Meniere hastalığının teşhisinde Yapay Sistemler: Bir Derleme

Yıl 2025, Cilt: 30 Sayı: 1, 140 - 149, 29.01.2025

Huri Nur Coskun , Çağrı Yıldız , Ömer Faruk Çalım , Emrah Gündüz

https://doi.org/10.21673/anadoluklin.1555477

Öz

Meniere Hastalığı (MH) spontan vertigo atakları, tek taraflı dalgalanan sensörinöral işitme kaybı, işitsel dolgunluk ve tinnitus ile karakterize karmaşık, multifaktöriyel bir iç kulak hastalığıdır. Endolenfatik hidrops (EH) genellikle MH’nin histopatolojik bir özelliği olarak kabul edilse de, 2015 tanı kılavuzları bunun varlığının tanı için gerekli olmadığını vurgulamaktadır. Manyetik Rezonans Görüntüleme (MRG), gelişim aşamasında olmasına rağmen EH’yi tespit etmek için değerli bir araç olarak ortaya çıkmıştır.
Son zamanlarda, insan bilişsel süreçlerini simüle eden ve hızla ilerleyen bir alan olan yapay zeka (YZ), MH çalışmalarında önemli bir ilgi görmüştür. Bu makale, Meniere Hastalığı’nın teşhisi, izlenmesi ve tedavisinde YZ ve derin öğrenmenin uygulanmasına ilişkin mevcut literatürü gözden geçirmektedir. İncelememiz PubMed, Scopus, Web of Science ve Science Direct’ten elde edilen yedi ilgili çalışmayı kapsamaktadır. Bunlar arasında dört makale, endolenfatik hidropsu tespit etmek ve ölçmek için MRG kullanımına odaklanmaktadır. Bu bulguları bir gelişim yörüngesi bağlamında sunuyor, mevcut metodolojilerin sınırlamalarını tartışıyor ve gelecekteki ilerlemeler için potansiyel yolları özetliyoruz.

Anahtar Kelimeler

Derin öğrenme, endolenfatik hidrops, makine öğrenmesi, Meniere hastalığı, MikroRNA, yapay zeka

Artificial systems for the diagnosis of Meniere’s disease: A review

Öz

Meniere’s Disease (MD) is a complex, multifactorial inner ear disorder characterized by episodes of spontaneous vertigo, unilateral fluctuating sensorineural hearing loss, aural fullness, and tinnitus. Although endolymphatic hydrops (EH) is often considered a histopathological hallmark of MD, the 2015 diagnostic guidelines emphasize that its presence is not essential for diagnosis. Magnetic Resonance Imaging (MRI) has emerged as a valuable tool for detecting EH, though it remains in a developmental phase.
Recently, artificial intelligence (AI)—a rapidly advancing field that simulates human cognitive processes—has garnered significant attention in the study of MD. This paper reviews the current literature on the application of AI and deep learning in the diagnosis, monitoring, and treatment of Meniere’s Disease. Our review encompasses seven relevant studies sourced from PubMed, Scopus, Web of Science, and ScienceDirect. Among these, four articles focus on the use of MRI to detect and quantify endolymphatic hydrops. We present these findings within the context of a development trajectory, discuss the limitations of current methodologies, and outline potential avenues for future advancements.

Anahtar Kelimeler

Artificial intelligence, deep learning, endolymphatic hydrops, machine learning, MicroRNA

Etik Beyan

Ethics committee approval is not required for this study.

Kaynakça

• Lopez-Escamez JA, Carey J, Chung WH, et al. Diagnostic criteria for Menière’s disease. J Vestib Res. 2015;25(1):1-7.
• Sajjadi H, Paparella MM. Meniere’s disease. Lancet. 2008;372(9636):406-14.
• Alexander TH, Harris JP. Current epidemiology of Meniere’s syndrome. Otolaryngol Clin North Am. 2010;43(5):965-70.
• Nakashima T, Pyykkö I, Arroll MA, et al. Meniere’s disease. Nat Rev Dis Primers. 2016;2:16028.
• Cureoglu S, da Costa Monsanto R, Paparella MM. Histopathology of Meniere’s disease. Oper Tech Otolayngol Head Neck Surg. 2016;27(4):194-204.
• Li J, Li L, Jin X, et al. MRI can help differentiate Ménière’s disease from other menieriform diseases. Sci Rep. 2023;13(1):21527.
• Young YH. Potential application of ocular and cervical vestibular-evoked myogenic potentials in Meniere’s disease: a review. Laryngoscope. 2013;123(2):484-91.
• Nakashima T, Naganawa S, Sugiura M, et al. Visualization of endolymphatic hydrops in patients with Meniere’s disease. Laryngoscope. 2007;117(3):415-20.
• Naganawa S, Yamazaki M, Kawai H, Bokura K, Sone M, Nakashima T. Visualization of endolymphatic hydrops in Ménière’s disease with single-dose intravenous gadolinium-based contrast media using heavily T(2)-weighted 3D-FLAIR. Magn Reson Med Sci. 2010;9(4):237-42.
• Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci. 2021;41(6):1105-15.
• Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9.
• Cao Z, Chen F, Grais EM, et al. Machine Learning in Diagnosing Middle Ear Disorders Using Tympanic Membrane Images: A Meta-Analysis. Laryngoscope. 2023;133(4):732-41.
• van der Lubbe MFJA, Vaidyanathan A, de Wit M, et al. A non-invasive, automated diagnosis of Menière’s disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study. Radiol Med. 2022;127(1):72-82.
• Gürkov R, Berman A, Dietrich O, et al. MR volumetric assessment of endolymphatic hydrops. Eur Radiol. 2015;25(2):585-95.
• Bragg PG, Norton BM, Petrak MR, et al. Application of supervised machine learning algorithms for the evaluation of utricular function on patients with Meniere’s disease: utilizing subjective visual vertical and ocular-vestibular-evoked myogenic potentials. Acta Otolaryngol. 2023;143(4):262-73.
• Cho YS, Cho K, Park CJ, et al. Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation. Sci Rep. 2020;10(1):7003.
• Park CJ, Cho YS, Chung MJ, et al. A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study. J Med Internet Res. 2021;23(9):e29678.
• Liu YW, Kao SL, Wu HT, Liu TC, Fang TY, Wang PC. Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière’s disease. Acta Otolaryngol. 2020;140(3):230-5.
• Shew M, Wichova H, Bur A, et al. MicroRNA Profiling as a Methodology to Diagnose Ménière’s Disease: Potential Application of Machine Learning. Otolaryngol Head Neck Surg. 2021;164(2):399-406.
• Zou J, Pyykkö I, Bjelke B, Dastidar P, Toppila E. Communication between the perilymphatic scalae and spiral ligament visualized by in vivo MRI. Audiol Neurootol. 2005;10(3):145-52.
• Naganawa S, Satake H, Kawamura M, Fukatsu H, Sone M, Nakashima T. Separate visualization of endolymphatic space, perilymphatic space and bone by a single pulse sequence; 3D-inversion recovery imaging utilizing real reconstruction after intratympanic Gd-DTPA administration at 3 Tesla. Eur Radiol. 2008;18(5):920-4.