Background/Objectives: Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes mellitus and a leading cause of vision impairment worldwide. Early detection and timely management are critical in preventing vision loss, yet current screening programs face challenges, including limited specialist availability and variability in diagnoses, particularly in underserved areas. This literature review explores the evolving role of artificial intelligence (AI) in enhancing the diagnosis, screening, and management of diabetic retinopathy. It examines AI’s potential to improve diagnostic accuracy, accessibility, and patient outcomes through advanced machine-learning and deep-learning algorithms. Methods: We conducted a non-systematic review of the published literature to explore advancements in the diagnostics of diabetic retinopathy. Relevant articles were identified by searching the PubMed and Google Scholar databases. Studies focusing on the application of artificial intelligence in screening, diagnosis, and improving healthcare accessibility for diabetic retinopathy were included. Key information was extracted and synthesized to provide an overview of recent progress and clinical implications. Conclusions: Artificial intelligence holds transformative potential in diabetic retinopathy care by enabling earlier detection, improving screening coverage, and supporting individualized disease management. Continued research and ethical deployment will be essential to maximize AI’s benefits and address challenges in real-world applications, ultimately improving global vision health outcomes.

Background: Endometrial cancer (EC) is the most common gynecological malignancy in developed countries, with diagnostic accuracy and early detection being critical to patient outcomes. Recent advances in artificial intelligence (AI) offer new opportunities to enhance diagnostic precision and clinical decision-making. Objectives: This literature review aims to explore recent developments in AI-augmented diagnostic approaches for EC, with a focus on applications in histopathology, imaging, and multi-omics, and to assess their clinical impact and future potential. Methods: A non-systematic literature review was conducted to examine recent advances in artificial intelligence applications for the diagnosis of EC. Relevant studies were identified through searches on PubMed and Google Scholar, focusing on the integration of AI techniques in histopathology, imaging, and multi-omics data. Conclusions: AI-driven diagnostic tools have shown high performance in detecting and characterizing EC across multiple modalities, often matching or exceeding expert-level accuracy. These technologies hold promise for earlier detection, better risk assessment, and more personalized treatment planning. However, further research and validation are needed to address current limitations and support their broader integration into clinical workflows.

Background/Objectives: Malignant pleural effusions (MPEs) pose a significant challenge in clinical practice and exert a considerable socio-economic burden on the healthcare system, affecting approximately 1 million individuals annually. These effusions are a leading cause of debilitating dyspnea and a diminished quality of life among cancer patients, with distant metastasis to the pleural layers occurring in about 20% of cases during treatment. Methods: A cross-sectional, observational case-control study was conducted on 151 Bulgarian patients with a hydrothorax. The control group included 72 patients with benign diseases, confirmed via biopsy, with 38 having inflammatory and 34 non-inflammatory pleural effusions. The other 79 patients had malignant pleural involvement. These groups are representative of the main types of pleural pathology. Results: The study found that all of the tumor markers, except for PIVKA-II (Protein induced by vitamin K absence-II), showed statistically significant differences between the malignant and non-malignant patient groups, with CAE (carcinoembryonic antigen) and CA19-9 showing the most notable differences. The Receiver Operating Characteristic (ROC) analysis revealed that CA72-4 had the best ability to distinguish between the two groups, while PIVKA was the weakest, with optimal cut-off values for all of the relevant tumor markers being derived using the Youden index. Conclusions: In conclusion, our study highlights the transformative potential of pleural fluid tumor markers as precise and minimally invasive resources for distinguishing malignant from non-malignant pleural effusions. These findings pave the way for improved diagnostic accuracy and personalized clinical management, addressing a critical gap in the care of patients with pleural pathologies.

Background: The sphenoid sinus is essential for transsphenoidal surgical accesses to the sellar and parasellar regions because of its anatomic proximity to vital vascular and neurologic structures such as the internal carotid artery, optic nerve, and cavernous sinus. The high degree of morphological variability of the sphenoid sinus has a significant impact on surgical technique and the risk of intraoperative complications. Detailed knowledge of individual anatomy is therefore crucial for the safety and efficacy of transsphenoidal approaches. Objectives: This review aims to conduct a systematic analysis of the current scientific literature on anatomical variations in the sphenoid sinus and their clinical relevance in surgical interventions to the skull base. Special attention is paid to the influence of morphological features on surgical strategies to pathological processes in this area and postoperative outcomes. Materials and Methods: A systematic review of the literature was conducted according to PRISMA 2020 guidelines. The PubMed, Scopus, Web of Science, and Google Scholar databases were searched for the period March 2010 to March 2025. Keywords such as “sphenoid sinus”, “anatomical variations”, “transsphenoidal surgery” and “skull base” were used. Original studies, systematic reviews, and meta-analyses focused on the anatomy, pneumatization, and surgical significance of sphenoid sinus variations are included. Quality and relevance criteria for published material were considered in the selection of articles. Results: The most commonly identified anatomic variations included sellar and lateral pneumaticity, the presence of Onodi cells, multiple and deviated septa, and dehiscence of the posterior wall of the sphenoid sinus and prolapse into its cavity of the internal carotid artery. These variations are associated with an increased risk of intraoperative vascular injury, visual deficit, and postoperative liquorrhea. Accurate preoperative assessment by high-resolution computed axial tomography and magnetic resonance imaging, as well as the use of intraoperative neuronavigation, are critical to reduce surgical risk. Conclusions: Anatomic variations in the sphenoid sinus are an essential factor to consider when planning and performing transsphenoidal surgical accesses. An individualized approach based on detailed diagnostic imaging analysis and neuronavigation technologies contributes to a higher safety of the performed surgical interventions, a better radicality of tumor resection and more favorable postoperative outcomes.

Introduction: Malignant pleural effusions are a common and debilitating complication of advanced malignancies, affecting approximately one million patients annually. This condition leads to significant morbidity and a decline in quality of life. Accurate diagnosis and effective management are critical yet challenging due to the overlap in biochemical markers between malignant and benign pleural effusions. Aim: This study evaluates an extended panel of biochemical parameters, including albumin gradient, total protein, cholesterol, pH, glucose, specific gravity, and lactate dehydrogenase (LDH), to enhance diagnostic precision. Materials and methods: In order to achieve this, we conducted a cross-sectional, observational case-control study in order to analyze pleural fluid samples from 151 Bulgarian patients, including 79 with malignant effusions and 72 with benign effusions. Biochemical markers, such as albumin gradient, total protein, cholesterol, lactate dehydrogenase (LDH), pH, glucose, and specific gravity, were measured using advanced clinical chemistry analyzers. Statistical analyses, including Mann-Whitney U tests, t-tests, and Spearman’s rank correlations, were used to identify diagnostic markers. Results: The key findings highlighted the diagnostic value of albumin gradient, total protein, and cholesterol levels, which are strongly associated with malignant effusions. LDH and specific gravity also demonstrated potential as supplementary markers, while pH and glucose measurements showed limited utility in differentiating malignancy. Conclusion: Combining these biochemical parameters enhances the precision of pleural effusion analysis, offering a more robust framework for diagnosing and managing malignant pleural effusions effectively.

Tracheobronchial amyloidosis (TBA) is a rare, localised form of amyloidosis characterised by the extracellular deposition of abnormal proteins within the tracheal and bronchial tissues. This condition, although uncommon, can significantly impact airway function, leading to symptoms such as persistent cough, dyspnea and airway obstruction. This report highlights the clinical presentation, diagnostic approaches and therapeutic options for TBA, emphasising the need for individualised management strategies and comprehensive patient care. This case describes a 62‐year‐old male with a history of smoking debuting with progressive hoarseness, dyspnea and dysphagia, who was initially diagnosed with tracheobronchial amyloidosis following biopsy of a subglottic mass. After surgical excision and a 3‐year disease‐free interval, he experienced multiple recurrences requiring further interventions, including bronchoscopic evaluation. Histopathological confirmation of recurrent amyloidosis led to a decision for strict follow‐up, as symptoms remained mild post‐biopsy.