-based suggestions. Such an attempt may allow to reduce 10-20% dose reductions in individuals having lower body weights without compromising diagnostic accuracy.
