Application of Artificial Intelligence in the Diagnosis and Management of Corneal Diseases

Fungal Keratitis: Diagnosis, Management, and Recent Advances

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.

A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis

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.

Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis

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.

Advances in artificial intelligence applications for ocular surface diseases diagnosis

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.

Artificial Intelligence Technique of Synthesis and Characterizations for Measurement of Optical Particles in Medical Devices

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.