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Bălășoiu AT, Bălășoiu M, Zlatian OM, Ghenea AE. Bacterial and Fungal Keratitis in a Tertiary Care Hospital from Romania. Microorganisms 2024; 12:787. [PMID: 38674731 PMCID: PMC11052338 DOI: 10.3390/microorganisms12040787] [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: 02/19/2024] [Revised: 03/16/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Infectious keratitis is a significant global problem that can lead to corneal blindness and visual impairments. This study aimed to investigate the etiology of infectious bacterial and fungal keratitis, identify the causative pathogens and their antimicrobial resistance patterns, and analyze the risk factors associated with the development of infectious keratitis. The study was observational and retrospective, involving 226 eyes from 223 patients presented at the Ophthalmology Clinic of the County Clinical Emergency Hospital of Craiova, Romania. The inclusion criteria included corneal ulceration/abscess/infiltrate present on slit-lamp examination and positive microbiological sampling for bacteria or fungi. The study found that the most common causes of infectious keratitis were coagulase-negative staphylococci (35.40%), Staphylococcus aureus (11.06%), and Pseudomonas aeruginosa (14.16%). The Gram-positive bacteria showed high resistance rates to penicillin, moderate rates to gentamycin and clindamycin, and low resistance to chinolones. The Gram-negative bacteria were highly resistant to ampicillin and amoxicillin-clavulanic acid, while third-generation cephalosporins, quinolones, and carbapenems were effective. Systemic antibiotics, such as vancomycine, piperacillin-tazobactam, amikacin, and ceftazidime, show promise against keratitis with low resistance rates, whereas carbapenems and topical aminoglycosides had higher resistance, leaving moxifloxacin as a potential topical option for Gram-positive bacteria and Pseudomonas aeruginosa, albeit with resistance concerns for Klebsiella spp. Although fungal keratitis was rare, Fusarium spp. and Candida albicans were the leading fungal pathogens, with incidences of 2.65% and 2.21%, respectively. Candida albicans was broadly susceptible to most antifungals, while Fusarium solani, Curvularia lunata, and Alternaria alternata exhibited resistance to many antifungals. Amphotericin B and caspofungin can be used as systemic antifungals in fungal keratitis. The study also identified risk factors for keratitis such as ocular trauma (65.92%, OR: 2.5), contact lens wear (11.94%, OR: 1.8), and corneal scarring/leukoma (10.17%, OR: 1.6). Keratitis was more frequent in individuals over 60 years old. The findings of this study have implications for the development of effective diagnostic, therapeutic, and preventive strategies for infectious keratitis.
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Affiliation(s)
- Andrei Theodor Bălășoiu
- Ophtalmology Department, County Clinical Emergency Hospital of Craiova, 200642 Craiova, Romania;
- Ophtalmology Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Maria Bălășoiu
- Medical Laboratory, County Clinical Emergency Hospital of Craiova, 200642 Craiova, Romania; (M.B.); (A.E.G.)
- Microbiology Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Ovidiu Mircea Zlatian
- Medical Laboratory, County Clinical Emergency Hospital of Craiova, 200642 Craiova, Romania; (M.B.); (A.E.G.)
- Microbiology Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Alice Elena Ghenea
- Medical Laboratory, County Clinical Emergency Hospital of Craiova, 200642 Craiova, Romania; (M.B.); (A.E.G.)
- Microbiology Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
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Kuo MT, Hsu BWY, Lin YS, Fang PC, Yu HJ, Hsiao YT, Tseng VS. Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks. Transl Vis Sci Technol 2023; 12:1. [PMID: 37910082 PMCID: PMC10627292 DOI: 10.1167/tvst.12.11.1] [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: 04/27/2023] [Accepted: 10/05/2023] [Indexed: 11/03/2023] Open
Abstract
Purpose For this study, we aimed to determine whether a convolutional neural network (CNN)-based method (based on a feature extractor and an identifier) can be applied to monitor the progression of keratitis while managing suspected microbial keratitis (MK). Methods This multicenter longitudinal cohort study included patients with suspected MK undergoing serial external eye photography at the 5 branches of Chang Gung Memorial Hospital from August 20, 2000, to August 19, 2020. Data were primarily analyzed from January 1 to March 25, 2022. The CNN-based model was evaluated via F1 score and accuracy. The area under the receiver operating characteristic curve (AUROC) was used to measure the precision-recall trade-off. Results The model was trained using 1456 image pairs from 468 patients. In comparing models via only training the identifier, statistically significant higher accuracy (P < 0.05) in models via training both the identifier and feature extractor (full training) was verified, with 408 image pairs from 117 patients. The full training EfficientNet b3-based model showed 90.2% (getting better) and 82.1% (becoming worse) F1 scores, 87.3% accuracy, and 94.2% AUROC for 505 getting better and 272 becoming worse test image pairs from 452 patients. Conclusions A CNN-based approach via deep learning applied in suspected MK can monitor the progress/regress during treatment by comparing external eye image pairs. Translational Relevance The study bridges the gap between the investigation of the state-of-the-art CNN-based deep learning algorithm applied in ocular image analysis and the clinical care of suspected patients with MK.
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Affiliation(s)
- Ming-Tse Kuo
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City, Taiwan
- School of Medicine, Chang Gung University, Taoyuan City, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung City, Taiwan
| | - Benny Wei-Yun Hsu
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yi Sheng Lin
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Po-Chiung Fang
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City, Taiwan
- School of Medicine, Chang Gung University, Taoyuan City, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung City, Taiwan
| | - Hun-Ju Yu
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung City, Taiwan
| | - Yu-Ting Hsiao
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung City, Taiwan
| | - Vincent S. Tseng
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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Tripathi A, Kumar P, Tulsani A, Chakrapani PK, Maiya G, Bhandary SV, Mayya V, Pathan S, Achar R, Acharya UR. Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids. Diagnostics (Basel) 2023; 13:2550. [PMID: 37568913 PMCID: PMC10416860 DOI: 10.3390/diagnostics13152550] [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: 06/20/2023] [Revised: 07/19/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME.
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Affiliation(s)
- Aditya Tripathi
- Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Preetham Kumar
- Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Akshat Tulsani
- Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Pavithra Kodiyalbail Chakrapani
- Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Geetha Maiya
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Sulatha V. Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India
| | - Veena Mayya
- Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Sameena Pathan
- Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Raghavendra Achar
- Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - U. Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia
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Wu J, Yuan Z, Fang Z, Huang Z, Xu Y, Xie W, Wu F, Yao YF. A knowledge-enhanced transform-based multimodal classifier for microbial keratitis identification. Sci Rep 2023; 13:9003. [PMID: 37268729 DOI: 10.1038/s41598-023-36024-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 05/27/2023] [Indexed: 06/04/2023] Open
Abstract
Microbial keratitis, a nonviral corneal infection caused by bacteria, fungi, and protozoa, is an urgent condition in ophthalmology requiring prompt treatment in order to prevent severe complications of corneal perforation and vision loss. It is difficult to distinguish between bacterial and fungal keratitis from image unimodal alone, as the characteristics of the sample images themselves are very close. Therefore, this study aims to develop a new deep learning model called knowledge-enhanced transform-based multimodal classifier that exploited the potential of slit-lamp images along with treatment texts to identify bacterial keratitis (BK) and fungal keratitis (FK). The model performance was evaluated in terms of the accuracy, specificity, sensitivity and the area under the curve (AUC). 704 images from 352 patients were divided into training, validation and testing set. In the testing set, our model reached the best accuracy was 93%, sensitivity was 0.97(95% CI [0.84,1]), specificity was 0.92(95% CI [0.76,0.98]) and AUC was 0.94(95% CI [0.92,0.96]), exceeding the benchmark accuracy of 0.86. The diagnostic average accuracies of BK ranged from 81 to 92%, respectively and those for FK were 89-97%. It is the first study to focus on the influence of disease changes and medication interventions on infectious keratitis and our model outperformed the benchmark models and reaching the state-of-the-art performance.
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Affiliation(s)
- Jianfeng Wu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 31002, China
| | - Zhouhang Yuan
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang Province, 31002, China
| | - Zhengqing Fang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang Province, 31002, China
| | - Zhengxing Huang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang Province, 31002, China
| | - Yesheng Xu
- Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, 310016, China
- Key Laboratory for Corneal Diseases Research of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Wenjia Xie
- Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, 310016, China
- Key Laboratory for Corneal Diseases Research of Zhejiang Province, Hangzhou, Zhejiang Province, China
| | - Fei Wu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang Province, 31002, China.
| | - Yu-Feng Yao
- Department of Ophthalmology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, 310016, China.
- Key Laboratory for Corneal Diseases Research of Zhejiang Province, Hangzhou, Zhejiang Province, China.
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An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images. APPL INTELL 2023; 53:1548-1566. [PMID: 35528131 PMCID: PMC9059700 DOI: 10.1007/s10489-022-03490-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/08/2022] [Indexed: 01/07/2023]
Abstract
Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Often, overt signs indicative of COD do not manifest until the disease has progressed to an advanced stage. However, if COD is detected early, vision impairment can be avoided by early intervention and cost-effective treatment. Ophthalmologists are trained to detect COD by examining certain minute changes in the retina, such as microaneurysms, macular edema, hemorrhages, and alterations in the blood vessels. The range of eye conditions is diverse, and each of these conditions requires a unique patient-specific treatment. Convolutional neural networks (CNNs) have demonstrated significant potential in multi-disciplinary fields, including the detection of a variety of eye diseases. In this study, we combined several preprocessing approaches with convolutional neural networks to accurately detect COD in eye fundus images. To the best of our knowledge, this is the first work that provides a qualitative analysis of preprocessing approaches for COD classification using CNN models. Experimental results demonstrate that CNNs trained on the region of interest segmented images outperform the models trained on the original input images by a substantial margin. Additionally, an ensemble of three preprocessing techniques outperformed other state-of-the-art approaches by 30% and 3%, in terms of Kappa and F 1 scores, respectively. The developed prototype has been extensively tested and can be evaluated on more comprehensive COD datasets for deployment in the clinical setup.
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Zhang Z, Wang Y, Zhang H, Samusak A, Rao H, Xiao C, Abula M, Cao Q, Dai Q. Artificial intelligence-assisted diagnosis of ocular surface diseases. Front Cell Dev Biol 2023; 11:1133680. [PMID: 36875760 PMCID: PMC9981656 DOI: 10.3389/fcell.2023.1133680] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.
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Affiliation(s)
- Zuhui Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Ying Wang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Hongzhen Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Arzigul Samusak
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Huimin Rao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Chun Xiao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Muhetaer Abula
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Qixin Cao
- Huzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang University of Traditional Chinese Medicine, Huzhou, China
| | - Qi Dai
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
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Qasmieh IA, Alquran H, Zyout A, Al-Issa Y, Mustafa WA, Alsalatie M. Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning. Diagnostics (Basel) 2022; 12:diagnostics12123204. [PMID: 36553211 PMCID: PMC9777193 DOI: 10.3390/diagnostics12123204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/26/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency.
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Affiliation(s)
- Isam Abu Qasmieh
- Biomedical Systems and Medical Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
| | - Hiam Alquran
- Biomedical Systems and Medical Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
| | - Ala’a Zyout
- Biomedical Systems and Medical Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
| | - Yazan Al-Issa
- Department of Computer Engineering, Yarmouk University, Irbid 21163, Jordan
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering & Technology, Campus Pauh Putra, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600, Malaysia
- Advanced Computing (AdvComp), Centre of Excellence (CoE), Campus Pauh Putra, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600, Malaysia
- Correspondence:
| | - Mohammed Alsalatie
- The Institute of Biomedical Technology, King Hussein Medical Center, Royal Jordanian Medical Service, Amman 11855, Jordan
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Deep Learning Approach in Image Diagnosis of Pseudomonas Keratitis. Diagnostics (Basel) 2022; 12:diagnostics12122948. [PMID: 36552954 PMCID: PMC9777188 DOI: 10.3390/diagnostics12122948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/15/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022] Open
Abstract
This investigation aimed to explore deep learning (DL) models' potential for diagnosing Pseudomonas keratitis using external eye images. In the retrospective research, the images of bacterial keratitis (BK, n = 929), classified as Pseudomonas (n = 618) and non-Pseudomonas (n = 311) keratitis, were collected. Eight DL algorithms, including ResNet50, DenseNet121, ResNeXt50, SE-ResNet50, and EfficientNets B0 to B3, were adopted as backbone models to train and obtain the best ensemble 2-, 3-, 4-, and 5-DL models. Five-fold cross-validation was used to determine the ability of single and ensemble models to diagnose Pseudomonas keratitis. The EfficientNet B2 model had the highest accuracy (71.2%) of the eight single-DL models, while the best ensemble 4-DL model showed the highest accuracy (72.1%) among the ensemble models. However, no statistical difference was shown in the area under the receiver operating characteristic curve and diagnostic accuracy among these single-DL models and among the four best ensemble models. As a proof of concept, the DL approach, via external eye photos, could assist in identifying Pseudomonas keratitis from BK patients. All the best ensemble models can enhance the performance of constituent DL models in diagnosing Pseudomonas keratitis, but the enhancement effect appears to be limited.
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Jeang L, Tuli SS. Therapy for contact lens-related ulcers. Curr Opin Ophthalmol 2022; 33:282-289. [PMID: 35779052 DOI: 10.1097/icu.0000000000000861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The current review covers the current literature and practice patterns of antimicrobial therapy for contact lens-related microbial keratitis (CLMK). Although the majority of corneal ulcers are bacterial, fungus and acanthamoeba are substantial contributors in CLMK and are harder to treat due to the lack of commercially available topical medications and low efficacy of available topical therapy. RECENT FINDINGS Topical antimicrobials remain the mainstay of therapy for corneal ulcers. Fluoroquinolones may be used as monotherapy for small, peripheral bacterial ulcers. Antibiotic resistance is a persistent problem. Fungal ulcers are less responsive to topical medications and adjunct oral or intrastromal antifungal medications may be helpful. Acanthamoeba keratitis continues to remain a therapeutic challenge but newer antifungal and antiparasitic agents may be helpful adjuncts. Other novel and innovative therapies are being studied currently and show promise. SUMMARY Contact lens-associated microbial keratitis is a significant health issue that can cause vision loss. Treatment remains a challenge but many promising diagnostics and procedures are in the pipeline and offer hope.
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Affiliation(s)
- Lauren Jeang
- Department of Ophthalmology, University of Florida, Gainesville, Florida, USA
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Agrawal S, Chowdhary A, Agarwala S, Mayya V, Kamath S. S. Content-based medical image retrieval system for lung diseases using deep CNNs. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY : AN OFFICIAL JOURNAL OF BHARATI VIDYAPEETH'S INSTITUTE OF COMPUTER APPLICATIONS AND MANAGEMENT 2022; 14:3619-3627. [PMID: 35791434 PMCID: PMC9246357 DOI: 10.1007/s41870-022-01007-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/02/2022] [Indexed: 06/15/2023]
Abstract
Content-based image retrieval (CBIR) systems are designed to retrieve images that are relevant, based on detailed analysis of latent image characteristics, thus eliminating the dependency of natural language tags, text descriptions, or keywords associated with the images. A CBIR system maintains high-level image visuals in the form of feature vectors, which the retrieval engine leverages for similarity-based matching and ranking for a given query image. In this paper, a CBIR system is proposed for the retrieval of medical images (CBMIR) for enabling the early detection and classification of lung diseases based on lung X-ray images. The proposed CBMIR system is built on the predictive power of deep neural models for the identification and classification of disease-specific features using transfer learning based models trained on standard COVID-19 Chest X-ray image datasets. Experimental evaluation on the standard dataset revealed that the proposed approach achieved an improvement of 49.71% in terms of precision, averaging across various distance metrics. Also, an improvement of 26.55% was observed in the area under precision-recall curve (AUPRC) values across all subclasses.
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Affiliation(s)
- Shubham Agrawal
- Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka 575025 India
| | - Aastha Chowdhary
- Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka 575025 India
| | - Saurabh Agarwala
- Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka 575025 India
| | - Veena Mayya
- Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka 575025 India
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576104 India
| | - Sowmya Kamath S.
- Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka 575025 India
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Kuo MT, Hsu BWY, Lin YS, Fang PC, Yu HJ, Chen A, Yu MS, Tseng VS. Comparisons of deep learning algorithms for diagnosing bacterial keratitis via external eye photographs. Sci Rep 2021; 11:24227. [PMID: 34930952 PMCID: PMC8688438 DOI: 10.1038/s41598-021-03572-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/30/2021] [Indexed: 02/08/2023] Open
Abstract
Bacterial keratitis (BK), a painful and fulminant bacterial infection of the cornea, is the most common type of vision-threatening infectious keratitis (IK). A rapid clinical diagnosis by an ophthalmologist may often help prevent BK patients from progression to corneal melting or even perforation, but many rural areas cannot afford an ophthalmologist. Thanks to the rapid development of deep learning (DL) algorithms, artificial intelligence via image could provide an immediate screening and recommendation for patients with red and painful eyes. Therefore, this study aims to elucidate the potentials of different DL algorithms for diagnosing BK via external eye photos. External eye photos of clinically suspected IK were consecutively collected from five referral centers. The candidate DL frameworks, including ResNet50, ResNeXt50, DenseNet121, SE-ResNet50, EfficientNets B0, B1, B2, and B3, were trained to recognize BK from the photo toward the target with the greatest area under the receiver operating characteristic curve (AUROC). Via five-cross validation, EfficientNet B3 showed the most excellent average AUROC, in which the average percentage of sensitivity, specificity, positive predictive value, and negative predictive value was 74, 64, 77, and 61. There was no statistical difference in diagnostic accuracy and AUROC between any two of these DL frameworks. The diagnostic accuracy of these models (ranged from 69 to 72%) is comparable to that of the ophthalmologist (66% to 74%). Therefore, all these models are promising tools for diagnosing BK in first-line medical care units without ophthalmologists.
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Affiliation(s)
- Ming-Tse Kuo
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung City, 833, Taiwan (R.O.C.). .,School of Medicine, Chang Gung University, Taoyuan City, 33302, Taiwan.
| | - Benny Wei-Yun Hsu
- Department of Computer Science, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd., East Dist., Hsinchu City, 300, Taiwan (R.O.C.)
| | - Yi-Sheng Lin
- Department of Computer Science, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd., East Dist., Hsinchu City, 300, Taiwan (R.O.C.)
| | - Po-Chiung Fang
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung City, 833, Taiwan (R.O.C.).,School of Medicine, Chang Gung University, Taoyuan City, 33302, Taiwan
| | - Hun-Ju Yu
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung City, 833, Taiwan (R.O.C.)
| | - Alexander Chen
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung City, 833, Taiwan (R.O.C.)
| | - Meng-Shan Yu
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, No.123, Dapi Rd., Niaosong Dist., Kaohsiung City, 833, Taiwan (R.O.C.)
| | - Vincent S Tseng
- Department of Computer Science, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd., East Dist., Hsinchu City, 300, Taiwan (R.O.C.).
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