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Objective: We undertook a systematic review of the literature with meta‐analysis to identify the role of obesity (BMI ≥30) in the patient characteristics presenting with spontaneous cerebrospinal fluid (sCSF) leaks of the lateral skull base and the outcomes of their repair. Data Sources: A Systematic Review of English Articles using MEDLINE, EMBASE, and Cochrane Library. Review Methods: The research algorithm included the following keywords: "spontaneous CSF leak," "lateral skull base," "temporal bone," "meningocele," "encephalocele," and "otorrhea." We also manually searched the references of included studies, to identify possible studies missed during our literature search. Results: More than two‐thirds of the patients were female (69.2%) and often were obese (mean BMI 36.5 kg/m2) with a mean age of 57. Most common presenting symptoms were otorrhea and hearing loss. Most authors did not report a routine use of a post‐operative lumbar drain. Most patients had a single skull base defect and encephaloceles prolapsing through, across obese and non‐obese groups. Median length of stay in hospital was 3.2 days, and the majority of patients did not have any recurrence during their follow‐up (89.6%), which was not affected by obesity. Conclusion: Obesity does not affect length of hospital stay or recurrence rate following surgical repair of lateral skull base sCSF leaks. Surgical repair is a safe and viable approach in the management of obese patients with sCSF leaks in the temporal bone. Level of Evidence: NA Laryngoscope, 134:2012–2018, 2024 [ABSTRACT FROM AUTHOR]

Objective: The human temporal bone comprises more than 30 identifiable anatomical components. With the demand for precise image interpretation in this complex region, the utilization of artificial intelligence (AI) applications is steadily increasing. This systematic review aims to highlight the current role of AI in temporal bone imaging., Data Sources: A Systematic Review of English Publications searching MEDLINE (PubMed), COCHRANE Library, and EMBASE., Review Methods: The search algorithm employed consisted of key items such as 'artificial intelligence,' 'machine learning,' 'deep learning,' 'neural network,' 'temporal bone,' and 'vestibular schwannoma.' Additionally, manual retrieval was conducted to capture any studies potentially missed in our initial search. All abstracts and full texts were screened based on our inclusion and exclusion criteria., Results: A total of 72 studies were included. 95.8% were retrospective and 88.9% were based on internal databases. Approximately two-thirds involved an AI-to-human comparison. Computed tomography (CT) was the imaging modality in 54.2% of the studies, with vestibular schwannoma (VS) being the most frequent study item (37.5%). Fifty-eight out of 72 articles employed neural networks, with 72.2% using various types of convolutional neural network models. Quality assessment of the included publications yielded a mean score of 13.6 ± 2.5 on a 20-point scale based on the CONSORT-AI extension., Conclusion: Current research data highlight AI's potential in enhancing diagnostic accuracy with faster results and decreased performance errors compared to those of clinicians, thus improving patient care. However, the shortcomings of the existing research, often marked by heterogeneity and variable quality, underscore the need for more standardized methodological approaches to ensure the consistency and reliability of future data., Level of Evidence: NA Laryngoscope, 2024., (© 2024 The American Laryngological, Rhinological and Otological Society, Inc.)