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Chiang JCB, Roy M, Kim J, Markoulli M, Krishnan AV. In-vivo corneal confocal microscopy: Imaging analysis, biological insights and future directions. Commun Biol 2023; 6:652. [PMID: 37336941 DOI: 10.1038/s42003-023-05005-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/31/2023] [Indexed: 06/21/2023] Open
Abstract
In-vivo corneal confocal microscopy is a powerful imaging technique which provides clinicians and researcher with the capabilities to observe microstructures at the ocular surfaces in significant detail. In this Mini Review, the optics and image analysis methods with the use of corneal confocal microscopy are discussed. While novel insights of neuroanatomy and biology of the eyes, particularly the ocular surface, have been provided by corneal confocal microscopy, some debatable elements observed using this technique remain and these are explored in this Mini Review. Potential improvements in imaging methodology and instrumentation are also suggested.
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Affiliation(s)
- Jeremy Chung Bo Chiang
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, NSW, UK
| | - Maitreyee Roy
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Juno Kim
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Maria Markoulli
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Arun V Krishnan
- School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia.
- Department of Neurology, Prince of Wales Hospital, Sydney, NSW, Australia.
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Abstract
Fluorescence microscopy has represented a crucial technique to explore the cellular and molecular mechanisms in the field of biomedicine. However, the conventional one-photon microscopy exhibits many limitations when living samples are imaged. The new technologies, including two-photon microscopy (2PM), have considerably improved the in vivo study of pathophysiological processes, allowing the investigators to overcome the limits displayed by previous techniques. 2PM enables the real-time intravital imaging of the biological functions in different organs at cellular and subcellular resolution thanks to its improved laser penetration and less phototoxicity. The development of more sensitive detectors and long-wavelength fluorescent dyes as well as the implementation of semi-automatic software for data analysis allowed to gain insights in essential physiological functions, expanding the frontiers of cellular and molecular imaging. The future applications of 2PM are promising to push the intravital microscopy beyond the existing limits. In this review, we provide an overview of the current state-of-the-art methods of intravital microscopy, focusing on the most recent applications of 2PM in kidney physiology.
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Bakken IM, Jackson CJ, Utheim TP, Villani E, Hamrah P, Kheirkhah A, Nielsen E, Hau S, Lagali NS. The use of in vivo confocal microscopy in fungal keratitis - Progress and challenges. Ocul Surf 2022; 24:103-118. [PMID: 35278721 DOI: 10.1016/j.jtos.2022.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/02/2022] [Accepted: 03/02/2022] [Indexed: 01/02/2023]
Abstract
Fungal keratitis (FK) is a serious and sight-threatening corneal infection with global reach. The need for prompt diagnosis is paramount, as a delay in initiation of treatment could lead to irreversible vision loss. Current "gold standard" diagnostic methods, namely corneal smear and culture, have limitations due to diagnostic insensitivity and their time-consuming nature. PCR is a newer, complementary method used in the diagnosis of fungal keratitis, whose results are also sample-dependent. In vivo confocal microscopy (IVCM) is a promising complementary diagnostic method of increasing importance as it allows non-invasive real-time direct visualization of potential fungal pathogens and manifesting infection directly in the patient's cornea. In numerous articles and case reports, FK diagnosis by IVCM has been evaluated, and different features, approaches, sensitivity/specificity, and limitations have been noted. Here, we provide an up-to-date, comprehensive review of the current literature and present the authors' combined recommendations for fungal identification in IVCM images, while also looking to the future of FK assessment by IVCM using artificial intelligence methods.
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Affiliation(s)
- Ingvild M Bakken
- Department of Ophthalmology, Sørlandet Hospital Arendal, Arendal, Norway
| | - Catherine J Jackson
- Ifocus Eye Clinic, Haugesund, Norway; Department of Oral Biology, Faculty of Dentistry, University of Oslo, Oslo, Norway
| | - Tor P Utheim
- Department of Ophthalmology, Sørlandet Hospital Arendal, Arendal, Norway; Department of Ophthalmology, Oslo University Hospital, Oslo, Norway; Department of Health and Nursing Science, The Faculty of Health and Sport Sciences, University of Agder, Grimstad, Norway
| | - Edoardo Villani
- Department of Clinical Science and Community Health, University of Milan, Italy; Eye Clinic San Giuseppe Hospital, IRCCS Multimedica, Milan, Italy
| | - Pedram Hamrah
- Cornea Service, New England Eye Center, Department of Ophthalmology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - Ahmad Kheirkhah
- Department of Ophthalmology, Long School of Medicine, UT Health San Antonio, San Antonio, TX, USA
| | - Esben Nielsen
- Department of Ophthalmology, Aarhus University Hospital, Aarhus, Denmark
| | - Scott Hau
- Department of External Disease, NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom; UCL Institute of Ophthalmology, London, United Kingdom
| | - Neil S Lagali
- Department of Ophthalmology, Sørlandet Hospital Arendal, Arendal, Norway; Division of Ophthalmology, Institute for Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
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Xu F, Jiang L, He W, Huang G, Hong Y, Tang F, Lv J, Lin Y, Qin Y, Lan R, Pan X, Zeng S, Li M, Chen Q, Tang N. The Clinical Value of Explainable Deep Learning for Diagnosing Fungal Keratitis Using in vivo Confocal Microscopy Images. Front Med (Lausanne) 2021; 8:797616. [PMID: 34970572 PMCID: PMC8712475 DOI: 10.3389/fmed.2021.797616] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 11/25/2021] [Indexed: 12/14/2022] Open
Abstract
Background: Artificial intelligence (AI) has great potential to detect fungal keratitis using in vivo confocal microscopy images, but its clinical value remains unclarified. A major limitation of its clinical utility is the lack of explainability and interpretability. Methods: An explainable AI (XAI) system based on Gradient-weighted Class Activation Mapping (Grad-CAM) and Guided Grad-CAM was established. In this randomized controlled trial, nine ophthalmologists (three expert ophthalmologists, three competent ophthalmologists, and three novice ophthalmologists) read images in each of the conditions: unassisted, AI-assisted, or XAI-assisted. In unassisted condition, only the original IVCM images were shown to the readers. AI assistance comprised a histogram of model prediction probability. For XAI assistance, explanatory maps were additionally shown. The accuracy, sensitivity, and specificity were calculated against an adjudicated reference standard. Moreover, the time spent was measured. Results: Both forms of algorithmic assistance increased the accuracy and sensitivity of competent and novice ophthalmologists significantly without reducing specificity. The improvement was more pronounced in XAI-assisted condition than that in AI-assisted condition. Time spent with XAI assistance was not significantly different from that without assistance. Conclusion: AI has shown great promise in improving the accuracy of ophthalmologists. The inexperienced readers are more likely to benefit from the XAI system. With better interpretability and explainability, XAI-assistance can boost ophthalmologist performance beyond what is achievable by the reader alone or with black-box AI assistance.
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Affiliation(s)
- Fan Xu
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Li Jiang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Wenjing He
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Guangyi Huang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yiyi Hong
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Fen Tang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Jian Lv
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yunru Lin
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yikun Qin
- China-ASEAN Information Harbor Co., Ltd., Nanning, China
| | - Rushi Lan
- Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China
| | - Xipeng Pan
- Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Siming Zeng
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Min Li
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Qi Chen
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Ningning Tang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Research Center of Ophthalmology, Guangxi Academy of Medical Sciences & Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
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Xu F, Qin Y, He W, Huang G, Lv J, Xie X, Diao C, Tang F, Jiang L, Lan R, Cheng X, Xiao X, Zeng S, Chen Q, Cui L, Li M, Tang N. A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images. PLoS One 2021; 16:e0252653. [PMID: 34081736 PMCID: PMC8174724 DOI: 10.1371/journal.pone.0252653] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/19/2021] [Indexed: 01/10/2023] Open
Abstract
Purpose Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in computer assisted diagnosis. This study aimed to develop deep transfer learning models for automatic detection of activated dendritic cells and inflammatory cells using in vivo confocal microscopy images. Methods A total of 3453 images was used to train the models. External validation was performed on an independent test set of 558 images. A ground-truth label was assigned to each image by a panel of cornea specialists. We constructed a deep transfer learning network that consisted of a pre-trained network and an adaptation layer. In this work, five pre-trained networks were considered, namely VGG-16, ResNet-101, Inception V3, Xception, and Inception-ResNet V2. The performance of each transfer network was evaluated by calculating the area under the curve (AUC) of receiver operating characteristic, accuracy, sensitivity, specificity, and G mean. Results The best performance was achieved by Inception-ResNet V2 transfer model. In the validation set, the best transfer system achieved an AUC of 0.9646 (P<0.001) in identifying activated dendritic cells (accuracy, 0.9319; sensitivity, 0.8171; specificity, 0.9517; and G mean, 0.8872), and 0.9901 (P<0.001) in identifying inflammatory cells (accuracy, 0.9767; sensitivity, 0.9174; specificity, 0.9931; and G mean, 0.9545). Conclusions The deep transfer learning models provide a completely automated analysis of corneal inflammatory cellular components with high accuracy. The implementation of such models would greatly benefit the management of corneal diseases and reduce workloads for ophthalmologists.
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Affiliation(s)
- Fan Xu
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Yikun Qin
- China-ASEAN Information Harbor, Nanning, Guangxi, China
| | - Wenjing He
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Guangyi Huang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Jian Lv
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Xinxin Xie
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Chunli Diao
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Fen Tang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Li Jiang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Rushi Lan
- Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics, Guilin University of Electronic Technology, Guilin, Guangxi, China
| | - Xiaohui Cheng
- Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology, Guilin, Guangxi, China
| | - Xiaolin Xiao
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Siming Zeng
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Qi Chen
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Ling Cui
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Min Li
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
- * E-mail: (ML); (NT)
| | - Ningning Tang
- Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology, Ophthalmology Department, the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
- * E-mail: (ML); (NT)
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