Background: The armed conflict between the Sudanese Armed Forces (SAF) and the Rapid Support Forces (RSF), which began on April 15, 2023, has plunged Sudan into a severe humanitarian crisis, deeply affecting healthcare delivery and medical education. This study investigated the current state of dental practice in Sudan during the ongoing conflict, identified challenges faced by dental professionals, and explored coping strategies adopted to sustain dental care delivery. Methods: A descriptive, cross-sectional, institution-based study was conducted across conflict-affected regions of Sudan. The study population comprised dentists working in hospitals and private clinics nationwide. A sample size of 312 participants, calculated at a 95% confidence level, was selected randomly. Data were collected using a structured questionnaire and analyzed with SPSS version 25. Descriptive statistics summarized the frequencies, means, and percentages, while chi-square tests assessed associations, with P < 0.05 considered statistically significant. Results: Among 312 dental professionals, 38.5% reported complete cessation of dental services and 50.0% reported substantial reductions. Frequent shortages of medications and anesthetics affected 67.3%, while 55.8% reported markedly reduced patient attendance. Major nonclinical challenges included income loss (36.5%) and psychological stress (23.1%). Independent sourcing of dental supplies was reported by 59.6%. Priority support needs were financial assistance (32.7%), training in conflictbased healthcare delivery (30.8%), and improved supply chains (28.8%). Conclusion: The findings revealed the impact of conflict on dental practice and the urgent need for interventions to restore and strengthen oral healthcare services. Efforts should focus on rebuilding healthcare infrastructure, ensuring workforce safety, improving service quality, and fostering resilience among dental professionals. Strengthening these areas is essential for achieving a sustainable and equitable dental healthcare system in post-conflict Sudan.

Background: Dental caries detection using intraoral radiographs is essential for early diagnosis but may be affected by observer variability. Artificial intelligence (AI)-based systems have emerged as potential tools to improve diagnostic consistency. This study evaluated the diagnostic accuracy of a deep learning-based AI system (Better Diagnostics Caries Assist (BDCA) Version 1.0) for detecting dental caries on bitewing (BW) and intraoral periapical (IOPA) radiographs, and examined its performance across demographic, technical, and lesion-based subgroups. Methods: A retrospective validation study was conducted using anonymized digital BW and IOPA radiographs with expert-defined ground truth at the tooth-surface level. The AI software independently analyzed each image to identify carious lesions. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated with 95% confidence intervals. Generalized estimating equations were applied to adjust for the clustering of multiple surfaces per image. Subgroup analyses were performed by age, sex, digital sensor type, and lesion category. Results: The AI system demonstrated high diagnostic accuracy for both modalities. For BW radiographs, sensitivity was 0.892 and specificity was 0.995, while for IOPA radiographs sensitivity was 0.882 and specificity was 0.991. NPVs exceeded 0.99 for both modalities. Across age groups, BW sensitivity ranged from 0.881 to 0.901 and IOPA sensitivity from 0.854 to 0.906, with consistently high specificity (>0.989). Sex-based differences were minimal. Sensor-wise analysis showed sensitivity ranging from 0.833 to 0.933 for BW and 0.828 to 0.933 for IOPA, while specificity remained above 0.984 for all sensors. Detection performance was comparable for primary (sensitivity 0.883) and secondary caries (0.879), although PPV was slightly lower for secondary lesions. The lower AUC indicated reduced accuracy in lesion identification in the absence of BDCA v 1.0, the difference between BDCA v1.0 and Ground truth was 0.042 at 95% CI 0.030 to 0.055, and the difference was also statistically significant with (p ≤ 0.001). Conclusions: The evaluated AI system demonstrated excellent and consistent performance for detecting dental caries on both BW and IOPA radiographs across demographic groups, sensor technologies, and lesion types, supporting its potential role as a reliable decision support tool in dental radiographic interpretation.