Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, € Ozy€ urek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. ) methods were compared using Wilcoxon signed rank test and Bland-Altman analysis.Results The deep convolutional neural network system was successful in detecting teeth and numbering specific teeth. Only one tooth was incorrectly identified. The AI system was able to detect 142 of a total of 153 periapical lesions. The reliability of correctly detecting a periapical lesion was 92.8%. The deep convolutional neural network volumetric measurements of the lesions were similar to those with manual segmentation. There was no significant difference between the two measurement methods (P > 0.05).Conclusions Volume measurements performed by humans and by AI systems were comparable to each other. AI systems based on deep learning methods can be useful for detecting periapical pathosis on CBCT images for clinical application.
Recent developments in diagnostic imaging herald a new approach to diagnosis and management of prostate cancer. Multimodality fusion that combines anatomic with functional imaging data has surpassed either of the two alone. This opens up the possibility to “find and fix” malignancy with greater accuracy than ever before. This is particularly important for prostate cancer because it is the most common male cancer in most developed countries. This article describes technical advances under investigation at our institution and others using multimodality image fusion of magnetic resonance imaging (MRI), transrectal ultrasound (TRUS), and PSMA PET/CT (defined as the combination of prostate-specific membrane antigen [PSMA], positron emission tomography [PET], and computed tomography [CT]) for personalized medicine in the diagnosis and focal therapy of prostate cancer with high-intensity focused ultrasound (HiFUS).
Fifty-seven women with the hyperoprolactinemic syndrome coursing from 6 months to 13 years were examined. Total blood serum immunoreactive prolactin was measured, and craniograms made in all the patients, computer-aided tomography of the head was carried out in 49, and magnetic imaging in 38 patients. A total of 29 micro-, 16 macroadenomas, 1 chaniopharyngiomas, and 2 cases of “empty sella turcica” were revealed. Efficacy of prolactinoma detection by computer-aided tomography and magnetic imaging was 63.2 and 78.9%, respectively. Hence, these methods may effectively diagnose hypophyseal prolactinomas, but magnetic imaging is preferable for the detection of microprolactinomas and in cases with suspected volumic processes of the brain.
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