Objective: The primary objective of this study is to review the clinical parameters associated with skull base osteomyelitis (SBO), with a secondary aim of studying their association with patient outcomes 1 and 6 months after treatment initiation.Study Design: This is a single‐center restrospective observational study.Setting: The study was conducted from January 2018 to December 2022 at the University Malaya Medical Center in Kuala Lumpur.Methods: Patients aged over 15 years with a diagnosis of SBO were included in the study. Clinical parameters, investigations, and follow‐up records were recorded. The disease outcomes were analyzed at 1 and 6 months after treatment initiation using multivariable analyses.Results: The study identified 31 patients with SBO, the majority of whom were elderly males with comorbidities such as diabetes and hypertension. Otalgia and otorrhea were the most common symptoms, and computed tomography scans were used for diagnosis. Pseudomonas aeruginosa was the most commonly identified pathogen, and intravenous broad‐spectrum antimicrobials were used to treat all patients. Surgical intervention was required for 25% of patients, and underlying ischemic heart disease, anemia, and single nerve palsy were significantly associated with an unfavorable prognosis. Patients with higher body mass index and elevated C‐reactive protein showed poorer outcomes after 1 and 6 months of treatment, respectively.Conclusion: Early recognition, prompt treatment, better control of comorbidities, nutrition, and monitoring can improve SBO outcomes and reduce complications. Therefore, as the prevalence of SBO increases, diagnostic criteria or management guidelines should be established to guide the best clinical practice.

Artificial intelligence (AI), particularly machine learning and deep learning architectures, has been widely applied to support thyroid cancer diagnosis, but existing evidence on its performance and limitations remains scattered across techniques, tasks, and data types. This systematic review synthesizes recent work on knowledge extraction from heterogeneous imaging and clinical data for thyroid cancer diagnosis and detection published between 2021 and 2025. We searched eight major databases, applied predefined inclusion and exclusion criteria, and assessed study quality using the Newcastle–Ottawa Scale. A total of 150 primary studies were included and analyzed with respect to AI techniques, diagnostic tasks, imaging and non-imaging modalities, model generalization, explainable AI, and recommended future directions. We found that deep learning, particularly convolutional neural networks, U-Net variants, and transformer-based models, dominated recent work, mainly for ultrasound-based benign–malignant classification, nodule detection, and segmentation, while classical machine learning, ensembles, and advanced paradigms remained important in specific structured-data settings. Ultrasound was the primary modality, complemented by cytology, histopathology, cross-sectional imaging, molecular data, and multimodal combinations. Key limitations included diagnostic ambiguity, small and imbalanced datasets, limited external validation, gaps in model generalization, and the use of largely non-interpretable black-box models with only partial use of explainable AI techniques. This review provides a structured, machine learning-oriented evidence map that highlights opportunities for more robust representation learning, workflow-ready automation, and trustworthy AI systems for thyroid oncology.

Sakina Ghauth, Liew Yew Toong; Laryngeal amyloidosis: A rare aetiology of hoarseness, QJM: An International Journal of Medicine, , hcab256, https://doi.org/10.1

Case Series of Endoscopic Transoral Excision of Huge Parapharyngeal Tumors: Pushing the Boundaries - Turkish Archives of Otorhinolaryngology