{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T11:14:16Z","timestamp":1785323656884,"version":"3.55.0"},"reference-count":55,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2024,11,8]]},"abstract":"<jats:p>BACKGROUND: Schwann cell sheaths are the source of benign, slowly expanding tumours known as acoustic neuromas (AN). The diagnostic and treatment approaches for AN must be patient-centered, taking into account unique factors and preferences. OBJECTIVE: The purpose of this study is to investigate how machine learning and artificial intelligence (AI) can revolutionise AN management and diagnostic procedures. METHODS: A thorough systematic review that included peer-reviewed material from public databases was carried out. Publications on AN, AI, and deep learning up until December 2023 were included in the review\u2019s purview. RESULTS: Based on our analysis, AI models for volume estimation, segmentation, tumour type differentiation, and separation from healthy tissues have been developed successfully. Developments in computational biology imply that AI can be used effectively in a variety of fields, including quality of life evaluations, monitoring, robotic-assisted surgery, feature extraction, radiomics, image analysis, clinical decision support systems, and treatment planning. CONCLUSION: For better AN diagnosis and treatment, a variety of imaging modalities require the development of strong, flexible AI models that can handle heterogeneous imaging data. Subsequent investigations ought to concentrate on reproducing findings in order to standardise AI approaches, which could transform their use in medical environments.<\/jats:p>","DOI":"10.3233\/thc-232043","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T09:42:50Z","timestamp":1722591770000},"page":"3801-3813","source":"Crossref","is-referenced-by-count":3,"title":["The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review"],"prefix":"10.1177","volume":"32","author":[{"given":"Hadeel","family":"Alsaleh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/THC-232043_ref2","doi-asserted-by":"crossref","first-page":"3733","DOI":"10.2147\/CMAR.S140764","article-title":"Stereotactic radiosurgery for vestibular schwannomas","volume":"20","author":"Braunstein","year":"2018","journal-title":"Cancer management and research."},{"issue":"14","key":"10.3233\/THC-232043_ref3","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1056\/NEJMra2020394","article-title":"Vestibular schwannomas","volume":"384","author":"Carlson","year":"2021","journal-title":"New England Journal of Medicine."},{"issue":"1","key":"10.3233\/THC-232043_ref4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1215\/S1522851704001097","article-title":"Descriptive epidemiology of vestibular schwannomas","volume":"8","author":"Propp","year":"2006","journal-title":"Neuro-oncology."},{"key":"10.3233\/THC-232043_ref5","doi-asserted-by":"crossref","unstructured":"Fisher JL, Pettersson D, Palmisano S, Schwartzbaum JA, Edwards CG, Mathiesen T, Prochazka M, Bergenheim T, Florentzson R, Harder H, Nyberg G. 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