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This paper provides two different methods to diagnose osteoporosis in women; the first method is the fractal analysis evaluated by CBCT at two bone locations (the mandible and the second cervical vertebrae) to see if there is any correlation between the two. At the same time, the second method is deep convolutional neural networks (DCNNs). One hundred eighty-eight patients' mandibular CBCT images were used, and DCNN models based on the ResNet-101 framework were employed. Dual X-ray absorptiometry of the hip and lumbar spine revealed that 139 of the 188 postmenopausal women tested had osteoporosis, whereas 49 had average bone mineral density. The second cervical vertebra and the mandible were selected as locations of interest for FD analysis on the CBCT images. Measurement accuracy, both within and between observers' agreements, and correlations between two data sets were all calculated. To evaluate osteoporosis, we used a segmented, three-phase approach. Stage 1 was devoted to the identification of mandibular bone slices. In Stage 2, the coordinates for the mandible's cross-sectional views were established, and Stage 3 calculated the thickness of the mandible bone, emphasizing osteoporotic variations. The average FD values within the interest area of the mandible were significantly lower in people with osteoporosis than in those with average bone mineral density. At the same time, the two groups had no significant difference in FD values at the second cervical vertebra. For the mandibular site, areas beneath the curve were 0.644 (P = 0.008), while the area under the curve for the vertebral site was 0.531 (P = 0.720). DCNN training in the first stage yielded an astounding 98.85% training accuracy, the second stage decreased L1 loss to a meager 1.02 pixels, and the bone thickness computation method used in the last stage had a mean squared error of 0. 8377. We concluded that FD was underutilized even though it distinguished between women with normal BMD and those with osteoporosis in the mandibular area. Additionally, even with small mandibular CBCT datasets, the results show the value of a modular transfer learning approach for osteoporosis detection. [ABSTRACT FROM AUTHOR]

There has yet to be a comprehensive investigation on enhancing the diagnostic accuracy of oral disease using handheld smartphone photographic photos. To overcome the difficulties associated with the automatic detection of oral illnesses, we describe an approach based on smartphone image diagnosis powered by a deep learning algorithm. The centered rule method of image capture was offered as a quick and easy way to get high-quality pictures of the mouth. A resampling method was proposed to mitigate the influence of image variability from handheld smartphone cameras, and a medium-sized oral dataset with five types of disorders was developed based on this approach. Finally, we introduce a recently developed deep-learning network to assess oral cancer diagnosis. On 455 test images, the proposed technique showed an impressive 83.0% sensitivity, 96.6% specificity, 84.3% accuracy, and 83.6% F1. The proposed "center positioning" method was about 8% higher than a simulated "random positioning" method; the resampling process had an additional 6% performance improvement. The performance of a deep learning algorithm for detecting oral cancer can be enhanced by capturing oral photos centered on the lesion. Primary oral cancer diagnosis using smartphone-based images with deep learning offers promising potential. [ABSTRACT FROM AUTHOR]

Periapical lesions have been implicated in sinus-related complications, but the precise influence of anatomical variations in the posterior superior alveolar artery (PSAA) on mucosal thickening remains an uncharted aspect. The new AI tool employed in this research utilizes advanced image processing algorithms to enhance image visualization. Background and Objectives: This study examines the accuracy of a new cone beam computed tomography (CBCT) software (eVol DXS, version 1.0.1.0) employing AI to detect the PSAA's location and the effect of that on maxillary sinus thickening in the presence of periapical lesions. Materials and Methods: This retrospective study included 120 CBCT cases with posterior maxillary periapical lesions and 120 without odontogenic infections. Teeth with proximity (<2 mm) to the sinus were excluded in both groups to eliminate the sinus floor's perforation effect. Both the PSAA locations and maxillary sinus thickening were classified and compared. Results: The mucosal thickening differs significantly (p < 0.001) between the study group and the control group. The study showed that an increased sinus thickness occurred when the PSAA was beneath the sinus membrane in the study group (62.5% compared to 8.6%; p < 0.001 *). The AI tool helped to achieve a 100% identification rate in determining the PSAA locations. Conclusions: AI algorithms for PSAA localization, which affects mucosal thickness in response to periapical lesions, yield excellent results. [ABSTRACT FROM AUTHOR]