To develop and present a checklist proposed to assist in planning, conducting, and reporting artificial intelligence (AI) studies in dentomaxillofacial radiology (CAIDMR - Checklist for AI in Dentomaxillofacial Radiology). To prepare the CAIDMR, a review was performed with searches in the PubMed, Embase, Scopus, and Web of Science databases with the descriptors of “Artificial Intelligence,” “Deep learning,” “Machine learning,” “Checklist,” “Dental,” and “Radiology,” using the PICOT strategy. In addition, pre-existing guidance documents and the AI management and ethical principles manual provided by the WHO were evaluated. After searching, 81 manuscripts were recruited: 27 from PubMed, 34 from Embase, 10 from Scopus, and 10 from Web of Science. Duplicate articles were removed. The studies were selected by reading the titles and abstracts and finally, the full article, resulting in six manuscripts for the full reading. The checklist was developed with the topic of planning and conducting research and 27 structured items for verification divided into the title, abstract, introduction, method, result, discussion, and other information. The CAIDMR is a guideline with a checklist for reports and studies on the application of AI in oral radiology.

Purpose: Skull is the most studied skeletal remaining as enamel and bones are the last ones to disintegrate after death. The clivus is one such dense part of the skull base and most of the time it is recovered intact from a damaged or incinerated skull and can be used as an indicator in identification. The present study is aimed to perform a radiomorphometric analysis of the clivus using cone-beam computed tomography (CBCT) and to assess its pertinence in age and gender estimation. Materials and Methods: The CBCT images of 254 (162 males, 92 females) subjects were obtained from New tom Giano HR (QR SRL Company, Verona, Italy) CBCT machine at 90 kVp, 6 mA for 7.2 s at the field of view (16 × 18), voxel size of 300 in the age group of 6–70 years were chosen. The clivus widths and lengths were measured using NNT software programs on axial and sagittal reconstructed images. Results: Mean clivus lengths and widths were significantly different in male and female patients. Bivariate correlations showed that there was a stronger association of the clivus lengths and widths to age when considered a linear combination as opposed to when taken individually in both female and male patients. Association between the linear combination of the clivus lengths and widths with age was found to be the highest in female patients among all the results (R = 0.553). All the above results were statistically significant (P < 0.05). Conclusion: CBCT measurements of clivus dimensions can be used reliably for anthropometric analysis as they are precisely associated with age and gender. Hence, it can be concluded that these dimensions can be used as a supplementary or only parameter when other parameters are uneventful in medicolegal cases.

Background: Recently, an automated diagnostic software called PanoSCOPE was developed. Aims: The aim of this study was to investigate whether there was any change in the mandibular cortical morphology at pre- and postdental implant operations using a computer aided diagnosis (CAD) system for panoramic radiography. Materials and Methods: Twenty patients were examined by panoramic radiography for pre- and postdental implant operations on the same date. The mandibular cortical index (MCI) and degree of deformation were analyzed using PanoSCOPE. The MCI classifications of all patients were evaluated with kappa coefficients between pre- and postdental implant operations. The degree of deformation of pre- and postdental implant operations was performed by the Pearson's rank correlation test. P value lower than 0.05 was considered statistically significant. Results: MCI of predental implant operation was Class 1: 10 cases, Class 2: nine cases, and Class 3: one case. MCI of postdental implant operation was Class 1: eight cases, Class 2: nine cases, and Class 3: three cases. The kappa coefficients between pre- and postdental implant operations were 0.746 (P < 0.001). We plotted degree of deformation of postdental implant operation (X) against degree of deformation of predental implant operation (Y) and observed a significant correlation (Y = 0.660 + 10.867 (n = 20, R² = 0.650, P < 0.001). Conclusions: This study showed that there was no significant change in the mandibular cortical morphology at pre- and postdental implant operations using the CAD system and indicated that the CAD system can be useful for the quantitative evaluation of MCI of patients with dental implant operation.

Purpose: This work aims to determine the magnetic resonance imaging (MRI) findings of the masses seen in the head-and-neck region and the effectiveness of the MRI in separation of malignant from benign head-and-neck masses through histopathological data. Materials and Methods: This retrospective study was conducted on 86 patients who were referred with prediagnosed as mass in oral, head and neck regions for evaluation with magnetic resonance imaging. MRI images were analyzed according to homogeneity and signal intensity in T1- and T2-weighted images, contrast enhancement pattern, peripheral edge characteristic of the lesion, invasion condition, presence of cystic/necrotic content, and presence of bone involvement of head-and-neck masses. Results: The MRI features of benign and malignant head and neck masses in which signal intensity on T1-weighted MRI images, signal homogeneity on T2-weighted MRI images, contrast enhancement pattern, edge feature and presence of invasion showed a statistically significant difference to determine benignity and malignancy. However, the signal homogeneity in T1-weighted images, signal intensity in T2-weighted images, presence of cystic/necrotic content, and bone involvement did not reveal a statistically significant difference. Conclusions: Overall, our findings were consistent with the literature data. As a result of our study, we recommend that certain MRI features of head-and-neck masses (signal intensity in T1-weighted images, signal homogeneity in T2-weighted images, contrast enhancement pattern, edge feature, and presence of invasion) can be used to differentiate between benign and malignant masses.