Negar Firoozeh

Active surveillance (AS) is an established management strategy for men with low-risk and some favorable intermediate-risk prostate cancer. Multiparametric MRI (MRI) plays an increasingly important role in AS, contributing to initial risk stratification, confirmatory evaluation, and longitudinal monitoring. However, comparing baseline and follow-up MR images is challenging because acquisition varies, readers differ, and imaging alone has limited ability to define clinically meaningful progression. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) scoring system was developed to standardize the assessment of interval change on serial prostate MRI. PRECISE provides a structured framework for categorizing radiologic regression, stability, and progression, thereby addressing a key limitation of Prostate Imaging – Reporting and Data System (PI-RADS), which was designed for lesion detection rather than longitudinal assessment. PRECISE version 2 further refines definitions and introduces clearer categories, including separation of stable visible and stable non-visible disease and recognition of non-diagnostic examinations. Available evidence suggests that PRECISE performs best as a rule-out tool, with consistently high negative predictive value for clinically significant progression, while radiologic progression demonstrates more limited positive predictive value for histopathologic upgrading. Although PRECISE reduces subjective interpretation through structured scoring, its application remains affected by variability in MRI quality and heterogeneity in AS protocols. To mitigate these limitations, radiologists should incorporate prostate-specific antigen kinetics, biopsy results, and clinical context into their interpretation of serial MRI. In this review, we provide a practical, radiologist-focused overview of PRECISE, summarize its evolution and evidence base, and illustrate its application in clinical practice. We also discuss key limitations, common pitfalls, and the importance of MRI quality and standardization in improving reproducibility. PRECISE represents an important step toward standardized longitudinal assessment in prostate cancer AS and supports more consistent, evidence-based multidisciplinary decision-making.

Several artificial intelligence (AI) algorithms have been designed for detection of pulmonary embolism (PE) using computed tomographic pulmonary angiography (CTPA). Due to the rapid development of this field and the lack of an updated meta-analysis, we aimed to systematically review the available literature about the accuracy of AI-based algorithms to diagnose PE via CTPA. We searched EMBASE, PubMed, Web of Science, and Cochrane for studies assessing the accuracy of AI-based algorithms. Studies that reported sensitivity and specificity were included. The R software was used for univariate meta-analysis and drawing summary receiver operating characteristic (sROC) curves based on bivariate analysis. To explore the source of heterogeneity, sub-group analysis was performed (PROSPERO: CRD42024543107). A total of 1722 articles were found, and after removing duplicated records, 1185 were screened. Twenty studies with 26 AI models/population met inclusion criteria, encompassing 11,950 participants. Univariate meta-analysis showed a pooled sensitivity of 91.5% (95% CI 85.5–95.2) and specificity of 84.3 (95% CI 74.9–90.6) for PE detection. Additionally, in the bivariate sROC, the pooled area under the curved (AUC) was 0.923 out of 1, indicating a very high accuracy of AI algorithms in the detection of PE. Also, subgroup meta-analysis showed geographical area as a potential source of heterogeneity where the I² for sensitivity and specificity in the Asian article subgroup were 60% and 6.9%, respectively. Findings highlight the promising role of AI in accurately diagnosing PE while also emphasizing the need for further research to address regional variations and improve generalizability.

This study concentrates on the segmentation of intracranial aneurysms, a pivotal aspect of diagnosis and treatment planning. We aim to overcome the inherent instance imbalance and morphological variability by introducing a novel morphology and texture loss reweighting approach. Our innovative method involves the incorporation of tailored weights within the loss function of deep neural networks. Specifically designed to account for aneurysm size, shape, and texture, this approach strategically guides the model to focus on capturing discriminative information from imbalanced features. The study conducted extensive experimentation utilizing ADAM and RENJI TOF-MRA datasets to validate the proposed approach. The results of our experimentation demonstrate the remarkable effectiveness of the introduced methodology in improving aneurysm segmentation accuracy. By dynamically adapting to the variances present in aneurysm features, our model showcases promising outcomes for accurate diagnostic insights. The nuanced consideration of morphological and textural nuances within the loss function proves instrumental in overcoming the challenge posed by instance imbalance. In conclusion, our study presents a nuanced solution to the intricate challenge of intracranial aneurysm segmentation. The proposed morphology and texture loss reweighting approach, with its tailored weights and dynamic adaptability, proves to be instrumental in enhancing segmentation precision. The promising outcomes from our experimentation suggest the potential for accurate diagnostic insights and informed treatment strategies, marking a significant advancement in this critical domain of medical imaging.

Du e to rapid population growth and the need to use artificial intelligence to make quick decisions, developing a machine learning-based disease detection model and abnormality identification system has greatly improved the level of medical diagnosis Since COVID-19 has become one of the most severe diseases in the world, developing an automatic COVID-19 detection framework helps medical doctors in the diagnostic process of disease and provides correct and fast results. In this paper, we propose a machine lear ning based framework for the detection of Covid 19. The proposed model employs a Tsukamoto Neuro Fuzzy Inference network to identify and distinguish Covid 19 disease from normal and pneumonia cases. While the traditional training methods tune the parameters of the neuro-fuzzy model by gradient-based algorithms and recursive least square method, we use an evolutionary-based optimization, the Cat swarm algorithm to update the parameters. In addition, six texture features extracted from chest X-ray images are give n as input to the model. Finally, the proposed model is conducted on the chest X-ray dataset to detect Covid 19. The simulation results indicate that the proposed model achieves an accuracy of 98.51%, sensitivity of 98.35%, specificity of 98.08%, and F1 score of 98.17%.