Artificial Intelligence in Temporal Bone Imaging: A Systematic Review

被引:0
作者
Spinos, Dimitrios [1 ,2 ]
Martinos, Anastasios [3 ]
Petsiou, Dioni-Pinelopi [3 ]
Mistry, Nina [4 ]
Garas, George [5 ,6 ]
机构
[1] South Warwickshire NHS Fdn Trust, Warwick, England
[2] Univ Hosp Birmingham NHS Fdn Trust, 76 Woolacombe Lodge Rd, Birmingham B296PY, West Midlands, England
[3] Natl & Kapodistrian Univ Athens, Sch Med, Athens, Greece
[4] Gloucestershire Hosp NHS Fdn Trust, ENT & Head & Neck Surg, Gloucester, England
[5] Imperial Coll London, St Marys Hosp, Dept Surg & Canc, Surg Innovat Ctr, London, England
[6] Athens Med Ctr, Marousi & Psychiko Clin, Athens, Greece
关键词
artificial intelligence; lateral skull base; otology; machine learning; review; VESTIBULAR SCHWANNOMA; DIFFERENTIAL-DIAGNOSIS; OTITIS-MEDIA;
D O I
10.1002/lary.31809
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
ObjectiveThe human temporal bone comprises more than 30 identifiable anatomical components. With the demand for precise image interpretation in this complex region, the utilization of artificial intelligence (AI) applications is steadily increasing. This systematic review aims to highlight the current role of AI in temporal bone imaging.Data SourcesA Systematic Review of English Publications searching MEDLINE (PubMed), COCHRANE Library, and EMBASE.Review MethodsThe search algorithm employed consisted of key items such as 'artificial intelligence,' 'machine learning,' 'deep learning,' 'neural network,' 'temporal bone,' and 'vestibular schwannoma.' Additionally, manual retrieval was conducted to capture any studies potentially missed in our initial search. All abstracts and full texts were screened based on our inclusion and exclusion criteria.ResultsA total of 72 studies were included. 95.8% were retrospective and 88.9% were based on internal databases. Approximately two-thirds involved an AI-to-human comparison. Computed tomography (CT) was the imaging modality in 54.2% of the studies, with vestibular schwannoma (VS) being the most frequent study item (37.5%). Fifty-eight out of 72 articles employed neural networks, with 72.2% using various types of convolutional neural network models. Quality assessment of the included publications yielded a mean score of 13.6 +/- 2.5 on a 20-point scale based on the CONSORT-AI extension.ConclusionCurrent research data highlight AI's potential in enhancing diagnostic accuracy with faster results and decreased performance errors compared to those of clinicians, thus improving patient care. However, the shortcomings of the existing research, often marked by heterogeneity and variable quality, underscore the need for more standardized methodological approaches to ensure the consistency and reliability of future data.Level of EvidenceNA Laryngoscope, 2024 This systematic review investigates the current state of the art in the artificial intelligence applications in temporal bone imaging.image
引用
收藏
页码:973 / 981
页数:9
相关论文
共 85 条
[1]   The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review [J].
Amanian, Ameen ;
Heffernan, Austin ;
Ishii, Masaru ;
Creighton, Francis X. ;
Thamboo, Andrew .
OTOLARYNGOLOGY-HEAD AND NECK SURGERY, 2023, 169 (01) :21-30
[2]   Radiomics of high-resolution computed tomography for the differentiation between cholesteatoma and middle ear inflammation: effects of post-reconstruction methods in a dual-center study [J].
Arendt, Christophe T. ;
Leithner, Doris ;
Mayerhoefer, Marius E. ;
Gibbs, Peter ;
Czerny, Christian ;
Arnoldner, Christoph ;
Burck, Iris ;
Leinung, Martin ;
Tanyildizi, Yasemin ;
Lenga, Lukas ;
Martin, Simon S. ;
Vogl, Thomas J. ;
Schernthaner, Ruediger E. .
EUROPEAN RADIOLOGY, 2021, 31 (06) :4071-4078
[3]  
Ayral M, 2023, EUR REV MED PHARMACO, V27, P215, DOI 10.26355/eurrev_202301_30874
[4]   Improving the quality of reporting of randomized controlled trials - The CONSORT statement [J].
Begg, C ;
Cho, M ;
Eastwood, S ;
Horton, R ;
Moher, D ;
Olkin, I ;
Pitkin, R ;
Rennie, D ;
Schulz, KF ;
Simel, D ;
Stroup, DF .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 1996, 276 (08) :637-639
[5]   Machine Learning for Automated Calculation of Vestibular Schwannoma Volumes [J].
Cass, Nathan D. D. ;
Lindquist, Nathan R. R. ;
Zhu, Qibang ;
Li, Hao ;
Oguz, Ipek ;
Tawfik, Kareem O. O. .
OTOLOGY & NEUROTOLOGY, 2022, 43 (10) :1252-1256
[6]   The CONSORT statement [J].
Cuschieri, Sarah .
SAUDI JOURNAL OF ANAESTHESIA, 2019, 13 :27-30
[7]   A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging [J].
Ding, Andy S. ;
Lu, Alexander ;
Li, Zhaoshuo ;
Sahu, Manish ;
Galaiya, Deepa ;
Siewerdsen, Jeffrey H. ;
Unberath, Mathias ;
Taylor, Russell H. ;
Creighton, Francis X. .
OTOLARYNGOLOGY-HEAD AND NECK SURGERY, 2023, 169 (04) :988-998
[8]   CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation [J].
Dorent, Reuben ;
Kujawa, Aaron ;
Ivory, Marina ;
Bakas, Spyridon ;
Rieke, Nicola ;
Joutard, Samuel ;
Glocker, Ben ;
Cardoso, Jorge ;
Modat, Marc ;
Batmanghelich, Kayhan ;
Belkov, Arseniy ;
Calisto, Maria Baldeon ;
Choi, Jae Won ;
Dawant, Benoit M. ;
Dong, Hexin ;
Escalera, Sergio ;
Fan, Yubo ;
Hansen, Lasse ;
Heinrich, Mattias P. ;
Joshi, Smriti ;
Kashtanova, Victoriya ;
Kim, Hyeon Gyu ;
Kondo, Satoshi ;
Kruse, Christian N. ;
Lai-Yuen, Susana K. ;
Li, Hao ;
Liu, Han ;
Ly, Buntheng ;
Oguz, Ipek ;
Shin, Hyungseob ;
Shirokikh, Boris ;
Su, Zixian ;
Wang, Guotai ;
Wu, Jianghao ;
Xu, Yanwu ;
Yao, Kai ;
Zhang, Li ;
Ourselin, Sebastien ;
Shapey, Jonathan ;
Vercauteren, Tom .
MEDICAL IMAGE ANALYSIS, 2023, 83
[9]   An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic results [J].
Duan, Bo ;
Pan, Li-Li ;
Chen, Wen-Xia ;
Qiao, Zhong-Wei ;
Xu, Zheng-Min .
FRONTIERS IN PEDIATRICS, 2022, 10
[10]  
Duan B, 2022, AM J TRANSL RES, V14, P4728