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Awad R, Ghaith AA, Awad K, Mamdouh Saad M, Elmassry AA. Fungal Keratitis: Diagnosis, Management, and Recent Advances. Clin Ophthalmol 2024; 18:85-106. [PMID: 38223815 PMCID: PMC10788054 DOI: 10.2147/opth.s447138] [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] [Received: 10/31/2023] [Accepted: 12/09/2023] [Indexed: 01/16/2024] Open
Abstract
Fungal keratitis is one of the major causes of microbial keratitis that may lead to corneal blindness. Many problems related to diagnosis and therapy are encountered in fungal keratitis, including difficulty in obtaining laboratory diagnoses and the availability and efficacy of antifungal medications. Intensive and prolonged use of antifungal topical preparations may not be enough. The use of antifungal medications is considered the main treatment for fungal keratitis. It is recommended to start antifungal therapy after confirmation of the clinical diagnosis with a smear or positive cultures. Topical application of antifungal medications is a mainstay for the treatment of fungal keratitis; however, systemic, intra-stromal, or intra-cameral routes may be used. Therapeutic keratoplasty is the main surgical procedure approved for the management of fungal keratitis with good success rate. Intrastromal corneal injection of antifungal medications may result in steady-state drug levels within the corneal tissue and prevent intervals of decreased antifungal drug concentration below its therapeutic level. In cases of severe fungal keratitis with deep stromal infiltration not responding to treatment, intracameral injection of antifungal agents may be effective. Collagen cross-linking has been proposed to be beneficial for cases of fungal keratitis as a stand-alone therapy or as an adjunct to antifungal medications. Although collagen cross-linking has been extensively studied in the past few years, its protocol still needs many modifications to optimize UV fluence levels, irradiation time, and concentration of riboflavin to achieve 100% microbial killing.
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Affiliation(s)
- Ramy Awad
- Department of Ophthalmology, Alexandria General Ophthalmology Hospital, Alexandria, Egypt
| | - Alaa Atef Ghaith
- Department of Ophthalmology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Khaled Awad
- Department of Ophthalmology, Alexandria General Ophthalmology Hospital, Alexandria, Egypt
| | - Marina Mamdouh Saad
- Department of Ophthalmology, Alexandria General Ophthalmology Hospital, Alexandria, Egypt
| | - Ahmed Ak Elmassry
- Department of Ophthalmology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
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Wu WJ, Chen R, Guo R, Yan JJ, Zhang CK, Wang YQ, Yan HX, Zhang YQ. A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis. Ir J Med Sci 2023; 192:2697-2706. [PMID: 36961673 PMCID: PMC10692030 DOI: 10.1007/s11845-023-03341-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/08/2023] [Indexed: 03/25/2023]
Abstract
BACKGROUND The timely assessment of B-type natriuretic peptide (BNP) marking chronic heart failure risk in patients with coronary heart disease (CHD) helps to reduce patients' mortality. OBJECTIVE To evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with CHD. METHODS A total of 419 patients with CHD were assigned to Group 1 (BNP < 95 pg/mL, n = 249), 2 (95 < BNP < 221 pg/mL, n = 85), and 3 (BNP > 221 pg/mL, n = 85) according to BNP levels. Wrist pulse signals were measured noninvasively. Both the time-domain method and multiscale entropy (MSE) method were used to extract pulse features. Decision tree (DT) and random forest (RF) algorithms were employed to construct models for classifying three groups, and the models' performance metrics were compared. RESULTS The pulse features of the three groups differed significantly, suggesting different pathological states of the cardiovascular system in patients with CHD. Moreover, the RF models outperformed the DT models in performance metrics. Furthermore, the optimal RF model was that based on a dataset comprising both time-domain and MSE features, achieving accuracy, average precision, average recall, and average F1-score of 90.900%, 91.048%, 90.900%, and 90.897%, respectively. CONCLUSIONS The wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.
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Affiliation(s)
- Wen-Jie Wu
- Department of Basic Medical Science, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New District, Shanghai, 201203, China
| | - Rui Chen
- Department of Basic Medical Science, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New District, Shanghai, 201203, China
| | - Rui Guo
- Department of Basic Medical Science, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New District, Shanghai, 201203, China.
| | - Jian-Jun Yan
- Institute of Intelligent Perception and Diagnosis, School of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai, 200237, China
| | - Chun-Ke Zhang
- Department of Basic Medical Science, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New District, Shanghai, 201203, China
| | - Yi-Qin Wang
- Department of Basic Medical Science, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New District, Shanghai, 201203, China
| | - Hai-Xia Yan
- Department of Basic Medical Science, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New District, Shanghai, 201203, China
| | - Ye-Qing Zhang
- Department of Chinese Internal Medicine, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200071, China
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Sarayar R, Lestari YD, Setio AAA, Sitompul R. Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis. Front Public Health 2023; 11:1239231. [PMID: 38074720 PMCID: PMC10704127 DOI: 10.3389/fpubh.2023.1239231] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 11/03/2023] [Indexed: 12/18/2023] Open
Abstract
Background Infectious keratitis (IK) is a sight-threatening condition requiring immediate definite treatment. The need for prompt treatment heavily depends on timely diagnosis. The diagnosis of IK, however, is challenged by the drawbacks of the current "gold standard." The poorly differentiated clinical features, the possibility of low microbial culture yield, and the duration for culture are the culprits of delayed IK treatment. Deep learning (DL) is a recent artificial intelligence (AI) advancement that has been demonstrated to be highly promising in making automated diagnosis in IK with high accuracy. However, its exact accuracy is not yet elucidated. This article is the first systematic review and meta-analysis that aims to assess the accuracy of available DL models to correctly classify IK based on etiology compared to the current gold standards. Methods A systematic search was carried out in PubMed, Google Scholars, Proquest, ScienceDirect, Cochrane and Scopus. The used keywords are: "Keratitis," "Corneal ulcer," "Corneal diseases," "Corneal lesions," "Artificial intelligence," "Deep learning," and "Machine learning." Studies including slit lamp photography of the cornea and validity study on DL performance were considered. The primary outcomes reviewed were the accuracy and classification capability of the AI machine learning/DL algorithm. We analyzed the extracted data with the MetaXL 5.2 Software. Results A total of eleven articles from 2002 to 2022 were included with a total dataset of 34,070 images. All studies used convolutional neural networks (CNNs), with ResNet and DenseNet models being the most used models across studies. Most AI models outperform the human counterparts with a pooled area under the curve (AUC) of 0.851 and accuracy of 96.6% in differentiating IK vs. non-IK and pooled AUC 0.895 and accuracy of 64.38% for classifying bacterial keratitis (BK) vs. fungal keratitis (FK). Conclusion This study demonstrated that DL algorithms have high potential in diagnosing and classifying IK with accuracy that, if not better, is comparable to trained corneal experts. However, various factors, such as the unique architecture of DL model, the problem with overfitting, image quality of the datasets, and the complex nature of IK itself, still hamper the universal applicability of DL in daily clinical practice.
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Affiliation(s)
- Randy Sarayar
- Residency Program in Ophthalmology Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Yeni Dwi Lestari
- Department of Ophthalmology, Faculty of Medicine Universitas Indonesia – Cipto Mangunkusumo General Hospital, Jakarta, Indonesia
| | - Arnaud A. A. Setio
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Ratna Sitompul
- Department of Ophthalmology, Faculty of Medicine Universitas Indonesia – Cipto Mangunkusumo General Hospital, Jakarta, Indonesia
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Ji Y, Liu S, Hong X, Lu Y, Wu X, Li K, Li K, Liu Y. Advances in artificial intelligence applications for ocular surface diseases diagnosis. Front Cell Dev Biol 2022; 10:1107689. [PMID: 36605721 PMCID: PMC9808405 DOI: 10.3389/fcell.2022.1107689] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.
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Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Sha Liu
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangqian Hong
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Yi Lu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Xingyang Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Kunke Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Keran Li
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Yunfang Liu
- Department of Ophthalmology, First Affiliated Hospital of Huzhou University, Huzhou, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
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Artificial Intelligence Technique of Synthesis and Characterizations for Measurement of Optical Particles in Medical Devices. Appl Bionics Biomech 2022; 2022:9103551. [PMID: 35186120 PMCID: PMC8856814 DOI: 10.1155/2022/9103551] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/12/2022] [Accepted: 01/27/2022] [Indexed: 02/03/2023] Open
Abstract
The aim of this study is to demonstrate the effect of particle size on semiconductor properties; artificial intelligence is being used for the research methods. As a result, we picked cadmium sulfide (CdS), which is a unique semiconductor material that is employed in a broad variety of current applications. Given that CdS has distinct electrical and optical characteristics, it may be employed in the production of solar cells, for example. Solar cells, as is also well known, have become an essential source of energy in the world. Within the visible range (500-700 nm), we create one layer of bulk CdS and one layer of nano-CdS air bulk CdS air and air nano-CdS air. We used a number of instrumentation methods to investigate the naked CdS nanoparticles, including XRD, SEM-EDX, UV-Vis spectroscopy, TEM, XPS, and PL spectroscopy, among others. The results show that for bulk CdS at normal incidence, the transmittance is T = 45, and for nano-CdS with particle size 3 nm, the transmittance is T = 85.8, with transverse-electric (S-polarized) and transverse-magnetic (P-polarized) transmittances of TE = 75 and TM = 80, respectively.
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