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Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far-reaching implications across various medical fields. This review meticulously examines the integration of artificial intelligence (AI) in anomaly detection for MR images, spotlighting its transformative impact on medical diagnostics. We delve into the forefront of AI applications in MRI, exploring advanced machine learning (ML) and deep learning (DL) methodologies that are pivotal in enhancing the precision of diagnostic processes. The review provides a detailed analysis of preprocessing, feature extraction, classification, and segmentation techniques, alongside a comprehensive evaluation of commonly used metrics. Further, this paper explores the latest developments in ensemble methods and explainable AI, offering insights into future directions and potential breakthroughs. This review synthesizes current insights, offering a valuable guide for researchers, clinicians, and medical imaging experts. It highlights AI's crucial role in improving the precision and speed of detecting key structural and functional irregularities in MRI. Our exploration of innovative techniques and trends furthers MRI technology development, aiming to refine diagnostics, tailor treatments, and elevate patient care outcomes. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.
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BACKGROUND AND OBJECTIVE: Prostate cancer is one of the most widespread cancers among men throughout the world. In addition, it is the second cause of death after lung cancer. Occurrence of the prostate cancer is variable in various regions of the world. Solely, there are three known risk factors for the prostate cancer, including: Age, inheritance and ethnic origin. ELAC2 protein is a phosphodiesterase enzyme encoded by ELAC2 gene in human. This gene is placed on chromosome 17, and it is believed that product of the mentioned gene is an endonuclease contributed in puberty of mitochondrion's tRNA. From clinical viewpoint, variables of ELAC2 gene such as Ser217Leu and Ala541Thr Missense mutations which are accompanied by hereditary prostate cancer (HPC2).Objective of this study is to investigate Ser217Leu (rs4792311) and Ala541Thr (rs5030739) polymorphisms in the individuals with prostate cancer or those who are suspicious of prostate cancer with family past record/history. STUDY METHOD: In this study conducted by case-control method in 2018, 102 men with prostate cancer and 98 men being suspicious of prostate cancer out of 10 families referred to shahid Rajaei Hospital in Tonekabon county to study and check were investigated. After collection of data using questionnaire, sampling from individuals and performance of the rest steps, study of polymorphism was carried out by PCR sequencing technique, and the results were analyzed by Chromas software. FINDING: Of the total studied 102 individuals, 44 individuals (43.1%) were homozygote for Ser217Leu mutation, 36 individuals (35.2%) were heterozygote and 22 individuals (21.5%) lacked Missense mutation. for Ala541Thr mutation, 18 ones (17.6%) were heterozygote and 84 ones (82.3%) lacked Missense mutation. For Ser217Leu mutation, out of 98 suspicious individuals, 21 individuals (21.4%) were homozygote. 6 individuals (6.1%) were heterozygote and 71 individuals (72.4%) lacked the mutation. For Ala541Thr mutation, 15 ones (15.3%) were homozygote and 84 ones (84.6%) lacked the studied mutation. CONCLUSION: Results of this research showed that, in the individuals with the prostate cancer, there is a relationship between Ser217Leu and Ala541Thr polymorphism of ELAC2 gene and/with prostate cancer, and the suspicious individuals gotten involved in the mutation must take action to prevent this cancer.
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