Hemangiopericytoma is an uncommon neoplasm that may present in myriad locations, including the lower extremities, pelvic area, and the head and neck area, including the orbit.1 Orbital hemangiopericytoma is often described as synonymous with orbital solitary fibrous tumor, giant cell angiofibroma, and fibrous histiocytoma, as they all belong to a spectrum of collagen-rich fibroblastic tumors that are often CD34-positive and have overlapping histopathologic features.2 Many cases of orbital hemangiopericytoma have been reported in the literature along with various surgical approaches, long-term outcomes, and techniques to manage recurrence; however, few have discussed preoperative embolization.1,3-5 Intraoperative hemorrhage is a concern in both the congenital and the adult form of these cases6,7 and may be an indication for preoperative embolization. A unique case of preoperative embolization was presented with n-butyl cyanoacrylate for surgical resection of a large orbital hemangiopericytoma in a 58-year-old woman.

Background: The automatic coding of electronic medical records with ICD (International Classification of Diseases) codes is an area of interest due to its potential in improving efficiency and streamlining processes such as billing and outcome tracking. artificial intelligence (AI), and particularly convolutional neural networks (CNN), have been suggested as a possible mechanism for automatic coding. To this end, a rapid review has been undertaken in order to assess the current use of CNN in predicting ICD codes from electronic medical records. Methods: After screening PubMed, IEEE Xplore, Scopus, and Google Scholar, 11 studies were analyzed for the use of CNN in predicting ICD codes. We used artificial intelligence and ICD prediction as keywords in the search strategy. Results: The analysis yielded a recommendation to further explore and research CNN frameworks as a promising lead to automatic ICD coding when paired with word embedding and/or neural transfer learning, while keeping research open to a wide variety of AI techniques. Conclusion: CNN frameworks are promising for the prediction of ICD codes from clinical notes. Bangabandhu Sheikh Mujib Medical University Journal 2023;16(2): 118-123

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