Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by the clonal proliferation of myeloid precursors and rapid progression. Historically consisting of intensive chemotherapy, AML management has evolved significantly due to advances in molecular diagnostics and risk stratification. This review discusses current therapeutic paradigms in AML, emphasizing the growing role of personalized medicine across age and risk groups. For younger, fit patients, intensive regimens such as the “7 + 3” protocol remain the standard, often enhanced by targeted agents like FMS-like tyrosine kinase 3 (FLT3) and IDH inhibitors. Older or unfit individuals benefit from low-intensity treatments such as hypomethylating agents combined with venetoclax, now considered a frontline standard of care. The use of liposomal chemotherapy (CPX-351), measurable residual disease (MRD) monitoring, and maintenance therapy further refine post-remission strategies. Emerging therapies, including menin inhibitors, antibody–drug conjugates, and immunotherapies like CAR-T cells and vaccines, offer additional options, especially in relapsed/refractory settings. This comprehensive review outlines the current landscape and future directions in AML therapy, emphasizing the transition toward individualized, mutation-driven treatment strategies.

Eosinophilic granulomatosis with polyangiitis (EGPA) also referred to as Churg-Strauss syndrome is a rare vasculitis of the small to medium vessels. We present a rare case of acute coronary artery dissection brought on by EGPA, which generally has a poor prognosis. A 41-year-old male with history of bronchial asthma presented to the emergency room with a 2-week history of dyspnea, cough with clear phlegm, and fever. For the past eight months he had experienced episodes with similar symptoms relieved by steroids. CT chest showed bilateral upper lobe patchy opacities with extensive workup for infectious etiology being negative. He had peripheral eosinophilia with sinusitis. He had acute coronary syndrome and Coronary angiogram showed Right coronary artery dissection. After making a diagnosis of EGPA based on American college of Rheumatology criteria, he was successfully treated with high dose immunosuppression. Coronary artery dissection is a fatal and uncommon complication of EGPA which is usually diagnosed postmortem. Early recognition of this condition ante mortem and aggressive treatment can be lifesaving as demonstrated in our case.

Background: Abdominal aortic aneurysm (AAA) management relies heavily on imaging for surveillance, treatment planning, and follow-up. Deep learning (DL)-based segmentation may improve the efficiency and reproducibility of AAA image analysis; however, reported performance varies across studies. This study aimed to systematically review and quantitatively summarize the performance of DL models for AAA-related segmentation. Methods: PubMed, Embase, Scopus, and Web of Science were searched for studies applying DL to AAA imaging in adults and reporting Dice score or intersection-over-union (IoU) as segmentation metrics. Each DL model evaluated on a held-out test set was treated as a separate model-level observation. Random-effects meta-analyses were used to pool Dice score and IoU. Subgroup analyses were performed by imaging modality, model category, and input dimensionality, and additional analyses provided structure-specific estimates for aneurysm sac, intraluminal thrombus (ILT), lumen, and EVAR-related segmentations. Results: Across all targets, overall segmentation performance was 0.81 (95% CI 0.78–0.83) for Dice score and 0.72 (95% CI 0.67–0.77) for IoU. In subgroup analyses by model category, pooled Dice scores were 0.83 (95% CI 0.80–0.86) for U-Net family models, 0.82 (95% CI 0.77–0.86) for transformer or hybrid models, and 0.76 (95% CI 0.72–0.81) for other convolutional neural networks. By imaging modality, Dice scores were 0.87 (95% CI 0.84–0.89) for contrast-enhanced CT (CECT), 0.83 (95% CI 0.78–0.88) for non-contrast CT (NCCT), 0.84 (95% CI 0.81–0.87) for MRI, 0.78 (95% CI 0.74–0.82) for CT angiography (CTA), and 0.95 (95% CI 0.92–0.98) for X-ray. IoU subgroup analyses showed broadly similar patterns by modality and model category. In structure-specific analyses, aneurysm sac segmentation achieved a pooled Dice of 0.87 (95% CI 0.85–0.89) and IoU of 0.80 (95% CI 0.68–0.93); ILT segmentation reached 0.72 (95% CI 0.62–0.81) and 0.67 (95% CI 0.57–0.77); lumen segmentation 0.84 (95% CI 0.73–0.95) and 0.92 (95% CI 0.80–1.00); and EVAR-related targets 0.76 (95% CI 0.67–0.86) and 0.66 (95% CI 0.55–0.77) for Dice and IoU, respectively. Conclusions: DL-based models demonstrate good segmentation performance for the AAA sac and lumen, indicating clinically acceptable accuracy for applications such as volumetric surveillance and surgical planning. Performance for ILT and EVAR-related structures is more variable, suggesting that human oversight remains advisable for these targets in clinical practice. These findings support the use of DL-based segmentation in research and highlight its potential for selected clinical applications. However, the evidence base remains heterogeneous and is derived from a limited number of studies, with most models lacking external validation. Larger, standardized, multicentre evaluations with robust external validation are needed to confirm and refine these estimates.

Background: The integration of artificial intelligence (AI) in dental and maxillofacial radiology is revolutionizing diagnostic accuracy and clinical decision-making. This bibliometric analysis investigates the research landscape, emerging trends, and scholarly impact of AI applications in this specialized field. Methods: A comprehensive search was conducted on 25 December 2024 using the Web of Science Core Collection database. Data analysis tools, including VOSviewer, CiteSpace, and Biblioshiny, were employed to examine publication trends, global contributions, collaborative networks, and keyword dynamics. Results: The analysis revealed a marked increase in AI-related publications in dental and maxillofacial radiology, particularly from 2016 onward. The number of studies rose steadily, reaching 218 publications in 2024. The United States led in research output, followed closely by China and South Korea, with KU Leuven emerging as the top-contributing institution. Reinhilde Jacobs was identified as the most prolific author, while Medical Physics was the most cited journal. Co-citation analysis highlighted influential works by authors such as J.H. Lee and F. Schwendicke . Keywords including “artificial intelligence,” “deep learning,” “CBCT,” and “classification” dominated research discussions, reflecting the field’s evolving focus. Recent research trends emphasize advanced applications in segmentation, accuracy enhancement, and predictive modeling. Conclusion: AI has become integral to the advancement of dental and maxillofacial radiology, offering significant improvements in diagnostic precision and treatment planning. This study underscores the importance of staying abreast of AI innovations to enhance patient care and foster future research opportunities. Researchers and clinicians are encouraged to adopt AI-driven approaches to maximize clinical efficiency and outcomes.