Background: Multiple sclerosis is often a longitudinal disease continuum with an initial relapsing-remitting phase (RRMS) and later secondary progression (SPMS). Most currently approved therapies are not sufficiently effective in SPMS. Early detection of SPMS conversion is therefore critical for therapy selection. Important decision-making tools may include testing of partial cognitive performance and magnetic resonance imaging (MRI). Aim of the work: To demonstrate the importance of cognitive testing and MRI for the prediction and detection of SPMS conversion. Elaboration of strategies for follow-up and therapy management in practice, especially in outpatient care. Material and methods: Review based on an unsystematic literature search. Results: Standardized cognitive testing can be helpful for early SPMS diagnosis and facilitate progression assessment. Annual use of sensitive screening tests such as Symbol Digit Modalities Test (SDMT) and Brief Visual Memory Test-Revised (BVMT-R) or the Brief International Cognitive Assessment for MS (BICAMS) test battery is recommended. Persistent inflammatory activity on MRI in the first three years of disease and the presence of cortical lesions are predictive of SPMS conversion. Standardized MRI monitoring for features of progressive MS can support clinically and neurocognitively based suspicion of SPMS. Discussion: Interdisciplinary care of MS patients by clinically skilled neurologists, supported by neuropsychological testing and MRI, has a high value for SPMS prediction and diagnosis. The latter allows early conversion to appropriate therapies, as SPMS requires different interventions than RRMS. After drug switching, clinical, neuropsychological, and imaging vigilance allows stringent monitoring for neuroinflammatory and degenerative activity as well as treatment complications.

Gout is an arthritis characterized by the deposition of sodium monoacid crystals in the synovial membrane, articular cartilage, and periarticular tissues that leads to an inflamatory process. In most cases, the diagnosis is established by clinical criteria and analysis of the synovial fluid for MSU crystals. However, gout may manifest in atypical ways and make diagnosis difficult. In these situations, imaging studies play a fundamental role in helping to confirm the diagnosis or even exclude other differential diagnoses. Conventional radiography is still the most commonly used method in the follow-up of these patients, but it is a very insensitive test, because it only detects late changes. In recent years, advances in imaging methods have emerged in relation to gout. Ultrasound has proven to be a highly accurate test in the diagnosis of gout, identifying MSU deposits in articular cartilage and periarticular tissues, and detecting and characterizing tophi, tendinopathies, and tophi enthesopathies. Computed tomography is an excellent exam for the detection of bone erosions and evaluation of spinal involvement. Dual-energy computed tomography, a new method that provides information on the chemical composition of tissues, allows identification of MSU deposits with high accuracy. MRI can be useful in the evaluation of deep tissues not accessible by ultrasound. In addition to diagnosis, with the emergence of drugs that aim to reduce the tophaceous burden, imaging examinations become a useful tool in the follow-up treatment of gout patients.

Objective: To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and cirrhotic nodules via radiomics models based on magnetic resonance images. Background: This study is to distinguish hepatocellular carcinoma and cirrhotic nodules using MR-radiomics features extracted from four different phases of MRI images, concluded T1WI, T2WI, T2 SPIR and delay phase of contrast MRI. Methods: In this study, the four kind of magnetic resonance images of 23 patients with hepatocellular carcinoma (HCC) were collected. Among them, 12 patients with liver cirrhosis were used to obtain cirrhotic nodules (CN). The dataset was used to extract MR-radiomics features from regions of interest (ROI). The statistical methods of MRradiomics features could distinguish HCC and CN. And the ability of radiomics features between HCC and CN was estimated by receiver operating characteristic curve (ROC). Results: A total of 424 radiomics features were extracted from four kind of magnetic resonance images. 86 features in delay phase of contrast MRI，86 features in spir phase of T2WI，86 features in T1WI and 88 features in T2WI showed statistical difference (p < 0.05). Among them, the area under the curves (AUC) of these features larger than 0.85 were 58 features in delay phase of contrast MRI, 54 features in spir phase of T2WI, 62 features in T1WI and 57 features in T2WI. Conclusions: Radiomics features extracted from MRI images have the potential to distinguish HCC and CN.

Imaging technology plays a key role in guiding endovascular treatment of aortic aneurysm, especially in the complex thoracoabdominal aorta. The combination of high quality images with a sterile and functional environment in the surgical suite can reduce contrast and radiation exposure for both patient and operator, in addition to better outcomes. This presentation aims to describe the current use of this technique, combining angiotomography and intraoperative cone beam computed tomography, image “fusion” and intravascular ultrasound, to guide procedures and thus improve the intraoperative success rate and reduce the need for reoperation. On the other hand, a procedure is described to create customized 3D templates with the high-definition images of the patient’s arterial anatomy, which serve as specific guides for making fenestrated stents in the operating room. These customized fenestration templates could expand the number of patients with complex aneurysms treated minimally invasively.

Introduction: Chest trauma has a high incidence and pneumothorax is the most frequent finding. The literature is scarce on what to do with asymptomatic patients with pneumothorax due to penetrating chest trauma. The aim of this study was to evaluate what are the findings of the control radiography of patients with penetrating chest trauma who are not initially taken to surgery, and their usefulness in determining the need for further treatment. Methods: A retrospective cohort study was performed, including patients older than 15 years who were admitted for penetrating chest trauma between January 2015 and December 2017 and who did not require initial surgical management. We analyzed the results of chest radiography, the time of its acquisition, and the behavior decided according to the findings in patients initially left under observation. Results: A total of 1,554 patients were included, whose average age was 30 years, 92.5% were male and 97% had a sharp weapon wound. Of these, 186 (51.5%) had no alterations in their initial X-ray, 142 had pneumothorax less than 30% and 33 had pneumothorax greater than 30 %, hemopneumothorax or hemothorax. Closed thoracostomy was required as the final procedure in 78 cases, sternotomy or thoracotomy in 2 cases and discharged in 281. Conclusion: In asymptomatic patients with small or moderate pneumothorax and no other significant lesions, longer observation times, radiographs and closed thoracostomy may be unnecessary.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.