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Kryszan K, Wylęgała A, Kijonka M, Potrawa P, Walasz M, Wylęgała E, Orzechowska-Wylęgała B. Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review. Diagnostics (Basel) 2024; 14:694. [PMID: 38611606 PMCID: PMC11011861 DOI: 10.3390/diagnostics14070694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/13/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part of the body, necessitates the precise diagnoses of various conditions. Convolutional neural networks (CNNs), a key component of deep learning, are a powerful tool for image data analysis. This review highlights AI applications in diagnosing keratitis, dry eye disease, and diabetic corneal neuropathy. It discusses the potential of AI in detecting infectious agents, analyzing corneal nerve morphology, and identifying the subtle changes in nerve fiber characteristics in diabetic corneal neuropathy. However, challenges still remain, including limited datasets, overfitting, low-quality images, and unrepresentative training datasets. This review explores augmentation techniques and the importance of feature engineering to address these challenges. Despite the progress made, challenges are still present, such as the "black-box" nature of AI models and the need for explainable AI (XAI). Expanding datasets, fostering collaborative efforts, and developing user-friendly AI tools are crucial for enhancing the acceptance and integration of AI into clinical practice.
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Affiliation(s)
- Katarzyna Kryszan
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Adam Wylęgała
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Health Promotion and Obesity Management, Pathophysiology Department, Medical University of Silesia in Katowice, 40-752 Katowice, Poland
| | - Magdalena Kijonka
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Patrycja Potrawa
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Mateusz Walasz
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Edward Wylęgała
- Chair and Clinical Department of Ophthalmology, School of Medicine in Zabrze, Medical University of Silesia in Katowice, District Railway Hospital, 40-760 Katowice, Poland; (A.W.); (M.K.); (E.W.)
- Department of Ophthalmology, District Railway Hospital in Katowice, 40-760 Katowice, Poland; (P.P.); (M.W.)
| | - Bogusława Orzechowska-Wylęgała
- Department of Pediatric Otolaryngology, Head and Neck Surgery, Chair of Pediatric Surgery, Medical University of Silesia, 40-760 Katowice, Poland;
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Herrera-Pereda R, Taboada Crispi A, Babin D, Philips W, Holsbach Costa M. A Review On digital image processing techniques for in-Vivo confocal images of the cornea. Med Image Anal 2021; 73:102188. [PMID: 34340102 DOI: 10.1016/j.media.2021.102188] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 06/12/2021] [Accepted: 07/16/2021] [Indexed: 12/27/2022]
Abstract
This work reviews the scientific literature regarding digital image processing for in vivo confocal microscopy images of the cornea. We present and discuss a selection of prominent techniques designed for semi- and automatic analysis of four areas of the cornea (epithelium, sub-basal nerve plexus, stroma and endothelium). The main context is image enhancement, detection of structures of interest, and quantification of clinical information. We have found that the preprocessing stage lacks of quantitative studies regarding the quality of the enhanced image, or its effects in subsequent steps of the image processing. Threshold values are widely used in the reviewed methods, although generally, they are selected empirically and manually. The image processing results are evaluated in many cases through comparison with gold standards not widely accepted. It is necessary to standardize values to be quantified in terms of sensitivity and specificity of methods. Most of the reviewed studies do not show an estimation of the computational cost of the image processing. We conclude that reliable, automatic, computer-assisted image analysis of the cornea is still an open issue, constituting an interesting and worthwhile area of research.
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Affiliation(s)
- Raidel Herrera-Pereda
- Departamento de Bioinformática, Facultad de Ciencias y Tecnologías Computacionales, Universidad de las Ciencias Informáticas (UCI), Carretera a San Antonio de los Baños Km 2 1/2, Torrens, Boyeros, La Habana, Cuba; TELIN-IPI, Ghent University - imec, Belgium.
| | - Alberto Taboada Crispi
- Centro de Investigaciones de la Informática, Universidad Central "Marta Abreu" de Las Villas (UCLV), Carretera a Camajuaní, km 5 1/2, Santa Clara, VC, CP 54830, Cuba
| | | | | | - Márcio Holsbach Costa
- Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, SC, Brazil
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Al-Fahdawi S, Qahwaji R, Al-Waisy AS, Ipson S, Malik RA, Brahma A, Chen X. A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 135:151-166. [PMID: 27586488 DOI: 10.1016/j.cmpb.2016.07.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 06/09/2016] [Accepted: 07/22/2016] [Indexed: 06/06/2023]
Abstract
Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the nerve structures can assist the early diagnosis of this disease. This paper proposes a robust, fast and fully automatic nerve segmentation and morphometric parameter quantification system for corneal confocal microscope images. The segmentation part consists of three main steps. First, a preprocessing step is applied to enhance the visibility of the nerves and remove noise using anisotropic diffusion filtering, specifically a Coherence filter followed by Gaussian filtering. Second, morphological operations are applied to remove unwanted objects in the input image such as epithelial cells and small nerve segments. Finally, an edge detection step is applied to detect all the nerves in the input image. In this step, an efficient algorithm for connecting discontinuous nerves is proposed. In the morphometric parameters quantification part, a number of features are extracted, including thickness, tortuosity and length of nerve, which may be used for the early diagnosis of diabetic polyneuropathy and when planning Laser-Assisted in situ Keratomileusis (LASIK) or Photorefractive keratectomy (PRK). The performance of the proposed segmentation system is evaluated against manually traced ground-truth images based on a database consisting of 498 corneal sub-basal nerve images (238 are normal and 260 are abnormal). In addition, the robustness and efficiency of the proposed system in extracting morphometric features with clinical utility was evaluated in 919 images taken from healthy subjects and diabetic patients with and without neuropathy. We demonstrate rapid (13 seconds/image), robust and effective automated corneal nerve quantification. The proposed system will be deployed as a useful clinical tool to support the expertise of ophthalmologists and save the clinician time in a busy clinical setting.
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Affiliation(s)
- Shumoos Al-Fahdawi
- School of Electrical Engineering and Computer Science, University of Bradford, Bradford, UK.
| | - Rami Qahwaji
- School of Electrical Engineering and Computer Science, University of Bradford, Bradford, UK
| | - Alaa S Al-Waisy
- School of Electrical Engineering and Computer Science, University of Bradford, Bradford, UK
| | - Stanley Ipson
- School of Electrical Engineering and Computer Science, University of Bradford, Bradford, UK
| | - Rayaz A Malik
- Division of Medicine, Weill Cornell Medical College in Qatar, Doha, Qatar; Centre for Endocrinology and Diabetes, Institute of Human Development, University of Manchester and the Manchester Royal Infirmary, Central Manchester Hospital Foundation Trust, Manchester, UK
| | - Arun Brahma
- Manchester Royal Eye Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
| | - Xin Chen
- Centre for Imaging Sciences, Institute of Population Health, University of Manchester, Manchester, UK
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Sharif M, Qahwaji R, Ipson S, Brahma A. Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.07.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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