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Prostate cancer is one of the most common cancers in the world. Due to the ageing of society and the extended life of the population, early diagnosis is a great challenge for healthcare. Unfortunately, the currently available diagnostic methods, in which magnetic resonance imaging (MRI) using the PIRADS protocol plays an increasingly important role, are imperfect, mostly in the inability to visualise small cancer foci and misinterpretation of the imagery data. Therefore, there is a great need to improve the methods currently applied and look for even better ones for the early detection of prostate cancer. In the presented research, anonymised MRI scans of 92 patients with evaluation in the PIRADS protocol were selected from the data routinely scanned for prostate cancer. Suspicious tissues were depicted manually under medical supervision. The texture features in the marked regions were calculated using the qMaZda software. The multiple-instance learning approach based on the SVM classifier allowed recognising between healthy and ill prostate tissue. The best F1 score equal to 0.77 with a very high recall equal to 0.70 and precision equal to 0.85 was recorded for the texture features describing the central zone. The research showed that the use of texture analysis in prostate MRI may allow for automation of the assessment of PIRADS scores. [ABSTRACT FROM AUTHOR]

Background: Disorders of the masticatory muscles and temporomandibular joints as well as the surrounding craniofacial structures are called temporomandibular disorders. These are dental diseases affecting an increasing number of people with a multifactorial etiology. Noninvasive ultrasonography imaging of temporomandibular joints was performed to obtain more detailed information on joint pathologies. Material and Methods: The aim of the study was to assess the influence of ultrasound examinations of the temporomandibular joints on the diagnosis and treatment planning in patients with temporomandibular disorders. The study included 110 patients examined with the use of the Research Diagnostic Criteria for Temporomandibular Disorders questionnaire, axis I and II, after which the initial treatment plan was created. All patients underwent an ultrasound examination of the temporomandibular joints. Results: The results reveal numerous morphological changes within the joint structures in all treatment groups. Comparative statistical analyses of symptoms were performed between study groups. Conclusions: The number of pathologies in the myofascial pain group was much higher than expected and required introduction of additional treatment procedures. Further studies confirming these results and the effectiveness of ultrasound diagnostic of temporomandibular disorders are recommended. [ABSTRACT FROM AUTHOR]

The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists. [ABSTRACT FROM AUTHOR]