Fibro-osseous lesions are a poorly defined category of conditions affecting the jaws and craniofacial bones, and include developmental lesions, reactive or dysplastic lesions, and neoplasms. Fibrous dysplasia and ossifying fibroma are the 2 main types of fibro-osseous lesions affecting the jaw, and ossifying fibroma is a true benign neoplasm of the bone-forming tissues with several well-recognized variants ranging from innocuous to extensively aggressive lesions. However, multiple simultaneous fibro-osseous lesions of the jaw bones involving all quadrants are exceedingly rare. One such case diagnosed by conventional radiography and computed tomography is discussed here.

Sjögren syndrome is a chronic autoimmune inflammatory disease characterized by lymphocytic infiltration of exocrine glands, predominantly the parotid and lacrimal glands, thereby resulting in oral and ocular dryness. It has been reported to occur most frequently in women between 40 and 50 years of age. Sjögren syndrome has an insidious onset, is slowly progressive, and presents a wide range of clinical manifestations, leading to delays or challenges in the diagnosis. Early diagnosis of this condition is essential to prevent the associated complications that affect patients’ quality of life. This report presents 3 cases of Sjögren syndrome in female patients aged between 40 and 75 years who presented with complaints of persistent dry mouth and burning sensation. The cases highlight the diagnostic value of 3-dimensional cone-beam computed tomographic sialography in the detection of salivary gland pathologies at an early stage.

Panoramic radiographs and computed tomography (CT) play a paramount role in the accurate diagnosis, treatment planning, and prognostic evaluation of various complex dental pathologies. The advent of cone-beam computed tomography (CBCT) has revolutionized the practice of dentistry, and this technique is now considered the gold standard for imaging the oral and maxillofacial area due to its numerous advantages, including reductions in exposure time, radiation dose, and cost in comparison to other imaging modalities. This review highlights the broad use of CBCT in the dentomaxillofacial region, and also focuses on future software advancements that can further optimize CBCT imaging.
Cone-Beam Computed Tomography ; Dentistry ; Diagnosis ; Pathology ; Radiography

Purpose: The aim of this review was to systematically analyze the available literature on the correlation between the gray values (GVs) of cone-beam computed tomography (CBCT) and the Hounsfield units (HUs) of computed tomography (CT) for assessing bone mineral density. Materials and Methods: A literature search was carried out in PubMed, Cochrane Library, Google Scholar, Scopus, and LILACS for studies published through September 2021. In vitro, in vivo, and animal studies that analyzed the correlations GVs of CBCT and HUs of CT were included in this review. The review was prepared according to the PRISMA checklist for systematic reviews, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A quantitative analysis was performed using a fixed-effects model. Results: The literature search identified a total of 5,955 studies, of which 14 studies were included for the qualitative analysis and 2 studies for the quantitative analysis. A positive correlation was observed between the GVs of CBCT and HUs of CT. Out of the 14 studies, 100% had low risks of bias for the domains of patient selection, index test, and reference standards, while 95% of studies had a low risk of bias for the domain of flow and timing. The fixed-effects meta-analysis performed for Pearson correlation coefficients between CBCT and CT showed a moderate positive correlation (r=0.669; 95% CI, 0.388 to 0.836; P<0.05). Conclusion The available evidence showed a positive correlation between the GVs of CBCT and HUs of CT.

Intelligent systems (i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.

Introduction: The increased prevalence of oral submucous fibrosis (OSMF) in the last few years relates to the increased consumption of areca nut(AN) products. OSMF is a premalignant condition and risk to progression to oral cancer is more when AN is chewed along with tobacco. Moreover, high copper content in AN is responsible for fibroblast dysfunction and fibrosis. This study was conducted with aim to assess and compare pH and copper content of raw AN and popular Indian commercial AN based (with and without tobacco) products. Method: Six samples each of twelve different brands of AN based commercial products i.e. six without tobacco (pan masala) and with tobacco were analyzed for pH and then the samples were dried, and powdered for estimation of the copper content. Results: For the six raw areca nuts (sample 1-6), the pH was found to range from 3.06±1.08 to 5.04±0.81, among the six non tobacco containing samples (sample 7-12), the pH was found to range from 6.03±1.08 to 9.09±0.81, and for six tobacco containing samples (sample 13-18), the pH was found to range from 9.18±0.90 to 11.07±0.09. The mean copper concentration among raw areca nut samples (sample 1-6) was 4.05±0.18 μg/g, among non-tobacco containing samples (sample 7-12) it was 10.17±1.08μg/g and among tobacco samples (sample 13-18),it was 18.09±1.08 μg/g (p<0.001). Conclusion: High copper content present in quid and commercial AN may be a causative factor for an increased fibrosis in OSMF, our findings need evaluation by further research and standardization.