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Objectives A convolutional neural network (CNN) was adapted to automatically detect early-stage nasopharyngeal carcinoma (NPC) and discriminate it from benign hyperplasia on a non-contrast-enhanced MRI sequence for potential use in NPC screening programs. Methods We retrospectively analyzed 412 patients who underwent T2-weighted MRI, 203 of whom had biopsy-proven primary NPC confined to the nasopharynx (stage T1) and 209 had benign hyperplasia without NPC. Thirteen patients were sampled randomly to monitor the training process. We applied the Residual Attention Network architecture, adapted for three-dimensional MR images, and incorporated a slice-attention mechanism, to produce a CNN score of 0–1 for NPC probability. Threefold cross-validation was performed in 399 patients. CNN scores between the NPC and benign hyperplasia groups were compared using Student's t test. Receiver operating characteristic with the area under the curve (AUC) was performed to identify the optimal CNN score threshold. Results In each fold, significant differences were observed in the CNN scores between the NPC and benign hyperplasia groups ( p < .01). The AUCs ranged from 0.95 to 0.97 with no significant differences between the folds ( p = .35 to .92). The combined AUC from all three folds ( n = 399) was 0.96, with an optimal CNN score threshold of > 0.71, producing a sensitivity, specificity, and accuracy of 92.4%, 90.6%, and 91.5%, respectively, for NPC detection. Conclusion Our CNN method applied to T2-weighted MRI could discriminate between malignant and benign tissues in the nasopharynx, suggesting that it as a promising approach for the automated detection of early-stage NPC. Key Points • The convolutional neural network (CNN)–based algorithm could automatically discriminate between malignant and benign diseases using T2-weighted fat-suppressed MR images. • The CNN-based algorithm had an accuracy of 91.5% with an area under the receiver operator characteristic curve of 0.96 for discriminating early-stage T1 nasopharyngeal carcinoma from benign hyperplasia. • The CNN-based algorithm had a sensitivity of 92.4% and specificity of 90.6% for detecting early-stage nasopharyngeal carcinoma.

The aim of this study was to investigate whether melatonin receptor type 1B (MTNR1B) rs10830963 polymorphism interacts with night shift work on the risk of incident stroke. This study included individuals free of stroke at baseline from the UK Biobank. Night-shift work was assessed by the self-reported questions. MTNR1B rs10830963 was directly genotyped (CC, GC, and GG). Incident stroke was ascertained through hospital records and death registries. Cox proportional hazards models were employed to examine the associations of night shift work and MTNR1B rs10830963 with the risk of incident stroke. A total of 242 194 participants were finally included (mean age: 52.95 years; 51.63% women). Over 12-year follow-up, 3287 incident stroke events occurred. Night shift work increased the risk of incident stroke [hazard ratio (HR) 1.13, 95% confidence interval (CI) 1.00-1.28] after adjusting for socio-demographics, and this association attenuated after additional adjustment for lifestyle factors (HR 1.06, 95% CI 0.94-1.20). MTNR1B rs10830963 polymorphism modified the association between night shift work and incident stroke (Pfor interaction =0.010). In the Cox models adjusted for socio-demographics and lifestyle factors, among night-shift workers, minor allele G was associated with a reduced risk of incident stroke (GC versus CC, HR 0.74, 95% CI 0.58-0.95; GG versus CC, HR 0.65, 95% CI 0.40-1.06; P for trend=0.010); while night shift work was associated with a higher stroke risk only among MTNR1B rs10830963 CC carriers (HR 1.23, 95% CI 1.05-1.44) but not GC/GG carriers. These results suggest that MTNR1B rs10830963 may potentially modify the associations between night shift work and incident stroke.

BackgroundIndividuals with type 2 diabetes mellitus (T2DM) are more vulnerable to social disconnection compared with the general population; however, there are few relevant studies investigating this issue.AimsTo investigate whether social isolation or loneliness may be associated with subsequent risk of developing major adverse cardiovascular events, whether these associations vary according to fatal and non-fatal outcomes and how behavioural, psychological and physiological factors mediate these associations.MethodsThis longitudinal analysis included data from 19 360 individuals with T2DM at baseline (2006–2010) from the UK Biobank. Social isolation and loneliness were measured using self-report questionnaires. The study outcomes included the first events of myocardial infarction (MI) or stroke (n=2273) and all-cause (n=2820) or cardiovascular disease-related mortality through linked hospital data or death registries.ResultsOver a median follow-up of 12.4 years (interquartile range (IQR): 11.6–13.3 years), participants who were more socially isolated (most social isolation vs least social isolation) experienced increased risks for all-cause (hazard ratio (HR) : 1.33, 95% confidence interval (CI): 1.19 to 1.47) and cardiovascular disease (HR: 1.36, 95% CI: 1.17 to 1.59) mortality but not first MI or stroke. Loneliness (yes vs no) was associated with a greater risk for a composite of incident MI or stroke (HR: 1.37, 95% CI: 1.19 to 1.57) but not mortality. Social isolation was associated with fatal MI and stroke, whereas loneliness was associated with non-fatal MI and stroke. The significant associations of social isolation and loneliness with outcomes were mainly mediated by behavioural factors (mediating proportion: 17.8%–28.2% and 17.6%–17.8%, respectively).ConclusionsAmong individuals with T2DM, social isolation and loneliness are associated with a greater risk of developing major adverse cardiovascular events, with differences in both risks stratified according to fatal and non-fatal events and underlying mediating factors.

PurposeConvolutional neural networks (CNNs) show potential for delineating cancers on contrast-enhanced MRI (ce-MRI) but there are clinical scenarios in which administration of contrast is not desirable. We investigated performance of the CNN for delineating primary nasopharyngeal carcinoma (NPC) on non-contrast-enhanced images and compared the performance to that on ce-MRI.Materials and methodsWe retrospectively analyzed primary NPC in 195 patients using a well-established CNN, U-Net, for tumor delineation on the non-contrast-enhanced fat-suppressed (fs)-T2W, ce-T1W and ce-fs-T1W images. The CNN-derived delineations were compared to manual delineations to obtain Dice similarity coefficient (DSC) and average surface distance (ASD). The DSC and ASD on fs-T2W were compared to those on ce-MRI. Primary tumor volumes (PTVs) of CNN-derived delineations were compared to that of manual delineations.ResultsThe CNN for NPC delineation on fs-T2W images showed similar DSC (0.71 ± 0.09) and ASD (0.21 ± 0.48 cm) to those on ce-T1W images (0.71 ± 0.09 and 0.17 ± 0.19 cm, respectively) (p > 0.05), and lower DSC but similar ASD to ce-fs-T1W images (0.73 ± 0.09, p < 0.001; and 0.17 ± 0.20 cm, p > 0.05). The CNN overestimated PTVs on all sequences (p < 0.001).ConclusionThe CNN showed promise for NPC delineation on fs-T2W images in cases where it is desirable to avoid contrast agent injection. The CNN overestimated PTVs on all sequences.

Purpose Anatomical imaging criteria for the diagnosis of malignant head and neck nodes may not always be reliable. This study aimed to evaluate the diagnostic value of conventional diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) DWI in discriminating benign and malignant metastatic retropharyngeal nodes (RPNs). Methods IVIM DWI using 14 b -values was performed on RPNs of 30 patients with newly diagnosed metastatic nasopharyngeal carcinoma (NPC) and 30 patients with elevated plasma Epstein-Barr virus (EBV)-DNA without NPC who were part of an EBV-based NPC screening program. Histogram measurements of the two groups were compared for pure diffusion coefficient ( D ), pseudo-diffusion coefficient ( D *), perfusion volume fraction ( f ) and apparent diffusion coefficient ( ADC ) using the Mann-Whitney U test. Area under the curves (AUCs) of significant measurements were calculated from receiver-operating characteristics analysis and compared using the DeLong test. Results Compared with metastatic RPNs, benign RPNs had lower ADC mean (0.73 vs 0.82 × 10 −3  mm 2 /s) and D mean (0.60 vs 0.71 × 10 −3  mm 2 /s) and a higher D * mean (35.21 vs 28.66 × 10 −3  mm 2 /s) (all p  < 0.05). There was no difference in the f measurements between the two groups ( p  = 0.204 to 0.301). D mean achieved the highest AUC of 0.800, but this was not statistically better than the AUCs of the other parameters ( p  = 0.148 to 0.991). Conclusion Benign RPNs in patients with EBV-DNA showed greater restriction of diffusion compared with malignant metastatic RPNs from NPC. IVIM did not show a significant advantage over conventional DWI in discriminating benign and malignant nodes.

Objectives: The aim of this study was to evaluate the performance of GPT-4o in identifying nine common dental conditions on panoramic radiographs, both overall and at specific tooth sites, and to assess whether the use of different tooth numbering systems (FDI and Universal) in prompts would affect its diagnostic accuracy. Methods: Fifty panoramic radiographs exhibiting various common dental conditions including missing teeth, impacted teeth, caries, endodontically treated teeth, teeth with restorations, periapical lesions, periodontal bone loss, tooth fractures, cracks, retained roots, dental implants, osteolytic lesions, and osteosclerosis were included. Each image was evaluated twice by GPT-4o in May 2025, using structured prompts based on either the FDI or Universal tooth numbering system, to identify the presence of these conditions at specific tooth sites or regions. GPT-4o responses were compared to a consensus reference standard established by an oral-maxillofacial radiology team. GPT-4o’s performance was evaluated using balanced accuracy, sensitivity, specificity, and F1 score both at the patient and tooth levels. Results: A total of 100 GPT-4o responses were generated. At the patient level, balanced accuracy ranged from 46.25% to 98.83% (FDI) and 49.75% to 92.86% (Universal), with the highest accuracies for dental implants (92.86–98.83%). F1-scores and sensitivities were highest for implants, missing, and impacted teeth, but zero for caries, periapical lesions, and fractures. Specificity was generally high across conditions. Notable discrepancies were observed between patient- and tooth-level performance, especially for implants and restorations. GPT-4o’s performance was similar between using the two numbering systems. Conclusions: GPT-4o demonstrated superior performance in detecting dental implants and treated or restored teeth but inferior performance for caries, periapical lesions, and fractures. Diagnostic accuracy was higher at the patient level than at the tooth level, with similar performances for both numbering systems. Future studies with larger, more diverse datasets and multiple models are needed.

Purposes To systematically review and perform meta-analysis to evaluate the prognostic value of cervical nodal necrosis (CNN) on the staging computed tomography/magnetic resonance imaging (MRI) of nasopharyngeal carcinoma (NPC) in era of intensity-modulated radiotherapy. Methods Literature search through PubMed, EMBASE, and Cochrane Library was conducted. The hazard ratios (HRs) with 95% confidence intervals (CIs) of CNN for distant metastasis-free survival (DMFS), disease free survival (DFS) and overall survival (OS) were extracted from the eligible studies and meta-analysis was performed to evaluate the pooled HRs with 95%CI. Results Nine studies, which investigated the prognostic values of 6 CNN patterns on MRI were included. Six/9 studies were eligible for meta-analysis, which investigated the CNN presence/absence in any nodal group among 4359 patients. The pooled unadjusted HRs showed that the CNN presence predicted poor DMFS (HR =1.89, 95%CI =1.72-2.08), DFS (HR =1.57, 95%CI =1.08-2.26), and OS (HR =1.87, 95%CI =1.69-2.06). The pooled adjusted HRs also showed the consistent results for DMFS (HR =1.34, 95%CI =1.17-1.54), DFS (HR =1.30, 95%CI =1.08-1.56), and OS (HR =1.61, 95%CI =1.27-2.04). Results shown in the other studies analysing different CNN patterns indicated the high grade of CNN predicted poor outcome, but meta-analysis was unable to perform because of the heterogeneity of the analysed CNN patterns. Conclusion The CNN observed on the staging MRI is a negative factor for NPC outcome, suggesting that the inclusion of CNN is important in the future survival analysis. However, whether and how should CNN be included in the staging system warrant further evaluation.

Abstract ObjectivesNovel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR.Materials and methodsA narrative review was conducted on the literature on AI algorithms in DMFR.ResultsIn the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping.ConclusionsFurther integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future.Clinical relevanceThis review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR.

ObjectivesTo propose and evaluate a convolutional neural network (CNN) algorithm for automatic detection and segmentation of mucosal thickening (MT) and mucosal retention cysts (MRCs) in the maxillary sinus on low-dose and full-dose cone-beam computed tomography (CBCT).Materials and methodsA total of 890 maxillary sinuses on 445 CBCT scans were analyzed. The air space, MT, and MRCs in each sinus were manually segmented. Low-dose CBCTs were divided into training, training-monitoring, and testing datasets at a 7:1:2 ratio. Full-dose CBCTs were used as a testing dataset. A three-step CNN algorithm built based on V-Net and support vector regression was trained on low-dose CBCTs and tested on the low-dose and full-dose datasets. Performance for detection of MT and MRCs using area under the curves (AUCs) and for segmentation using Dice similarity coefficient (DSC) was evaluated.ResultsFor the detection of MT and MRCs, the algorithm achieved AUCs of 0.91 and 0.84 on low-dose scans and of 0.89 and 0.93 on full-dose scans, respectively. The median DSCs for segmenting the air space, MT, and MRCs were 0.972, 0.729, and 0.678 on low-dose scans and 0.968, 0.663, and 0.787 on full-dose scans, respectively. There were no significant differences in the algorithm performance between low-dose and full-dose CBCTs.ConclusionsThe proposed CNN algorithm has the potential to accurately detect and segment MT and MRCs in maxillary sinus on CBCT scans with low-dose and full-dose protocols.Clinical relevanceAn implementation of this artificial intelligence application in daily practice as an automated diagnostic and reporting system seems possible.

The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.