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.

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.

This research underscores the importance of enhancing the early detection of diabetic retinopathy and glaucoma, two prominent culprits behind vision loss. Typically, retinal diseases lurk without symptoms until they inflict severe vision impairment, underscoring the critical need for early identification. The research is centered on the potential of leveraging fundus images, which offer invaluable insights by analyzing various attributes of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. The conventional practice of manually segmenting retinal vessels by medical professionals is both intricate and time-consuming, demanding specialized expertise. This approach, reliant on pathologists, grapples with limitations related to scalability and accessibility. To surmount these challenges, the research introduces an automated solution employing computer vision. It conducts an evaluation of diverse retinal vessel segmentation and classification methods, including machine learning, filtering-based, and model-based techniques. Robust performance assessments, involving metrics like the true positive rate, true negative rate, and accuracy, facilitate a comprehensive comparison of these methodologies. The ultimate goal of this research is to create more efficient and accessible diagnostic tools, consequently enhancing the early detection of eye diseases through automated retinal vessel segmentation and classification. This endeavor combines the capabilities of computer vision and deep learning to pioneer new benchmarks in the realm of biomedical imaging, thereby addressing the pressing issues surrounding eye disease diagnosis.