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Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR., Design: Retrospective study., Participants: A total of 48 269 retinal images from the open-source Kaggle DR detection dataset, the Messidor-1 dataset and the Singapore Epidemiology of Eye Diseases (SEED) study were included., Methods: Using 41 614 retinal photographs from the Kaggle dataset, we developed 5 CNN (Visual Geometry Group 19, ResNet50, InceptionV3, DenseNet201, and EfficientNetV2S) and 4 ViTs models (VAN&#95;small, CrossViT&#95;small, ViT&#95;small, and Hierarchical Vision transformer using Shifted Windows [SWIN]&#95;tiny) for the detection of referable DR. We defined the presence of referable DR as eyes with moderate or worse DR. The comparative performance of all 9 models was evaluated in the Kaggle internal test dataset (with 1045 study eyes), and in 2 external test sets, the SEED study (5455 study eyes) and the Messidor-1 (1200 study eyes)., Main Outcome Measures: Area under operating characteristics curve (AUC), specificity, and sensitivity., Results: Among all models, the SWIN transformer displayed the highest AUC of 95.7% on the internal test set, significantly outperforming the CNN models (all P  < 0.001). The same observation was confirmed in the external test sets, with the SWIN transformer achieving AUC of 97.3% in SEED and 96.3% in Messidor-1. When specificity level was fixed at 80% for the internal test, the SWIN transformer achieved the highest sensitivity of 94.4%, significantly better than all the CNN models (sensitivity levels ranging between 76.3% and 83.8%; all P  < 0.001). This trend was also consistently observed in both external test sets., Conclusions: Our findings demonstrate that ViTs provide superior performance over CNNs in detecting referable DR from retinal photographs. These results point to the potential of utilizing ViT models to improve and optimize retinal photo-based deep learning for referable DR detection., Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (© 2024 by the American Academy of Ophthalmology.)

Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts' ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson's correlations of 0.97 [95% CI: 0.96-0.98], 0.97 [0.95-0.98], 0.95 [0.92-0.98], and 0.87 [0.83-0.91], with intra-class correlation values of 0.99 [0.98-0.99], 0.98 [0.98-0.99], and 0.95 [0.96-0.98], 0.93 [0.91-0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions., (© 2024. The Author(s).)

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.

Sensory analysis and untargeted lipidomics were employed to study the impact of phospholipase B (PLB) on lipid oxidation and flavor in steamed sturgeon meat, revealing the inherent relationship between lipid oxidation and flavor regulation. The research verified that PLB effectively suppresses fat oxidation and improves the overall taste of steamed sturgeon meat. Furthermore, the PLB group identified 52 compounds, and the content of odor substances such as isoamyl alcohol and hexanal was reduced compared with other groups. Finally, lipid substances containing eicosapentaenoic acid (EPA) or docosahexaenoic acid (DHA) were screened out from 32 kinds of differential phospholipids. Through Pearson correlation analysis, it was observed that certain differential phospholipids such as PC (22:6) and PC (22:5) exhibited varying correlations with odor substances like hexanal and isovaleraldehyde. These findings suggest that PLB specifically affects certain phospholipids, leading to the production of distinct volatile substances through oxidative degradation., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Despite the favorable biocompatibility of natural antimicrobial peptides (AMPs), their scarcity limits their practical application. Through rational design, the activity of AMPs can be enhanced to expand their application. In this study, we selected a natural sturgeon epidermal mucus peptide, AP-16 (APATPAAPALLPLWLL), as the model molecule and studied its conformational regulation and antimicrobial activity through amino acid substitutions and N -terminal lipidation. The structural and morphological transitions of the peptide self-assemblies were investigated using circular dichroism and transmission electron microscopy. Following amino acid substitution, the conformation of AL-16 (AKATKAAKALLKLWLL) did not change. Following N -terminal alkylation, the C 8 -AL-16 and C 12 -AL-16 conformations changed from random coil to β-sheet or α-helix, and the self-assembly changed from nanofibers to nanospheres. AL-16, C 8 -AL-16, and C 8 -AL-16 presented significant antimicrobial activity against Pseudomonas and Shewanella at low concentrations. N -terminal alkylation effectively extended the shelf life of Litopenaeus vannamei . These results support the application of natural AMPs., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies., (© 2023. Springer Nature Limited.)

Background: The implementation of deep learning models for medical image classification poses significant challenges, including gradual performance degradation and limited adaptability to new diseases. However, frequent retraining of models is unfeasible and raises concerns about healthcare privacy due to the retention of prior patient data. To address these issues, this study investigated privacy-preserving continual learning methods as an alternative solution., Methods: We evaluated twelve privacy-preserving non-storage continual learning algorithms based deep learning models for classifying retinal diseases from public optical coherence tomography (OCT) images, in a class-incremental learning scenario. The OCT dataset comprises 108,309 OCT images. Its classes include normal (47.21%), drusen (7.96%), choroidal neovascularization (CNV) (34.35%), and diabetic macular edema (DME) (10.48%). Each class consisted of 250 testing images. For continuous training, the first task involved CNV and normal classes, the second task focused on DME class, and the third task included drusen class. All selected algorithms were further experimented with different training sequence combinations. The final model's average class accuracy was measured. The performance of the joint model obtained through retraining and the original finetune model without continual learning algorithms were compared. Additionally, a publicly available medical dataset for colon cancer detection based on histology slides was selected as a proof of concept, while the CIFAR10 dataset was included as the continual learning benchmark., Results: Among the continual learning algorithms, Brain-inspired-replay (BIR) outperformed the others in the continual learning-based classification of retinal diseases from OCT images, achieving an accuracy of 62.00% (95% confidence interval: 59.36-64.64%), with consistent top performance observed in different training sequences. For colon cancer histology classification, Efficient Feature Transformations (EFT) attained the highest accuracy of 66.82% (95% confidence interval: 64.23-69.42%). In comparison, the joint model achieved accuracies of 90.76% and 89.28%, respectively. The finetune model demonstrated catastrophic forgetting in both datasets., Conclusion: Although the joint retraining model exhibited superior performance, continual learning holds promise in mitigating catastrophic forgetting and facilitating continual model updates while preserving privacy in healthcare deep learning models. Thus, it presents a highly promising solution for the long-term clinical deployment of such models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Verma, Jin, Zhou, Huang, Tan, Choong, Tan, Gao, Xu, Ting and Liu.)

Objective: To develop and validate a deep learning algorithm that could automate the annotation of scleral spur (SS) and segmentation of anterior chamber (AC) structures for measurements of AC, iris, and angle width parameters in anterior segment OCT (ASOCT) scans., Design: Cross-sectional study., Subjects: Data from 2 population-based studies (i.e., the Singapore Chinese Eye Study and Singapore Malay Eye Study) and 1 clinical study on angle-closure disease were included in algorithm development. A separate clinical study on angle-closure disease was used for external validation., Method: Image contrast of ASOCT scans were first enhanced with CycleGAN. We utilized a heat map regression approach with coarse-to-fine framework for SS annotation. Then, an ensemble network of U-Net, full resolution residual network, and full resolution U-Net was used for structure segmentation. Measurements obtained from predicted SSs and structure segmentation were measured and compared with measurements obtained from manual SS annotation and structure segmentation (i.e., ground truth)., Main Outcome Measures: We measured Euclidean distance and intraclass correlation coefficients (ICC) to evaluate SS annotation and Dice similarity coefficient for structure segmentation. The ICC, Bland-Altman plot, and repeatability coefficient were used to evaluate agreement and precision of measurements., Results: For SS annotation, our algorithm achieved a Euclidean distance of 124.7 μm, ICC ≥ 0.95, and a 3.3% error rate. For structure segmentation, we obtained Dice similarity coefficient ≥ 0.91 for cornea, iris, and AC segmentation. For angle width measurements, ≥ 95% of data points were within the 95% limits-of-agreement in Bland-Altman plot with insignificant systematic bias (all P > 0.12). The ICC ranged from 0.71-0.87 for angle width measurements, 0.54 for IT750, 0.83-0.85 for other iris measurements, and 0.89-0.99 for AC measurements. Using the same SS coordinates from a human expert, measurements obtained from our algorithm were generally less variable than measurements obtained from a semiautomated angle assessment program., Conclusion: We developed a deep learning algorithm that could automate SS annotation and structure segmentation in ASOCT scans like human experts, in both open-angle and angle-closure eyes. This algorithm reduces the time needed and subjectivity in obtaining ASOCT measurements., Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article., (© 2023 Published by Elsevier Inc. on behalf of the American Academy of Ophthalmology.)

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform., Competing Interests: Competing interests These authors declare the following competing interests: B.R. is on the Regeneron advisory board. M.M.A. receives consulting fees from Genentech, Bristol-Myers Squibb, Merck, AstraZeneca, Maverick, Blueprint Medicine, Mirati, Amgen, Novartis, EMD Serono and Gritstone and research funding (to the Dana–Farber Cancer Institute) from AstraZeneca, Lilly, Genentech, Bristol-Myers Squibb and Amgen. N.P. is a scientific advisor to Caresyntax. V.N. is employed by Google and owns stock as part of a standard compensation package. The other authors declare no competing interests.

Background: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown., Methods: In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images., Results: With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively., Conclusions: The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 by North American Neuro-Ophthalmology Society.)

To investigate the differences of volatile and non-volatile metabolites between oyster enzymatic hydrolysates and boiling concentrates, molecular sensory analysis and untargeted metabolomics were employed. "Grassy," "fruity," "oily/fatty," "fishy," and "metallic" were identified as sensory attributes used to evaluate different processed oyster homogenates. Sixty-nine and 42 volatiles were identified by gas chromatography-ion mobility spectrometry and gas chromatography-mass spectrometry, respectively. Pentanal, 1-penten-3-ol, hexanal, ( E )-2-pentenal, heptanal, ( E )-2-hexenal, 4-octanone, ( E )-4-heptenal, 3-octanone, octanal, nonanal, 1-octen-3-ol, benzaldehyde, ( E )-2-nonenal, and ( E , Z )-2,6-nonadienal were detected as the key odorants (OAV > 1) after enzymatic hydrolysis. Hexanal, ( E )-4-heptenal, and ( E )-2-pentenal were significantly associated with off-odor, and 177 differential metabolites were classified. Aspartate, glutamine, alanine, and arginine were the key precursors affecting the flavor profile. Linking sensory descriptors to volatile and nonvolatile components of different processed oyster homogenates will provide information for the process and quality improvement of oyster products., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Fermented peppers are usually obtained by the spontaneous fermentation of microorganisms attached to fresh peppers, and the variable microbial composition would lead to inconsistencies in flavor between batches. To demonstrate the roles of microorganisms in flavor formation, the core microbes closely associated with the key aroma compounds of fermented pepper paste were screened and validated in this study. Lactobacillus was the dominant bacterial genus in fermented pepper paste, whereas the main fungal genera were Alternaria and Kazachstania. Nine strains of the genera Lactobacillus, Weissella, Bacillus, Zygosaccharomyces, Kazachstania, Debaryomyces, and Pichia were isolated from fermented pepper paste. Eleven key aroma compounds were identified using gas chromatography combined with olfactometry and relative odor activity values. Correlation analysis showed that Zygosaccharomyces and Kazachstania were positively correlated with the majority of the key aroma compounds, whereas Lactobacillus was negatively correlated with them. Thus, Zygosaccharomyces and Kazachstania were identified as core genera associated with the key odorants. Finally, Zygosaccharomyces bisporus, Kazachstania humilis, and Lactiplantibacillus plantarum were used as starter cultures for fermented peppers, confirming that Z. bisporus and K. humilis were more beneficial for the key aroma compounds (e.g., acetate, linalool, and phenyl ethanol) rather than L. plantarum. This study contributed to understanding the flavor formation mechanism and provided references for the quality control of food fermentation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The effect of oral processing on flavor release and change in composition of steamed sturgeon meat was investigated. Oral processing caused changes in the concentrations of taste compounds including amino acids, 5'-nucleotides, organic acids, and Na + . Sensory omics demonstrated that the concentrations of 12 volatile compounds increased significantly ( p  < 0.05) during the initial stage of oral processing. There is no significant difference in microstructure, texture, and particle size of meat bolus. The top fifteen differential lipids which including eight phospholipids in all processed samples significantly ( p  < 0.05) correlated with the flavor release. A total of 589 differential proteins were detected in three samples with different chewing times (0, 12, and 30 s). Analysis of the correlations between odorants and 19 differential proteins was performed. Enriched pathways including fatty acid degradation, valine, leucine and isoleucine degradation, glycine, serine and threonine metabolism, and arachidonic acid metabolism were associated with flavor release during oral processing. This study aimed to investigate potential links between flavor release and biological processes during oral processing from a proteomics perspective., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

New insights revealing the flavor formation of unrinsed mixed sturgeon surimi with chicken breast were evaluated. Seventy-two volatile compounds were identified by gas chromatography-ion mobility spectrometry among the 11 surimi sample groups. The addition of 40% chicken breast caused changes in the concentrations of amino acids, 5'-nucleotides, and organic acids. Sensory attributes of balsamic, waxy, green, fresh, fatty, citrus, and aldehydic were marked when corelated with 125 volatiles identified by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. A total of 357 different lipids were identified through UPLC-Q-Orbitrap. Analysis of the correlations between flavor-active compounds and 16 different lipids revealed that various pathways, including the degradation of triglycerides, the biosynthesis of phosphatidylcholine, and the biosynthesis of lysine, serine, and methionine, were associated with flavor formation. This study provides a theoretical basis for the development of sturgeon processing industry and surimi products from the perspective of lipid changes., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we report the development and validation of a retinal photograph-based, deep-learning algorithm for automated detection of visually significant cataracts, using more than 25,000 images from population-based studies. In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 96.6%. External testing performed across three studies showed AUROCs of 91.6-96.5%. In a separate test set of 186 eyes, we further compared the algorithm's performance with 4 ophthalmologists' evaluations. The algorithm performed comparably, if not being slightly more superior (sensitivity of 93.3% versus 51.7-96.6% by ophthalmologists and specificity of 99.0% versus 90.7-97.9% by ophthalmologists). Our findings show the potential of a retinal photograph-based screening tool for visually significant cataracts among older adults, providing more appropriate referrals to tertiary eye centers., (© 2022. The Author(s).)

Background: By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability., Methods: In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China., Findings: The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0·969 (95% CI 0·959-0·977) or higher for myopic macular degeneration and 0·913 (0·906-0·920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0·978 [0·957-0·994] for myopic macular degeneration and 0·973 [0·941-0·995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries., Interpretation: Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine., Funding: None., Competing Interests: Declaration of interests DSWT and TYW are co-inventors, with patents pending, for a deep learning system for diabetic retinopathy, glaucoma, and age-related macular degeneration (SG Non-Provisional Application number 10201706186V), and a computer-implemented method for training an image classifier using weakly annotated training data (SG Provisional Patent Application number 10201901083Y), and are co-founders and shareholders of EyRIS, Singapore. HLin has a patent pending for a system and method for medical data sharing using blockchain technology (distinct from the blockchain platform presented in this study; patent number CN: 202011401658.5). THR has two patents pending relating to cardiovascular and cardio-cerebrovascular disease and eye images (cardiovascular disease diagnosis assistant method and apparatus: 10–2018–0166720(KR), 10–2018–0166721(KR), 10–2018–0166722(KR), PCT/KR2018/016388; and method for predicting cardio-cerebrovascular disease using eye image: 10–2017–0175865(KR), and was a scientific advisor to and is a stockholder for Medi Whale. YLW is an employee of Essilor International. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment., Methods: In this proof-of-concept study, using retinal fundus images from 15 175 eyes with complete data related to best-corrected visual acuity or pinhole visual acuity from the Singapore Epidemiology of Eye Diseases Study, we first developed a single-modality deep learning algorithm based on retinal photographs alone for detection of any disease-related visual impairment (defined as eyes from patients with major eye diseases and best-corrected visual acuity of <20/40), and moderate or worse disease-related visual impairment (eyes with disease and best-corrected visual acuity of <20/60). After development of the algorithm, we tested it internally, using a new set of 3803 eyes from the Singapore Epidemiology of Eye Diseases Study. We then tested it externally using three population-based studies (the Beijing Eye study [6239 eyes], Central India Eye and Medical study [6526 eyes], and Blue Mountains Eye Study [2002 eyes]), and two clinical studies (the Chinese University of Hong Kong's Sight Threatening Diabetic Retinopathy study [971 eyes] and the Outram Polyclinic Study [1225 eyes]). The algorithm's performance in each dataset was assessed on the basis of the area under the receiver operating characteristic curve (AUC)., Findings: In the internal test dataset, the AUC for detection of any disease-related visual impairment was 94·2% (95% CI 93·0-95·3; sensitivity 90·7% [87·0-93·6]; specificity 86·8% [85·6-87·9]). The AUC for moderate or worse disease-related visual impairment was 93·9% (95% CI 92·2-95·6; sensitivity 94·6% [89·6-97·6]; specificity 81·3% [80·0-82·5]). Across the five external test datasets (16 993 eyes), the algorithm achieved AUCs ranging between 86·6% (83·4-89·7; sensitivity 87·5% [80·7-92·5]; specificity 70·0% [66·7-73·1]) and 93·6% (92·4-94·8; sensitivity 87·8% [84·1-90·9]; specificity 87·1% [86·2-88·0]) for any disease-related visual impairment, and the AUCs for moderate or worse disease-related visual impairment ranged between 85·9% (81·8-90·1; sensitivity 84·7% [73·0-92·8]; specificity 74·4% [71·4-77·2]) and 93·5% (91·7-95·3; sensitivity 90·3% [84·2-94·6]; specificity 84·2% [83·2-85·1])., Interpretation: This proof-of-concept study shows the potential of a single-modality, function-focused tool in identifying visual impairment related to major eye diseases, providing more timely and pinpointed referral of patients with disease-related visual impairment from the community to tertiary eye hospitals., Funding: National Medical Research Council, Singapore., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Purpose of Review: The use of artificial intelligence (AI) in ophthalmology has increased dramatically. However, interpretation of these studies can be a daunting prospect for the ophthalmologist without a background in computer or data science. This review aims to share some practical considerations for interpretation of AI studies in ophthalmology., Recent Findings: It can be easy to get lost in the technical details of studies involving AI. Nevertheless, it is important for clinicians to remember that the fundamental questions in interpreting these studies remain unchanged - What does this study show, and how does this affect my patients? Being guided by familiar principles like study purpose, impact, validity, and generalizability, these studies become more accessible to the ophthalmologist. Although it may not be necessary for nondomain experts to understand the exact AI technical details, we explain some broad concepts in relation to AI technical architecture and dataset management., Summary: The expansion of AI into healthcare and ophthalmology is here to stay. AI systems have made the transition from bench to bedside, and are already being applied to patient care. In this context, 'AI education' is crucial for ophthalmologists to be confident in interpretation and translation of new developments in this field to their own clinical practice.

The aim of this study was to evaluate the effect of γ-aminobutyric acid (GABA) treatment on the enzymatic browning of fresh-cut potatoes. The browning index and activities of browning and defense-related enzymes were analyzed after 0, 1, 2, 3, 4, 5, and 6 days of storage at 4 °C. The results showed that the treatment with 20 g/L GABA for 10 min significantly retarded the browning of fresh-cut potatoes. GABA inhibited the browning of fresh-cut potatoes by enhancing the activities of catalase and superoxide dismutase, and decreasing the activities of polyphenol oxidase and reactive oxygen species. The results suggest that GABA plays an important role in reducing the browning of fresh-cut potatoes. Hence, GABA treatment is a promising approach for reducing the browning and maintaining the quality of fresh-cut potatoes.

Recent evidence suggests that the endoplasmic reticulum stress (ERS)-thioredoxin-interacting protein (TXNIP)-inflammation chain contributes to diabetic renal injury. The aim of the current study was to investigate whether total glucosides of peony (TGP) could inhibit ERS and attenuate up-regulation of TXNIP in the kidneys of rats with streptozotocin-induced diabetes. TGP was orally administered daily at a dose of 50, 100, or 200 mg/kg for 8 weeks. The expression of glucose-regulated protein 78 (GRP78), phospho-protein kinase RNA-like ER kinase (p-PERK), phosphor- eukaryotic translation initiation factor 2α (p-eIF2α), C/EBP-homologous protein (CHOP), and TXNIP was assessed. Results indicated that TGP significantly decreased diabetes-induced albuminuria and it acted by down-regulating activation of the ERS-TXNIP-inflammation chain in the kidneys of diabetic rats. These findings indicate that renoprotection from TGP in diabetic rats possibly contributed to inhibition of ERS and decreased expression of TXNIP. These findings also offer a new perspective from which to study the molecular mechanisms of diabetic nephropathy and prevent its progression.