External Ear Diseases: A Comprehensive Review of the Pathologies With Neuroradiological Considerations

Samara, Amjad; Herrmann, Stephen D.; Ditzler, Matthew G.; Raj, Karuna M.; Orlowski, Hilary L.P.; Eldaya, Rami W.

doi:10.1067/j.cpradiol.2020.12.007

Cited by 2 publications

(1 citation statement)

References 50 publications

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“…This study is driven by a number of separate motivations, primarily driven by the urgent need for accurate and all-encompassing diagnostic strategies to deal with the intricacies of auriculotemporal [ 13 ] and ossicle-related disorders [ 14 ]. The first compelling force is the immediate requirement for thorough and precise diagnostic methods for auriculotemporal [ 13 ] and ossicle-related diseases [ 14 ]. These diseases frequently pose perplexing puzzles, prompting patients and medical professionals to search for better diagnostic techniques [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle

Cai,

Zhang,

Xie

et al. 2024

BMC Med Imaging

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Precision and intelligence in evaluating the complexities of middle ear structures are required to diagnose auriculotemporal and ossicle-related diseases within otolaryngology. Due to the complexity of the anatomical details and the varied etiologies of illnesses such as trauma, chronic otitis media, and congenital anomalies, traditional diagnostic procedures may not yield accurate diagnoses. This research intends to enhance the diagnosis of diseases of the auriculotemporal region and ossicles by combining High-Resolution Spiral Computed Tomography (HRSCT) scanning with Deep Learning Techniques (DLT). This study employs a deep learning method, Convolutional Neural Network-UNet (CNN-UNet), to extract sub-pixel information from medical photos. This method equips doctors and researchers with cutting-edge resources, leading to groundbreaking discoveries and better patient healthcare. The research effort is the interaction between the CNN-UNet model and high-resolution Computed Tomography (CT) scans, automating activities including ossicle segmentation, fracture detection, and disruption cause classification, accelerating the diagnostic process and increasing clinical decision-making. The suggested HRSCT-DLT model represents the integration of high-resolution spiral CT scans with the CNN-UNet model, which has been fine-tuned to address the nuances of auriculotemporal and ossicular diseases. This novel combination improves diagnostic efficiency and our overall understanding of these intricate diseases. The results of this study highlight the promise of combining high-resolution CT scanning with the CNN-UNet model in otolaryngology, paving the way for more accurate diagnosis and more individualized treatment plans for patients experiencing auriculotemporal and ossicle-related disruptions.

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Section: Introductionmentioning

confidence: 99%

Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle

Cai,

Zhang,

Xie

et al. 2024

BMC Med Imaging

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Factors Influencing Treatment Success in Cholesteatoma Management: A Cross-Sectional Study

Alshehri,

Al Shalwan,

Oraydan

et al. 2024

JCM

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Background/Objectives: Cholesteatoma presents significant management challenges in otolaryngology. This study aimed to delineate the influence of demographic and clinical characteristics, preoperative imaging, and surgical approaches on treatment success in cholesteatoma management. Methods: A cross-sectional analytical study was conducted at the Otolaryngology Department of the University Hospital from January 2021 to December 2022. It included 68 patients diagnosed with cholesteatoma, focusing on three objectives: assessing the impact of demographic and clinical characteristics on treatment outcomes, evaluating the predictive value of preoperative imaging findings, and analyzing the influence of surgical factors. Results: The study population predominantly consisted of male (56%) and Saudi (81%) patients, with an average age of 45 years. Logistic regression revealed that older age (OR: 1.05), male gender (OR: 0.63), and non-Saudi Arab ethnicity (OR: 2.14) significantly impacted treatment outcomes. Clinical characteristics such as severe disease severity (OR: 3.00) and longer symptom duration (OR: 0.96) also influenced treatment success. In preoperative imaging, labyrinthine fistula (Regression Coefficient: 0.63) and epidural extension (Coefficient: 0.55) emerged as key predictors. The surgical factors that significantly affected the outcomes included the extent of surgery (Complete Removal OR: 3.32) and the use of endoscopic approaches (OR: 1.42). Conclusions: This study highlights that patient demographics, clinical profiles, specific preoperative imaging features, and surgical strategies multifactorially determine cholesteatoma treatment success. These findings suggest the necessity for a tailored approach in cholesteatoma management, reinforcing the importance of individualized treatment plans based on comprehensive preoperative assessments.

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External Ear Diseases: A Comprehensive Review of the Pathologies With Neuroradiological Considerations

Cited by 2 publications

References 50 publications

Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle

Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle

Factors Influencing Treatment Success in Cholesteatoma Management: A Cross-Sectional Study

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