Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age-related macular degeneration

Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model

PURPOSE

To test the diagnostic performance of an artificial intelligence algorithm for detecting and segmenting macular neovascularization (MNV) with OCT and OCT angiography (OCTA) in eyes with macular edema from various diagnoses.

DESIGN

Prospective cross-sectional study.

PARTICIPANTS

Study participants with macular edema due to either treatment-naïve exudative age-related macular degeneration (AMD), diabetic macular edema (DME), or retinal vein occlusion (RVO).

METHODS

Study participants were imaged with macular 3 × 3-mm and 6 × 6-mm spectral-domain OCTA. Eyes with exudative AMD were required to have MNV in the central 3 × 3-mm area. A previously developed hybrid multitask convolutional neural network for MNV detection (aiMNV), and segmentation was applied to all images, regardless of image quality.

MAIN OUTCOME MEASURES

Sensitivity, specificity, positive predictive value, and negative predictive value of detecting MNV and intersection over union (IoU) score and F1 score for segmentation.

RESULTS

Of 114 eyes from 112 study participants, 56 eyes had MNV due to exudative AMD and 58 eyes with macular edema due to either DME or RVO. The 3 × 3-mm OCTA scans with aiMNV detected MNV with 96.4% sensitivity, 98.3% specificity, 98.2% positive predictive value, and 96.6% negative predictive value. For segmentation, the average IoU score was 0.947, and the F1 score was 0.973. The 6 × 6-mm scans performed well; however, sensitivity for MNV detection was lower than 3 × 3-mm scans due to lower scan sampling density.

CONCLUSIONS

This novel aiMNV algorithm can accurately detect and segment MNV in eyes with exudative AMD from a control group of eyes that present with macular edema from either DME or RVO. Higher scan sampling density improved the aiMNV sensitivity for MNV detection.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Optical coherence tomography angiography in neovascular age-related macular degeneration: comprehensive review of advancements and future perspective

Optical coherence tomography angiography (OCTA) holds promise in enhancing the care of various retinal vascular diseases, including neovascular age-related macular degeneration (nAMD). Given nAMD's vascular nature and the distinct vasculature of macular neovascularization (MNV), detailed analysis is expected to gain significance. Research in artificial intelligence (AI) indicates that en-face OCTA views may offer superior predictive capabilities than spectral domain optical coherence tomography (SD-OCT) images, highlighting the necessity to identify key vascular parameters. Analyzing vasculature could facilitate distinguishing MNV subtypes and refining diagnosis. Future studies correlating OCTA parameters with clinical data might prompt a revised classification system. However, the combined utilization of qualitative and quantitative OCTA biomarkers to enhance the accuracy of diagnosing disease activity remains underdeveloped. Discrepancies persist regarding the optimal biomarker for indicating an active lesion, warranting comprehensive prospective studies for validation. AI holds potential in extracting valuable insights from the vast datasets within OCTA, enabling researchers and clinicians to fully exploit its OCTA imaging capabilities. Nevertheless, challenges pertaining to data quantity and quality pose significant obstacles to AI advancement in this field. As OCTA gains traction in clinical practice and data volume increases, AI-driven analysis is expected to further augment diagnostic capabilities.

Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence

This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-instrument training dataset composed of Optical coherence tomography-angiography (OCTA) images from two different manufacturers. A retrospective cross-sectional study analyzed 2D vascular en-face OCTA images from Heidelberg Spectralis (1478 samples: 1102 training, 276 validation, 100 testing) and Optovue Solix (1003 samples: 754 training, 189 validation, 60 testing). OCTA scans were labeled based on clinical diagnoses and adjacent B-scan OCT fluid information, categorizing activity into normal, dry AMD, active wet AMD, and wet AMD in remission. Experiments explored cross-instrument disease classification using separate and combined datasets for training the DNN. Testing involved 100 Heidelberg and 60 Optovue samples. Training on Heidelberg data alone yielded 73% accuracy on Heidelberg images and 60% on Optovue images. Training on Optovue data alone resulted in 34% accuracy on Heidelberg and 85% on Optovue images. Combined training data from both instruments achieved 78% accuracy on Heidelberg and 76% on Optovue test sets. Results indicate that cross-instrument classifier training demonstrates high classification prediction accuracy, making cross-instrument training viable for future clinical applications. This implies that vascular morphology in OCTA can predict disease progression.

Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions

Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets have demonstrated impressive capabilities of producing highly transferable representations for different downstream tasks. In ophthalmology, large multi-modal datasets are abundantly available and conveniently accessible as modern retinal imaging scanners acquire both 2D fundus images and 3D optical coherence tomography (OCT) scans to assess the eye. In this context, we introduce a novel multi-modal contrastive learning-based pipeline to facilitate learning joint representations for the two retinal imaging modalities. After self-supervised pre-training on 153,306 scan pairs, we show that such a pre-training framework can provide both a retrieval system and encoders that produce comprehensive OCT and fundus image representations that generalize well for various downstream tasks on three independent external datasets, explicitly focusing on clinically pertinent prediction tasks. In addition, we show that interchanging OCT with lower-cost fundus imaging can preserve the predictive power of the trained models.

Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications

With the increasing availability of diverse healthcare data sources, such as medical images and electronic health records, there is a growing need to effectively integrate and fuse this multimodal data for comprehensive analysis and decision-making. However, despite its potential, multimodal data fusion in healthcare remains limited. This review paper provides an overview of existing literature on multimodal data fusion in healthcare, covering 69 relevant works published between 2018 and 2024. It focuses on methodologies that integrate different data types to enhance medical analysis, including techniques for integrating medical images with structured and unstructured data, combining multiple image modalities, and other features. Additionally, the paper reviews various approaches to multimodal data fusion, such as early, intermediate, and late fusion methods, and examines the challenges and limitations associated with these techniques. The potential benefits and applications of multimodal data fusion in various diseases are highlighted, illustrating specific strategies employed in healthcare artificial intelligence (AI) model development. This research synthesizes existing information to facilitate progress in using multimodal data for improved medical diagnosis and treatment planning.

Dry age-related macular degeneration classification from optical coherence tomography images based on ensemble deep learning architecture

Background

Dry age-related macular degeneration (AMD) is a retinal disease, which has been the third leading cause of vision loss. But current AMD classification technologies did not focus on the classification of early stage. This study aimed to develop a deep learning architecture to improve the classification accuracy of dry AMD, through the analysis of optical coherence tomography (OCT) images.

Methods

We put forward an ensemble deep learning architecture which integrated four different convolution neural networks including ResNet50, EfficientNetB4, MobileNetV3 and Xception. All networks were pre-trained and fine-tuned. Then diverse convolution neural networks were combined. To classify OCT images, the proposed architecture was trained on the dataset from Shenyang Aier Excellence Hospital. The number of original images was 4,096 from 1,310 patients. After rotation and flipping operations, the dataset consisting of 16,384 retinal OCT images could be established.

Results

Evaluation and comparison obtained from three-fold cross-validation were used to show the advantage of the proposed architecture. Four metrics were applied to compare the performance of each base model. Moreover, different combination strategies were also compared to validate the merit of the proposed architecture. The results demonstrated that the proposed architecture could categorize various stages of AMD. Moreover, the proposed network could improve the classification performance of nascent geographic atrophy (nGA).

Conclusion

In this article, an ensemble deep learning was proposed to classify dry AMD progression stages. The performance of the proposed architecture produced promising classification results which showed its advantage to provide global diagnosis for early AMD screening. The classification performance demonstrated its potential for individualized treatment plans for patients with AMD.

Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review

BACKGROUND

In recent years, ophthalmology has emerged as a new frontier in medical artificial intelligence (AI) with multi-modal AI in ophthalmology garnering significant attention across interdisciplinary research. This integration of various types and data models holds paramount importance as it enables the provision of detailed and precise information for diagnosing eye and vision diseases. By leveraging multi-modal ophthalmology AI techniques, clinicians can enhance the accuracy and efficiency of diagnoses, and thus reduce the risks associated with misdiagnosis and oversight while also enabling more precise management of eye and vision health. However, the widespread adoption of multi-modal ophthalmology poses significant challenges.

MAIN TEXT

In this review, we first summarize comprehensively the concept of modalities in the field of ophthalmology, the forms of fusion between modalities, and the progress of multi-modal ophthalmic AI technology. Finally, we discuss the challenges of current multi-modal AI technology applications in ophthalmology and future feasible research directions.

CONCLUSION

In the field of ophthalmic AI, evidence suggests that when utilizing multi-modal data, deep learning-based multi-modal AI technology exhibits excellent diagnostic efficacy in assisting the diagnosis of various ophthalmic diseases. Particularly, in the current era marked by the proliferation of large-scale models, multi-modal techniques represent the most promising and advantageous solution for addressing the diagnosis of various ophthalmic diseases from a comprehensive perspective. However, it must be acknowledged that there are still numerous challenges associated with the application of multi-modal techniques in ophthalmic AI before they can be effectively employed in the clinical setting.

Multimodality Fusion Strategies in Eye Disease Diagnosis

Multimodality fusion has gained significance in medical applications, particularly in diagnosing challenging diseases like eye diseases, notably diabetic eye diseases that pose risks of vision loss and blindness. Mono-modality eye disease diagnosis proves difficult, often missing crucial disease indicators. In response, researchers advocate multimodality-based approaches to enhance diagnostics. This study is a unique exploration, evaluating three multimodality fusion strategies-early, joint, and late-in conjunction with state-of-the-art convolutional neural network models for automated eye disease binary detection across three datasets: fundus fluorescein angiography, macula, and combination of digital retinal images for vessel extraction, structured analysis of the retina, and high-resolution fundus. Findings reveal the efficacy of each fusion strategy: type 0 early fusion with DenseNet121 achieves an impressive 99.45% average accuracy. InceptionResNetV2 emerges as the top-performing joint fusion architecture with an average accuracy of 99.58%. Late fusion ResNet50V2 achieves a perfect score of 100% across all metrics, surpassing both early and joint fusion. Comparative analysis demonstrates that late fusion ResNet50V2 matches the accuracy of state-of-the-art feature-level fusion model for multiview learning. In conclusion, this study substantiates late fusion as the optimal strategy for eye disease diagnosis compared to early and joint fusion, showcasing its superiority in leveraging multimodal information.

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review

We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.

Deep ocular tumor classification model using cuckoo search algorithm and Caputo fractional gradient descent

While digital ocular fundus images are commonly used for diagnosing ocular tumors, interpreting these images poses challenges due to their complexity and the subtle features specific to tumors. Automated detection of ocular tumors is crucial for timely diagnosis and effective treatment. This study investigates a robust deep learning system designed for classifying ocular tumors. The article introduces a novel optimizer that integrates the Caputo fractional gradient descent (CFGD) method with the cuckoo search algorithm (CSA) to enhance accuracy and convergence speed, seeking optimal solutions. The proposed optimizer's performance is assessed by training well-known Vgg16, AlexNet, and GoogLeNet models on 400 fundus images, equally divided between benign and malignant classes. Results demonstrate the significant potential of the proposed optimizer in improving classification accuracy and convergence speed. In particular, the mean accuracy attained by the proposed optimizer is 86.43%, 87.42%, and 87.62% for the Vgg16, AlexNet, and GoogLeNet models, respectively. The performance of our optimizer is compared with existing approaches, namely stochastic gradient descent with momentum (SGDM), adaptive momentum estimation (ADAM), the original cuckoo search algorithm (CSA), Caputo fractional gradient descent (CFGD), beetle antenna search with ADAM (BASADAM), and CSA with ADAM (CSA-ADAM). Evaluation criteria encompass accuracy, robustness, consistency, and convergence speed. Comparative results highlight significant enhancements across all metrics, showcasing the potential of deep learning techniques with the proposed optimizer for accurately identifying ocular tumors. This research contributes significantly to the development of computer-aided diagnosis systems for ocular tumors, emphasizing the benefits of the proposed optimizer in medical image classification domains.

ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION

PURPOSE

The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans.

METHODS

Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories.

RESULTS

The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021).

CONCLUSION

These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.

Optical Coherence Tomography Angiography: A 2023 Focused Update on Age-Related Macular Degeneration

Optical coherence tomography angiography (OCTA) has extensively enhanced our comprehension of eye microcirculation and of its associated diseases. In this narrative review, we explored the key concepts behind OCTA, as well as the most recent evidence in the pathophysiology of age-related macular degeneration (AMD) made possible by OCTA. These recommendations were updated since the publication in 2020, and are targeted for 2023. Importantly, as a future perspective in OCTA technology, we will discuss how artificial intelligence has been applied to OCTA, with a particular emphasis on its application to AMD study.

Evaluation of long noncoding RNA (LncRNA) in pathogenesis of HELLP syndrome: diagnostic and future approach

HELLP syndrome is a disorder during pregnancy which is defined by elevation of liver enzymes, haemolysis, and low platelet count. This syndrome is a multifactorial one and both genetic and environmental components can have a crucial role in this syndrome's pathogenesis. Long noncoding RNAs (lncRNAs), are defined as long non-protein coding molecules (more than 200 nucleotides), which are functional units in most cellular processes such as cell cycle, differentiation, metabolism and some diseases progression. As these markers discovered, there has been some evidence that they have an important role in the function of some organs, such as placenta; therefore, alteration and dysregulation of these RNAs can develop or alleviate HELLP disorder. Although the role of lncRNAs has been shown in HELLP syndrome, the process is still unclear. In this review, our purpose is to evaluate the association between molecular mechanisms of lncRNAs and HELLP syndrome pathogenicity to elicit some novel approaches for HELLP diagnosis and treatment.

Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the elderly, more commonly in developed countries. Optical coherence tomography (OCT) is a non-invasive imaging device widely used for the diagnosis and management of AMD. Deep learning (DL) uses multilayered artificial neural networks (NN) for feature extraction, and is the cutting-edge technique for medical image analysis for diagnostic and prognostication purposes. Application of DL models to OCT image analysis has garnered significant interest in recent years. In this review, we aimed to summarize studies focusing on DL models used in classification and detection of AMD. Additionally, we provide a brief introduction to other DL applications in AMD, such as segmentation, prediction/prognostication, and models trained on multimodal imaging.

Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume

Age-related Macular Degeneration (AMD) is a degenerative eye disease that causes central vision loss. Optical Coherence Tomography Angiography (OCTA) is an emerging imaging modality that aids in the diagnosis of AMD by displaying the pathogenic vessels in the subretinal space. In this paper, we investigate the effectiveness of OCTA from the view of deep classifiers. To the best of our knowledge, this is the first study that solely uses OCTA for AMD stage grading. By developing a 2D classifier based on OCTA projections, we identify that segmentation errors in retinal layers significantly affect the accuracy of classification. To address this issue, we propose analyzing 3D OCTA volumes directly using a 2D convolutional neural network trained with additional projection supervision. Our experimental results show that we achieve over 80% accuracy on a four-stage grading task on both error-free and error-prone test sets, which is significantly higher than 60%, the accuracy of human experts. This demonstrates that OCTA provides sufficient information for AMD stage grading and the proposed 3D volume analyzer is more robust when dealing with OCTA data with segmentation errors.

Two-step hierarchical neural network for classification of dry age-related macular degeneration using optical coherence tomography images

Purpose

The aim of this study is to apply deep learning techniques for the development and validation of a system that categorizes various phases of dry age-related macular degeneration (AMD), including nascent geographic atrophy (nGA), through the analysis of optical coherence tomography (OCT) images.

Methods

A total of 3,401 OCT macular images obtained from 338 patients admitted to Shenyang Aier Eye Hospital in 2019-2021 were collected for the development of the classification model. We adopted a convolutional neural network (CNN) model and introduced hierarchical structure along with image enhancement techniques to train a two-step CNN model to detect and classify normal and three phases of dry AMD: atrophy-associated drusen regression, nGA, and geographic atrophy (GA). Five-fold cross-validation was used to evaluate the performance of the multi-label classification model.

Results

Experimental results obtained from five-fold cross-validation with different dry AMD classification models show that the proposed two-step hierarchical model with image enhancement achieves the best classification performance, with a f1-score of 91.32% and a kappa coefficients of 96.09% compared to the state-of-the-art models. The results obtained from the ablation study demonstrate that the proposed method not only improves accuracy across all categories in comparison to a traditional flat CNN model, but also substantially enhances the classification performance of nGA, with an improvement from 66.79 to 81.65%.

Conclusion

This study introduces a novel two-step hierarchical deep learning approach in categorizing dry AMD progression phases, and demonstrates its efficacy. The high classification performance suggests its potential for guiding individualized treatment plans for patients with macular degeneration.

Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a "black box" that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model's predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model's predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment.

Models and Algorithms for the Refinement of Therapeutic Approaches for Retinal Diseases

We are developing a Virtual Eye for in silico therapies to accelerate research and drug development. In this paper, we present a model for drug distribution in the vitreous body that enables personalized therapy in ophthalmology. The standard treatment for age-related macular degeneration is anti-vascular endothelial growth factor (VEGF) drugs administered by repeated injections. The treatment is risky, unpopular with patients, and some of them are unresponsive with no alternative treatment. Much attention is paid to the efficacy of these drugs, and many efforts are being made to improve them. We are designing a mathematical model and performing long-term three-dimensional Finite Element simulations for drug distribution in the human eye to gain new insights in the underlying processes using computational experiments. The underlying model consists of a time-dependent convection-diffusion equation for the drug coupled with a steady-state Darcy equation describing the flow of aqueous humor through the vitreous medium. The influence of collagen fibers in the vitreous on drug distribution is included by anisotropic diffusion and the gravity via an additional transport term. The resulting coupled model was solved in a decoupled way: first the Darcy equation with mixed finite elements, then the convection-diffusion equation with trilinear Lagrange elements. Krylov subspace methods are used to solve the resulting algebraic system. To cope with the large time steps resulting from the simulations over 30 days (operation time of 1 anti-VEGF injection), we apply the strong A-stable fractional step theta scheme. Using this strategy, we compute a good approximation to the solution that converges quadratically in both time and space. The developed simulations were used for the therapy optimization, for which specific output functionals are evaluated. We show that the effect of gravity on drug distribution is negligible, that the optimal pair of injection angles is (50∘,50∘), that larger angles can result in 38% less drug at the macula, and that in the best case only 40% of the drug reaches the macula while the rest escapes, e.g., through the retina, that by using heavier drug molecules, more of the drug concentration reaches the macula in an average of 30 days. As a refined therapy, we have found that for longer-acting drugs, the injection should be made in the center of the vitreous, and for more intensive initial treatment, the drug should be injected even closer to the macula. In this way, we can perform accurate and efficient treatment testing, calculate the optimal injection position, perform drug comparison, and quantify the effectiveness of the therapy using the developed functionals. We describe the first steps towards virtual exploration and improvement of therapy for retinal diseases such as age-related macular degeneration.

Association between disease-modifying antirheumatic drugs and bone turnover biomarkers

Rheumatoid arthritis (RA) has been linked to an increased risk of osteoporosis as well as fractures. Patients diagnosed with RA had a 25% increased risk of osteoporotic fracture, according to a recent population-based cohort study that compared them to people without RA. Several studies have found a correlation between osteoporosis and the presence of pro-inflammatory cytokines, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1, and 6. These cytokines play a crucial part in the process of bone resorption by boosting osteoclast activation and encouraging osteoclast differentiation. Based on the correlation between RA, osteoporosis, and inflammation, it is possible that systemic immunosuppression with disease-modifying antirheumatic drugs (DMARDs) can help individuals with RA have a lower chance of developing osteoporosis and osteoporotic fractures. There is little information on how different DMARDs, biologic or non-biologic, affect RA patients' bone metabolism. In this study, we present an overview of the influence that targeted therapies, such as biologics, non-biologics, and small molecule inhibitors, have on bone homeostasis in RA patients.

Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.

Influencing factors of work stress of medical workers in clinical laboratory during COVID-19 pandemic: Working hours, compensatory leave, job satisfaction

Background

The COVID-19 pandemic continues to pose unprecedented threats and challenges to global public health. Hospital Clinical Laboratory and public health institutions have been playing an important role in case detection, epidemic research and decision-making, and epidemic prevention and control.

Objective

To explore the current situation and influencing factors of work stress of medical workers in hospital clinical laboratory in fighting against COVID-19.

Methods

A cluster random sampling method was used to select seven hospitals from 14 tertiary hospitals in Xiamen, and medical workers in the selected hospitals were investigated by self-administered questionnaire. A total of 150 medical workers inclinical laboratory participated in this survey, 138 valid questionnaires were collected, with a response rate of 92%.

Results

The work stress scores of the medical workers in the clinical laboratory of hospital in the COVID-19 epidemic were collected (55.22 ± 11.48); The top three dimensions of work stress score were work stress (work load), external environment and doctor-patient relationship. The results of multiple stepwise regression analysis showed that the working hours per day, whether overtime and night shift can get compensatory leave and Job satisfaction with the work of the clinical laboratory were the main factors affecting the work stress level of medical workers in the clinical laboratory of hospital during COVID-19 epidemic.

Conclusion

The COVID-19 has caused great harm to the physical and mental health of the public. Medical staff are in the front line of prevention and control of the epidemic, so medical workers in hospital clinical laboratory exposed to a high level of stress at work. Laboratory leaders and hospital managers should take active and effective measures to reduce the working hours of the medical staff in clinical laboratory, optimize the arrangement of night shift and overtime working, strengthen the training of group and individual pressure management, reduce the work stress of the medical staff, improve the overall happiness of the medical staff in clinical laboratory, and stabilize the clinical laboratory team, improve the physical and mental health of medical workers in clinical laboratory.

Detection of epileptic seizures through EEG signals using entropy features and ensemble learning

Introduction

Epilepsy is a disorder of the central nervous system that is often accompanied by recurrent seizures. World health organization (WHO) estimated that more than 50 million people worldwide suffer from epilepsy. Although electroencephalogram (EEG) signals contain vital physiological and pathological information of brain and they are a prominent medical tool for detecting epileptic seizures, visual interpretation of such tools is time-consuming. Since early diagnosis of epilepsy is essential to control seizures, we present a new method using data mining and machine learning techniques to diagnose epileptic seizures automatically.

Methods

The proposed detection system consists of three main steps: In the first step, the input signals are pre-processed by discrete wavelet transform (DWT) and sub-bands containing useful information are extracted. In the second step, the features of each sub-band are extracted by approximate entropy (ApEn) and sample entropy (SampEn) and then these features are ranked by ANOVA test. Finally, feature selection is done by the FSFS technique. In the third step, three algorithms are used to classify seizures: Least squared support vector machine (LS-SVM), K nearest neighbors (KNN) and Naive Bayes model (NB).

Results and discussion

The average accuracy for both LS-SVM and NB was 98% and it was 94.5% for KNN, while the results show that the proposed method can detect epileptic seizures with an average accuracy of 99.5%, 99.01% of sensitivity and 100% of specificity which show an improvement over most similar methods and can be used as an effective tool in diagnosing this complication.

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

Introduction

Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes.

Methods

This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.

Results

To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets.

Discussion

The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

Advances in Single-component inorganic nanostructures for photoacoustic imaging guided photothermal therapy

Phototheranostic based on photothermal therapy (PTT) and photoacoustic imaging (PAI), as one of avant-garde medical techniques, have sparked growing attention because it allows noninvasive, deeply penetrative, and highly selective and effective therapy. Among a variety of phototheranostic nanoagents, single-component inorganic nanostructures are found to be novel and attractive PAI and PTT combined nanotheranostic agents and received tremendous attention, which not only exhibit structural controllability, high tunability in physiochemical properties, size-dependent optical properties, high reproducibility, simple composition, easy functionalization, and simple synthesis process, but also can be endowed with multiple therapeutic and imaging functions, realizing the superior therapy result along with bringing less foreign materials into body, reducing systemic side effects and improving the bioavailability. In this review, according to their synthetic components, conventional single-component inorganic nanostructures are divided into metallic nanostructures, metal dichalcogenides, metal oxides, carbon based nanostructures, upconversion nanoparticles (UCNPs), metal organic frameworks (MOFs), MXenes, graphdiyne and other nanostructures. On the basis of this category, their detailed applications in PAI guide PTT of tumor treatment are systematically reviewed, including synthesis strategies, corresponding performances, and cancer diagnosis and therapeutic efficacy. Before these, the factors to influence on photothermal effect and the principle of in vivo PAI are briefly presented. Finally, we also comprehensively and thoroughly discussed the limitation, potential barriers, future perspectives for research and clinical translation of this single-component inorganic nanoagent in biomedical therapeutics.

Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet+

Computerized tomography (CT) is of great significance for the localization and diagnosis of liver cancer. Many scholars have recently applied deep learning methods to segment CT images of liver and liver tumors. Unlike natural images, medical image segmentation is usually more challenging due to its nature. Aiming at the problem of blurry boundaries and complex gradients of liver tumor images, a deep supervision network based on the combination of high-efficiency channel attention and Res-UNet++ (ECA residual UNet++) is proposed for liver CT image segmentation, enabling fully automated end-to-end segmentation of the network. In this paper, the UNet++ structure is selected as the baseline. The residual block feature encoder based on context awareness enhances the feature extraction ability and solves the problem of deep network degradation. The introduction of an efficient attention module combines the depth of the feature map with spatial information to alleviate the uneven sample distribution impact; Use DiceLoss to replace the cross-entropy loss function to optimize network parameters. The liver and liver tumor segmentation accuracy on the LITS dataset was 95.8% and 89.3%, respectively. The results show that compared with other algorithms, the method proposed in this paper achieves a good segmentation performance, which has specific reference significance for computer-assisted diagnosis and treatment to attain fine segmentation of liver and liver tumors.

The multifaced role and therapeutic regulation of autophagy in ovarian cancer

Ovarian cancer (OC) is one of the tumors that occurs most frequently in women. Autophagy is involved in cell homeostasis, biomolecule recycling, and survival, making it a potential target for anti-tumor drugs. It is worth noting that growing evidence reveals a close link between autophagy and OC. In the context of OC, autophagy demonstrates activity as both a tumor suppressor and a tumor promoter, depending on the context. Autophagy's exact function in OC is greatly reliant on the tumor microenvironment (TME) and other conditions, such as hypoxia, nutritional deficiency, chemotherapy, and so on. However, what can be concluded from different studies is that autophagy-related signaling pathways, especially PI3K/AKT/mTOR axis, increase in advanced stages and malignant phenotype of the disease reduces autophagy and ultimately leads to tumor progression. This study sought to present a thorough understanding of the role of autophagy-related signaling pathways in OC and existing therapies targeting these signaling pathways.

Deep-learning-based AI for evaluating estimated nonperfusion areas requiring further examination in ultra-widefield fundus images

We herein propose a PraNet-based deep-learning model for estimating the size of non-perfusion area (NPA) in pseudo-color fundus photos from an ultra-wide-field (UWF) image. We trained the model with focal loss and weighted binary cross-entropy loss to deal with the class-imbalanced dataset, and optimized hyperparameters in order to minimize validation loss. As expected, the resultant PraNet-based deep-learning model outperformed previously published methods. For verification, we used UWF fundus images with NPA and used Bland-Altman plots to compare estimated NPA with the ground truth in FA, which demonstrated that bias between the eNPA and ground truth was smaller than 10% of the confidence limits zone and that the number of outliers was less than 10% of observed paired images. The accuracy of the model was also tested on an external dataset from another institution, which confirmed the generalization of the model. For validation, we employed a contingency table for ROC analysis to judge the sensitivity and specificity of the estimated-NPA (eNPA). The results demonstrated that the sensitivity and specificity ranged from 83.3-87.0% and 79.3-85.7%, respectively. In conclusion, we developed an AI model capable of estimating NPA size from only an UWF image without angiography using PraNet-based deep learning. This is a potentially useful tool in monitoring eyes with ischemic retinal diseases.

Applications of Artificial Intelligence in Optical Coherence Tomography Angiography Imaging

Optical coherence tomography angiography (OCTA) and artificial intelligence (AI) are two emerging fields that complement each other. OCTA enables the noninvasive, in vivo, 3D visualization of retinal blood flow with a micrometer resolution, which has been impossible with other imaging modalities. As it does not need dye-based injections, it is also a safer procedure for patients. AI has excited great interest in many fields of daily life, by enabling automatic processing of huge amounts of data with a performance that greatly surpasses previous algorithms. It has been used in many breakthrough studies in recent years, such as the finding that AlphaGo can beat humans in the strategic board game of Go. This paper will give a short introduction into both fields and will then explore the manifold applications of AI in OCTA imaging that have been presented in the recent years. These range from signal generation over signal enhancement to interpretation tasks like segmentation and classification. In all these areas, AI-based algorithms have achieved state-of-the-art performance that has the potential to improve standard care in ophthalmology when integrated into the daily clinical routine.

Federated Learning in Ocular Imaging: Current Progress and Future Direction

Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the field of ocular imaging over the last few years. Specifically, DL has been utilised to detect and classify various ocular diseases on retinal photographs, optical coherence tomography (OCT) images, and OCT-angiography images. In order to achieve good robustness and generalisability of model performance, DL training strategies traditionally require extensive and diverse training datasets from various sites to be transferred and pooled into a "centralised location". However, such a data transferring process could raise practical concerns related to data security and patient privacy. Federated learning (FL) is a distributed collaborative learning paradigm which enables the coordination of multiple collaborators without the need for sharing confidential data. This distributed training approach has great potential to ensure data privacy among different institutions and reduce the potential risk of data leakage from data pooling or centralisation. This review article aims to introduce the concept of FL, provide current evidence of FL in ocular imaging, and discuss potential challenges as well as future applications.

The Architecture of Macular Neovascularizations Predicts Treatment Responses to Anti-VEGF Therapy in Neovascular AMD

Introduction: Anti-VEGF therapy is an effective option for improving and stabilizing the vision in neovascular age-related macular degeneration (nAMD). However, the response to treatment is markedly heterogeneous. The aim of this study was therefore to analyze the vascular characteristics of type 1,2, and 3 macular neovascularizations (MNV) in order to identify biomarkers that predict treatment response, especially with regard to changes in intraretinal and subretinal fluid. Materials and Methods: Overall, 90 treatment-naive eyes with nAMD confirmed by optic coherence tomography (OCT), fluorescein angiography, and OCT angiography (OCTA) were included in this retrospective study. The MNV detected by OCTA were subjected to quantitative vascular analysis by binarization and skeletonization of the vessel using ImageJ. We determined their area, total vascular length (sumL), fractal dimension (FD), flow density, number of vascular nodes (numN), and average vascular diameter (avgW). The results were correlated with the treatment response to the initial three injections of anti-VEGF and the changes in intraretinal (IRF) and subretinal fluid (SRF) and the occurrence of pigment epithelial detachements (PED). Results: All patients found to have no subretinal or intraretinal fluid following the initial three injections of anti-VEGF showed a significantly smaller MNV area (p < 0.001), a lower sumL (p < 0.0005), and lesser FD (p < 0.005) before treatment than those who still exhibited signs of activity. These parameters also showed a significant influence in the separate analysis of persistent SRF (p < 0.005) and a persistent PED (p < 0.05), whereas we could not detect any influence on changes in IRF. The vascular parameters avgW, numN, and flow density showed no significant influence on SRF/IRF or PED changes. Conclusions: The size, the total vessel length, and the fractal dimension of MNV at baseline are predictors for the treatment response to anti-VEGF therapy. Therefore, particularly regarding the development of new classes of drugs, these parameters could yield new insights into treatment response.

Influence of the coiling porosity on the risk reduction of the cerebral aneurysm rupture: computational study

The formation and progress of cerebral aneurysm is highly associated with hemodynamic factors and blood flow feature. In this study, comprehensive efforts are done to investigate the blood hemodynamic effects on the creation and growth of the Internal Carotid Artery. The computational fluid dynamic method is used for the visualization of the bloodstream inside the aneurysm. Transitional, non-Newtonian and incompressible conditions are considered for solving the Navier-Stokes equation to achieve the high-risk region on the aneurysm wall. OSI and WSS of the aneurysm wall are compared within different blood flow stages. The effects of blood viscosity and coiling treatment on these factors are presented in this work. Our study shows that in male patients (HCT = 0.45), changing the porosity of coiling from 0.89 with 0.79 would decreases maximum OSI up to 75% (in maximum acceleration). However, this effect is limited to about 45% for female patients (HCT = 0.35).

Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives

Background

The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic.

Main text

At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns.

Conclusions

This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.

Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System

The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activist fields. In this paper, first, an experimental data set was created using a designed scenario. The whale optimization algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference by setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, and PGO were also used for MLPNN training. The measured criteria are concurrency speed, ability to avoid local optimization, and the classification rate. The simulation results on the obtained data set showed that, respectively, the classification rate in MLPFWOA, MLP-CVOA, MLP-WOA, MLP-ChOA, MLP-BWO, and MLP-PGO classifiers is equal to 94.98, 92.80, 91.34, 90.24, 89.04, and 88.10. As a result, MLP-FWOA performed better than other algorithms.

Factors Analysis of the Compliance Rate of Hypertension Detection Control and Self-Assessment Control in Community Outpatient Clinics

Objective

To understand the related influencing factors of outpatient hypertension detection and control and self-test control compliance rate.

Methods

A total of 637 hypertensive patients who visited the outpatient clinic of our hospital from January 2021 to December 2021 were selected for investigation and research, and the relevant information such as blood pressure, treatment detection, and other related information of the patients were counted, and the detection and control of outpatient hypertension were explored through regression analysis and the related factors of the self-test control compliance rate.

Results

There was no statistically significant difference in the number of patients who met the standard or not under the gender difference (P > 0.05), and it can be found that there was no statistically significant difference in the age of patients who met the standard and those who did not (P > 0.05). The proportion of patients with self-test hypertension control at home was 64.68%, and the compliance rate of self-test blood pressure was 42.54%. The compliance rate of blood pressure control in outpatient testing was 61.85%. Heart rate, exercise, smoking, medication compliance, and other factors are important factors affecting the control of hypertension. Knowing hypertension-related knowledge, regular follow-up, office blood pressure compliance, smoking, excessive salt intake, and hypertension complications are important factors affecting the self-test control of hypertension in the family.

Conclusion

By urging patients to do daily physical exercise, admonishing patients to quit smoking, and improving patients' medication compliance, the control rate of hypertension in outpatient clinics can be effectively improved. Understanding the knowledge of hypertension, controlling the salt content in the diet, and receiving regular follow-up surveys from doctors can effectively improve the effect of self-measurement and control of blood pressure at home and further improve the control rate of hypertension.

A molecular modeling on the potential application of beryllium oxide nanotube for delivery of hydroxyurea anticancer drug

Within this work, we scrutinized the use of BeO nanotube (BeONT) as a nanocarrier for the anticancer drug hydroxyurea (HU) through density functional theory (DFT) calculations. We utilized the functional ꞷB97XD and the basis set 6-31G**. Based on a detailed surface analysis, HU was adsorbed on the surface of the nanotube through 4 different orientations. Also, no vibrational spectra exhibited imaginary frequencies, showing the minimum energy of the relaxed structures. The maximum adsorption energy and the minimum adsorption energy are in strong physical adsorption. The BeONT exhibited p-type semiconducting characteristics in all orientations since it received electronic charge from HU. The results demonstrate the possibility of using the BeONT as a promising carrier for HU drugs.

The neuroprotective effects of fisetin, a natural flavonoid in neurodegenerative diseases: Focus on the role of oxidative stress

Oxidative stress (OS) disrupts the chemical integrity of macromolecules and increases the risk of neurodegenerative diseases. Fisetin is a flavonoid that exhibits potent antioxidant properties and protects the cells against OS. We have viewed the NCBI database, PubMed, Science Direct (Elsevier), Springer-Nature, ResearchGate, and Google Scholar databases to search and collect relevant articles during the preparation of this review. The search keywords are OS, neurodegenerative diseases, fisetin, etc. High level of ROS in the brain tissue decreases ATP levels, and mitochondrial membrane potential and induces lipid peroxidation, chronic inflammation, DNA damage, and apoptosis. The subsequent results are various neuronal diseases. Fisetin is a polyphenolic compound, commonly present in dietary ingredients. The antioxidant properties of this flavonoid diminish oxidative stress, ROS production, neurotoxicity, neuro-inflammation, and neurological disorders. Moreover, it maintains the redox profiles, and mitochondrial functions and inhibits NO production. At the molecular level, fisetin regulates the activity of PI3K/Akt, Nrf2, NF-κB, protein kinase C, and MAPK pathways to prevent OS, inflammatory response, and cytotoxicity. The antioxidant properties of fisetin protect the neural cells from inflammation and apoptotic degeneration. Thus, it can be used in the prevention of neurodegenerative disorders.

Molecular insights and therapeutic implications of nanoengineered dietary polyphenols for targeting lung cancer: part II

Flavonoids represent a major group of polyphenolic compounds. Their capacity to inhibit tumor proliferation, cell cycle, angiogenesis, migration and invasion is substantially responsible for their chemotherapeutic activity against lung cancer. However, their clinical application is limited due to poor aqueous solubility, low permeability and quick blood clearance, which leads to their low bioavailability. Nanoengineered systems such as liposomes, nanoparticles, micelles, dendrimers and nanotubes can considerably enhance the targeted action of the flavonoids with improved efficacy and pharmacokinetic properties, and flavonoids can be successfully translated from bench to bedside through various nanoengineering approaches. This review addresses the therapeutic potential of various flavonoids and highlights the cutting-edge progress in the nanoengineered systems that incorporate flavonoids for treating lung cancer.

Phytochemical and toxicological evaluation of Zephyranthes citrina

Drugs obtained from medicinal plants have always played a pivotal role in the field of medicine and to identify novel compounds. Safety profiling of plant extracts is of utmost importance during the discovery of new biologically active compounds and the determination of their efficacy. It is imperative to conduct toxicity studies before exploring the pharmacological properties and perspectives of any plant. The present work aims to provide a detailed insight into the phytochemical and toxicological profiling of methanolic extract of Zephyranthes citrina (MEZ). Guidelines to perform subacute toxicity study (407) and acute toxicity study (425) provided by the organization of economic cooperation and development (OECD) were followed. A single orally administered dose of 2000 mg/kg to albino mice was used for acute oral toxicity testing. In the subacute toxicity study, MEZ in doses of 100, 200, and 400 mg/kg was administered orally, consecutive for 28 days. Results of each parameter were compared to the control group. In both studies, the weight of animals and their selected organs showed consistency with that of the control group. No major toxicity or organ damage was recorded except for some minor alterations in a few parameters such as in the acute study, leukocyte count was increased and decreased platelet count, while in the subacute study platelet count increased in all doses. In the acute toxicity profile liver enzymes Alanine aminotransferase (ALT), as well as, aspartate aminotransferase (AST) were found to be slightly raised while alkaline phosphatase (ALP) was decreased. In subacute toxicity profiling, AST and ALT were not affected by any dose while ALP was decreased only at doses of 200 and 400 mg/kg. Uric acid was raised at a dose of 100 mg/kg. In acute toxicity, at 2000 mg/kg, creatinine and uric acid increased while urea levels decreased. Therefore, it is concluded that the LD₅₀ of MEZ is more than 2000 mg/kg and the toxicity profile of MEZ was generally found to be safe.