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INTRODUCTION AND IMPORTANCE: This report details the unusual presentation of two hidden cavernous hemangiomas in the orbital apex, initially appearing as one tumor. The rarity and diagnostic complexity of this case underscore the need for meticulous surgical exploration and verification in orbital apex tumors. CASE PRESENTATION: A physical examination of an elderly male with a three-year history of headaches revealed a space-occupying lesion in the left orbital apex. Imaging confirmed a tumor in the extraconical space above the optic nerve. Initial nasal endoscopy removed an orbital apex tumor, pathologically confirmed as a cavernous hemangioma. CLINICAL DISCUSSION: Postoperative examination revealed incomplete tumor removal, prompting a second surgery for full excision. This case underscores the diagnostic and management challenges of orbital apex tumors, especially when imaging indicates a single mass. The endoscopic transsphenoidal approach for cavernous hemangiomas in the medial orbital apex, as illustrated in this case, appears promising. CONCLUSION: Clinicians must be aware of the potential for multiple tumors in orbital apex cases, even if imaging does not explicitly reveal them. This case highlights the importance of thorough surgical exploration and illustrates the effectiveness of endoscopic methods in intricate orbital apex surgeries.
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In recent years,deep learning,a pivotal subset of artificial intelligence machine learning,has achieved noteworthy advancements in the medical domain.It facilitates precise detection,diagnosis and prognostic assessment of various diseases through the analysis of medical images.Within ophthalmology,deep learning techniques have found wide-spread application in the diagnosis and prediction of thyroid-related eye diseases,orbital blowout fracture,melanoma,bas-al cell carcinoma,orbital abscess,lymphoma,retinoblastoma and other diseases.Leveraging images from computed tomo-graphy,magnetic resonance imaging and even pathological sections,this technology demonstrates a capacity to diagnose,differentiate and stage orbital diseases and ocular tumors with a high level of accuracy comparable to that of expert clini-cians.The promising prospects of this technology are expected to enhance the diagnosis and treatment of related diseases,concurrently reducing the time and cost associated with clinical practices.This review consolidates the latest research pro-gress on the application of artificial intelligence deep learning in orbital diseases and ocular tumors,aiming to furnish clini-cians with up-to-date information and developmental trends in this field,thereby furthering the clinical application and widespread adoption of this technology.
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The finite element method(FEM)is a widely employed mathematical technique in mechanical research that divides an object into discrete and interacting finite elements. Medically, finite element analysis(FEA)enables the simulation of biomechanical experiments that are challenging to conduct. Orbital surgery poses significant challenges to ophthalmologists due to its inherent difficulty and steep learning curve. FEM enables the simulation and analysis of the mechanical properties of orbital tissue, offering a novel approach for diagnosing and treating orbital-related diseases. With technological advancements, FEM has significantly matured in the diagnosis and treatment of orbital diseases, becoming a popular area of research in orbital biomechanics. This paper reviewed the latest advancements in orbital FEM, encompassing the development of orbital FEA models, simulation of orbital structure, and its application in orbital-related diseases. Additionally, the limitations of FEM and future research directions are also discussed. As a digital tool for auxiliary diagnosis and treatment, orbital FEA will progressively unlock its potential for diagnosing and treating orbital diseases alongside technological advancements.