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Zhang H, Zhang H, Jiang M, Li J, Li J, Zhou H, Song X, Fan X. Radiomics in ophthalmology: a systematic review. Eur Radiol 2024:10.1007/s00330-024-10911-4. [PMID: 39033472 DOI: 10.1007/s00330-024-10911-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/03/2024] [Accepted: 05/12/2024] [Indexed: 07/23/2024]
Abstract
BACKGROUND Radiomics holds great potential in medical image analysis for various ophthalmic diseases. In recent times, there have been numerous endeavors in this area of research. This systematic review aims to provide a comprehensive assessment of the strengths and limitations of radiomics in ophthalmology. METHOD Conforming to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, we conducted a systematic review with a pre-registered protocol (PROSPERO: CRD42023446317). We explored the PubMed, Embase, and Cochrane databases for original studies on this topic and made a comprehensive descriptive integration. Furthermore, the included studies underwent quality assessment by the radiomics quality score (RQS). RESULTS A total of 41 articles from an initial search of 227 studies were finally selected for further analysis. These articles included research across five disease categories and covered seven imaging modalities. The radiomics models demonstrated robust performance, with area under the curve (AUC) values mostly falling within 0.7-1.0. The moderate RQS (mean score: 11.17/36) indicated that most studies were retrospectively, single-center analyses without external validation. CONCLUSIONS Radiomics holds promising utility in the field of ophthalmology, assisting diagnosis, early-stage screening, and prognostication of treatment response. Artificial intelligence algorithms significantly contribute to the construction of radiomics models in ophthalmology. This study highlights the strengths and challenges of radiomics in ophthalmology and suggests potential avenues for future improvement. CLINICAL RELEVANCE STATEMENT Radiomics represents a valuable approach for generating innovative imaging markers, enhancing efficiency in clinical diagnosis and treatment, and aiding decision-making in clinical contexts of many ophthalmic diseases, thereby improving overall patient prognosis. KEY POINTS Radiomics has attracted extensive attention in the field of ophthalmology. Articles included five disease categories over seven imaging modalities, consistently yielding AUCs mostly above 0.7. Current research has few prospective and multi-center studies, underlining the necessity for future high-quality studies.
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Affiliation(s)
- Haiyang Zhang
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Huijie Zhang
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Mengda Jiang
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiaxin Li
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Jipeng Li
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Huifang Zhou
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China.
| | - Xuefei Song
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China.
| | - Xianqun Fan
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China.
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Sio SWC, Chan BKT, Aljufairi FMAA, Sebastian JU, Lai KKH, Tham CCY, Pang CP, Chong KKL. Diagnostic methods for dysthyroid optic neuropathy: A systematic review and analysis. Surv Ophthalmol 2024; 69:403-410. [PMID: 38007201 DOI: 10.1016/j.survophthal.2023.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 11/27/2023]
Abstract
Diagnosis of dysthyroid optic neuropathy (DON) typically relies on a set of diagnostic clinical features, including decreased visual acuity, impaired color vision, presence of relative afferent pupillary defect, optic disc swelling and ancillary tests including visual field (VF), pattern visual evoked potential (pVEP), and apical crowding or optic nerve stretching on neuroimaging. We summarize various diagnostic methods to establish or rule out DON. A total of 95 studies (involving 4619 DON eyes) met the inclusion criteria. All of the studies considered clinical features as evidence of DON, while most of the studies confirmed DON diagnosis by combining clinical features with ancillary tests. Forty studies (42.1%) used at least 2 out of the 3 tests (VF, pVEP and neuroimaging) and 13 studies (13.7%) used all 3 tests to diagnose DON. In 64 % of the published studies regarding DON, the diagnostic methods of DON were not specified. It is important to note the limitations of relying solely on clinical features for diagnosing DON. On the other hand, since some eyes with optic neuropathy can be normal in one ancillary test, but abnormal in another, using more than one ancillary test to aid diagnosis is crucial and should be interpreted in correlation with clinical features. We found that the diagnostic methods of DON in most studies involved using a combination of specific clinical features and at least 2 ancillary tests.
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Affiliation(s)
- Stella Weng Chi Sio
- Hong Kong Eye Hospital, Hong Kong SAR, China; Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Benson Kang To Chan
- Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Fatema Mohamed Ali Abdulla Aljufairi
- Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China; Department of Ophthalmology, Salmaniya Medical Complex, Government Hospitals, Bahrain
| | - Jake Uy Sebastian
- Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China; Department of Ophthalmology, Vicente Sotto Memorial Medical Centre, Cebu City, the Philippines
| | - Kenneth Ka Hei Lai
- Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China
| | - Clement Chee Yung Tham
- Hong Kong Eye Hospital, Hong Kong SAR, China; Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Chi Pui Pang
- Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Kelvin Kam Lung Chong
- Department of Ophthalmology and Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China.
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Lin LY, Zhou P, Shi M, Lu JE, Jeon S, Kim D, Liu JM, Wang M, Do S, Lee NG. A Deep Learning Model for Screening Computed Tomography Imaging for Thyroid Eye Disease and Compressive Optic Neuropathy. OPHTHALMOLOGY SCIENCE 2024; 4:100412. [PMID: 38046559 PMCID: PMC10692956 DOI: 10.1016/j.xops.2023.100412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 12/05/2023]
Abstract
Purpose Thyroid eye disease (TED) is an autoimmune condition with an array of clinical manifestations, which can be complicated by compressive optic neuropathy. It is important to identify patients with TED early to ensure close monitoring and treatment to prevent potential permanent disability or vision loss. Deep learning artificial intelligence (AI) algorithms have been utilized in ophthalmology and in other fields of medicine to detect disease. This study aims to introduce a deep learning model to evaluate orbital computed tomography (CT) images for the presence of TED and potential compressive optic neuropathy. Design Retrospective review and deep learning algorithm modeling. Subjects Patients with TED with dedicated orbital CT scans and with an examination by an oculoplastic surgeon over a 10-year period at a single academic institution. Patients with no TED and normal CTs were used as normal controls. Those with other diagnoses, such as tumors or other inflammatory processes, were excluded. Methods Orbital CTs were preprocessed and adopted for the Visual Geometry Group-16 network to distinguish patients with no TED, mild TED, and severe TED with compressive optic neuropathy. The primary model included training and testing of all 3 conditions. Binary model performance was also evaluated. An oculoplastic surgeon was also similarly tested with single and serial images for comparison. Main Outcome Measures Accuracy of deep learning model discernment of region of interest for CT scans to distinguish TED versus normal control, as well as TED with clinical signs of optic neuropathy. Results A total of 1187 photos from 141 patients were used to develop the AI model. The primary model trained on patients with no TED, mild TED, and severe TED had 89.5% accuracy (area under the curve: range, 0.96-0.99) in distinguishing patients with these clinical categories. In comparison, testing of an oculoplastic surgeon in these 3 categories showed decreased accuracy (70.0% accuracy in serial image testing). Conclusions The deep learning model developed in the study can accurately detect TED and further detect TED with clinical signs of optic neuropathy based on orbital CT. The model proved superior compared with human expert grading. With further optimization and validation, this TED deep learning model could help guide frontline health care providers in the detection of TED and help stratify the urgency of a referral to an oculoplastic surgeon and endocrinologist. Financial Disclosures The authors have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Lisa Y. Lin
- Department of Ophthalmology, Ophthalmic Plastic Surgery Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Paul Zhou
- Department of Ophthalmology, Gavin Herbert Eye Institute, University of California Irvine, Irvine, California
| | - Min Shi
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Jonathan E. Lu
- Department of Ophthalmology, Ophthalmic Plastic Surgery Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Soomin Jeon
- Department of Information Sciences and Mathematics, Dong-A University, Busan, Republic of Korea
| | - Doyun Kim
- Data Science, Athenahealth, Watertown, Massachusetts
| | - Josephine M. Liu
- Department of Radiology, Lab of Medical Imaging and Computation, Massachusetts General Brigham and Harvard Medical School, Boston, Massachusetts
| | - Mengyu Wang
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Synho Do
- Department of Radiology, Lab of Medical Imaging and Computation, Massachusetts General Brigham and Harvard Medical School, Boston, Massachusetts
| | - Nahyoung Grace Lee
- Department of Ophthalmology, Ophthalmic Plastic Surgery Service, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
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Zhang H, Lu T, Liu Y, Jiang M, Wang Y, Song X, Fan X, Zhou H. Application of Quantitative MRI in Thyroid Eye Disease: Imaging Techniques and Clinical Practices. J Magn Reson Imaging 2023. [PMID: 37974477 DOI: 10.1002/jmri.29114] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 10/19/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023] Open
Abstract
Thyroid eye disease (TED) is a complex autoimmune disorder that impairs various orbital structures, leading to cosmetic damage and vision loss. Magnetic resonance imaging (MRI) is a fundamental diagnostic tool utilized in clinical settings of TED, for its accurate demonstration of orbital lesions and indication of disease conditions. The application of quantitative MRI has brought a new prospect to the management and research of TED, offering more detailed information on morphological and functional changes in the orbit. Therefore, many researchers concentrated on the implementation of different quantitative MRI techniques on TED for the exploration of clinical practices. Despite the abundance of studies utilizing quantitative MRI in TED, there remain considerable barriers and disputes on the best exploitation of this tool. This could possibly be attributed to the complexity of TED and the fast development of MRI techniques. It is necessary that clinical and radiological aspects of quantitative MRI in TED be better integrated into comprehensive insights. Hence, this review traces back 30 years of publications regarding quantitative MRI utilized in TED and elucidates this promising application in the facets of imaging techniques and clinical practices. We believe that a deeper understanding of the application of quantitative MRI in TED will enhance the efficacy of the multidisciplinary management of TED. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Haiyang Zhang
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Ting Lu
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Yuting Liu
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Mengda Jiang
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yishi Wang
- MR Collaboration, Siemens Healthineers Ltd., Beijing, China
| | - Xuefei Song
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Xianqun Fan
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
| | - Huifang Zhou
- Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
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Wu WT, Lin CY, Shu YC, Shen PC, Lin TY, Chang KV, Özçakar L. The Potential of Ultrasound Radiomics in Carpal Tunnel Syndrome Diagnosis: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2023; 13:3280. [PMID: 37892101 PMCID: PMC10606315 DOI: 10.3390/diagnostics13203280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
Background: Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy for which ultrasound imaging has recently emerged as a valuable diagnostic tool. This meta-analysis aims to investigate the role of ultrasound radiomics in the diagnosis of CTS and compare it with other diagnostic approaches. Methods: We conducted a comprehensive search of electronic databases from inception to September 2023. The included studies were assessed for quality using the Quality Assessment Tool for Diagnostic Accuracy Studies. The primary outcome was the diagnostic performance of ultrasound radiomics compared to radiologist evaluation for diagnosing CTS. Results: Our meta-analysis included five observational studies comprising 840 participants. In the context of radiologist evaluation, the combined statistics for sensitivity, specificity, and diagnostic odds ratio were 0.78 (95% confidence interval (CI), 0.71 to 0.83), 0.72 (95% CI, 0.59 to 0.81), and 9 (95% CI, 5 to 15), respectively. In contrast, the ultrasound radiomics training mode yielded a combined sensitivity of 0.88 (95% CI, 0.85 to 0.91), a specificity of 0.88 (95% CI, 0.84 to 0.92), and a diagnostic odds ratio of 58 (95% CI, 38 to 87). Similarly, the ultrasound radiomics testing mode demonstrated an aggregated sensitivity of 0.85 (95% CI, 0.78 to 0.89), a specificity of 0.80 (95% CI, 0.73 to 0.85), and a diagnostic odds ratio of 22 (95% CI, 12 to 41). Conclusions: In contrast to assessments by radiologists, ultrasound radiomics exhibited superior diagnostic performance in detecting CTS. Furthermore, there was minimal variability in the diagnostic accuracy between the training and testing sets of ultrasound radiomics, highlighting its potential as a robust diagnostic tool in CTS.
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Affiliation(s)
- Wei-Ting Wu
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 10048, Taiwan;
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Bei-Hu Branch, Taipei 10845, Taiwan
| | - Che-Yu Lin
- Institute of Applied Mechanics, College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (C.-Y.L.); (Y.-C.S.)
| | - Yi-Chung Shu
- Institute of Applied Mechanics, College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (C.-Y.L.); (Y.-C.S.)
| | - Peng-Chieh Shen
- Department of Physical Medicine and Rehabilitation, Lo-Hsu Medical Foundation, Inc., Lotung Poh-Ai Hospital, Yilan 26546, Taiwan; (P.-C.S.); (T.-Y.L.)
| | - Ting-Yu Lin
- Department of Physical Medicine and Rehabilitation, Lo-Hsu Medical Foundation, Inc., Lotung Poh-Ai Hospital, Yilan 26546, Taiwan; (P.-C.S.); (T.-Y.L.)
| | - Ke-Vin Chang
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 10048, Taiwan;
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Bei-Hu Branch, Taipei 10845, Taiwan
- Center for Regional Anesthesia and Pain Medicine, Wang-Fang Hospital, Taipei Medical University, Taipei 11600, Taiwan
| | - Levent Özçakar
- Department of Physical and Rehabilitation Medicine, Hacettepe University Medical School, Ankara 06100, Turkey;
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Bao XL, Sun YJ, Zhan X, Li GY. Orbital and eyelid diseases: The next breakthrough in artificial intelligence? Front Cell Dev Biol 2022; 10:1069248. [PMID: 36467418 PMCID: PMC9716028 DOI: 10.3389/fcell.2022.1069248] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/08/2022] [Indexed: 12/07/2023] Open
Abstract
Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.
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Affiliation(s)
- Xiao-Li Bao
- Department of Ophthalmology, Second Hospital of Jilin University, Changchun, China
| | - Ying-Jian Sun
- Department of Ophthalmology, Second Hospital of Jilin University, Changchun, China
| | - Xi Zhan
- Department of Engineering, The Army Engineering University of PLA, Nanjing, China
| | - Guang-Yu Li
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, China
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