Application and visualization study of an intelligence-assisted classification model for common eye diseases using B-mode ultrasound images.

Aim: Conventional approaches to diagnosing common eye diseases using B-mode ultrasonography are labor-intensive and time-consuming, must requiring expert intervention for accuracy. This study aims to address these challenges by proposing an intelligence-assisted analysis five-classification model for diagnosing common eye diseases using B-mode ultrasound images. Methods: This research utilizes 2064 B-mode ultrasound images of the eye to train a novel model integrating artificial intelligence technology. Results: The ConvNeXt-L model achieved outstanding performance with an accuracy rate of 84.3% and a Kappa value of 80.3%. Across five classifications (no obvious abnormality, vitreous opacity, posterior vitreous detachment, retinal detachment, and choroidal detachment), the model demonstrated sensitivity values of 93.2%, 67.6%, 86.1%, 89.4%, and 81.4%, respectively, and specificity values ranging from 94.6% to 98.1%. F1 scores ranged from 71% to 92%, while AUC values ranged from 89.7% to 97.8%. Conclusion: Among various models compared, the ConvNeXt-L model exhibited superior performance. It effectively categorizes and visualizes pathological changes, providing essential assisted information for ophthalmologists and enhancing diagnostic accuracy and efficiency.

Squeeze-and-excitation-attention-based mobile vision transformer for grading recognition of bladder prolapse in pelvic MRI images.

BACKGROUND: Bladder prolapse is a common clinical disorder of pelvic floor dysfunction in women, and early diagnosis and treatment can help them recover. Pelvic magnetic resonance imaging (MRI) is one of the most important methods used by physicians to diagnose bladder prolapse; however, it is highly subjective and largely dependent on the clinical experience of physicians. The application of computer-aided diagnostic techniques to achieve a graded diagnosis of bladder prolapse can help improve its accuracy and shorten the learning curve. PURPOSE: The purpose of this study is to combine convolutional neural network (CNN) and vision transformer (ViT) for grading bladder prolapse in place of traditional neural networks, and to incorporate attention mechanisms into mobile vision transformer (MobileViT) for assisting in the grading of bladder prolapse. METHODS: This study focuses on the grading of bladder prolapse in pelvic organs using a combination of a CNN and a ViT. First, this study used MobileNetV2 to extract the local features of the images. Next, a ViT was used to extract the global features by modeling the non-local dependencies at a distance. Finally, a channel attention module (i.e., squeeze-and-excitation network) was used to improve the feature extraction network and enhance its feature representation capability. The final grading of the degree of bladder prolapse was thus achieved. RESULTS: Using pelvic MRI images provided by a Huzhou Maternal and Child Health Care Hospital, this study used the proposed method to grade patients with bladder prolapse. The accuracy, Kappa value, sensitivity, specificity, precision, and area under the curve of our method were 86.34%, 78.27%, 83.75%, 95.43%, 85.70%, and 95.05%, respectively. In comparison with other CNN models, the proposed method performed better. CONCLUSIONS: Thus, the model based on attention mechanisms exhibits better classification performance than existing methods for grading bladder prolapse in pelvic organs, and it can effectively assist physicians in achieving a more accurate bladder prolapse diagnosis.

Seeding Atomic Silver into Internal Lattice Sites of Transition Metal Oxide for Advanced Electrocatalysis.

Transition metal oxides (TMOs) are widely studied for loading of various catalysts due to their low cost and high structure flexibility. However, the prevailing close-packed nature of most TMOs crystals has restricted the available loading sites to surface only, while their internal bulk lattice remains unactuated due to the inaccessible narrow space that blocks out most key reactants and/or particulate catalysts. Herein, using tunnel-structured MnO2, this study demonstrates how TMO's internal lattice space can be activated as extra loading sites for atomic Ag in addition to the conventional surface-only loading, via which a dual-form Ag catalyst within MnO2 skeleton is established. In this design, not only faceted Ag nanoparticles are confined onto MnO2 surface by coherent lattice-sharing, Ag atomic strings are also seeded deep into the sub-nanoscale MnO2 tunnel lattice, enriching the catalytically active sites. Tested for electrochemical CO2 reduction reaction (eCO2RR), such dual-form catalyst exhibits a high Faradaic efficiency (94%), yield (67.3 mol g-1 h-1) and durability (≈48 h) for CO production, exceeding commercial Ag nanoparticles and most Ag-based electrocatalysts. Theoretical calculations further reveal the concurrent effect of such dual-form catalyst featuring facet-dependent eCO2RR for Ag nanoparticles and lattice-confined eCO2RR for Ag atomic strings, inspiring the future design of catalyst-substrate configuration.

Two-dimensional Cu Plates with Steady Fluid Fields for High-rate Nitrate Electroreduction to Ammonia and Efficient Zn-Nitrate Batteries.

Nitrate electroreduction reaction (eNO3 -RR) to ammonia (NH3) provides a promising strategy for nitrogen utilization, while achieving high selectivity and durability at an industrial scale has remained challenging. Herein, we demonstrated that the performance of eNO3 -RR could be significantly boosted by introducing two-dimensional Cu plates as electrocatalysts and eliminating the general carrier gas to construct a steady fluid field. The developed eNO3 -RR setup provided superior NH3 Faradaic efficiency (FE) of 99 %, exceptional long-term electrolysis for 120 h at 200 mA cm-2, and a record-high yield rate of 3.14 mmol cm-2 h-1. Furthermore, the proposed strategy was successfully extended to the Zn-nitrate battery system, providing a power density of 12.09 mW cm-2 and NH3 FE of 85.4 %, outperforming the state-of-the-art eNO3 -RR catalysts. Coupled with the COMSOL multiphysics simulations and in situ infrared spectroscopy, the main contributor for the high-efficiency NH3 production could be the steady fluid field to timely rejuvenate the electrocatalyst surface during the electrocatalysis.

Research on an artificial intelligence-based myopic maculopathy grading method using EfficientNet.

PURPOSE: We aimed to develop an artificial intelligence-based myopic maculopathy grading method using EfficientNet to overcome the delayed grading and diagnosis of different myopic maculopathy degrees. METHODS: The cooperative hospital provided 4642 healthy and myopic maculopathy color fundus photographs, comprising the four degrees of myopic maculopathy and healthy fundi. The myopic maculopathy grading models were trained using EfficientNet-B0 to EfficientNet-B7 models. The diagnostic results were compared with those of the VGG16 and ResNet50 classification models. The leading evaluation indicators were sensitivity, specificity, F1 score, area under the receiver operating characteristic (ROC) curve area under curve (AUC), 95% confidence interval, kappa value, and accuracy. The ROC curves of the ten grading models were also compared. RESULTS: We used 1199 color fundus photographs to evaluate the myopic maculopathy grading models. The size of the EfficientNet-B0 myopic maculopathy grading model was 15.6 MB, and it had the highest kappa value (88.32%) and accuracy (83.58%). The model's sensitivities to diagnose tessellated fundus (TF), diffuse chorioretinal atrophy (DCA), patchy chorioretinal atrophy (PCA), and macular atrophy (MA) were 96.86%, 75.98%, 64.67%, and 88.75%, respectively. The specificity was above 93%, and the AUCs were 0.992, 0.960, 0.964, and 0.989, respectively. CONCLUSION: The EfficientNet models were used to design grading diagnostic models for myopic maculopathy. Based on the collected fundus images, the models could diagnose a healthy fundus and four types of myopic maculopathy. The models might help ophthalmologists to make preliminary diagnoses of different degrees of myopic maculopathy.

Recent Progress Toward Electrocatalytic Conversion of Nitrobenzene.

Despite that extensive efforts have been dedicated to the search for advanced catalysts to boost the electrocatalytic nitrobenzene reduction reaction (eNBRR), its progress is severely hampered by the limited understanding of the relationship between catalyst structure and its catalytic performance. Herein, this review aims to bridge such a gap by first analyzing the eNBRR pathway to present the main influential factors, such as electrolyte feature, applied potential, and catalyst structure. Then, the recent advancements in catalyst design for eNBRR are comprehensively summarized, particularly about the impacts of chemical composition, morphology, and crystal facets on regulating the local microenvironment, electron and mass transport for boosting catalytic performance. Finally, the future research of eNBRR is also proposed from the perspectives of performance enhancement, expansion of product scope, in-depth understanding of the reaction mechanism, and acceleration of the industrialization process through the integration of upstream and downstream technologies.

Electrocatalytic CO2 Reduction to Ethylene: From Advanced Catalyst Design to Industrial Applications.

The value-added chemicals, monoxide, methane, ethylene, ethanol, ethane, and so on, can be efficiently generated through the electrochemical CO2 reduction reaction (eCO2 RR) when equipped with suitable catalysts. Among them, ethylene is particularly important as a chemical feedstock for petrochemical manufacture. However, despite its high Faradaic efficiency achievable at relatively low current densities, the substantial enhancement of ethylene selectivity and stability at industrial current densities poses a formidable challenge. To facilitate the industrial implementation of eCO2 RR for ethylene production, it is imperative to identify key strategies and potential solutions through comprehending the recent advancements, remaining challenges, and future directions. Herein, the latest and innovative catalyst design strategies of eCO2 RR to ethylene are summarized and discussed, starting with the properties of catalysts such as morphology, crystalline, oxidation state, defect, composition, and surface engineering. The review subsequently outlines the related important state-of-the-art technologies that are essential in driving forward eCO2 RR to ethylene into practical applications, such as CO2 capture, product separation, and downstream reactions. Finally, a greenhouse model that integrates CO2 capture, conversion, storage, and utilization is proposed to present an ideal perspective direction of eCO2 RR to ethylene.

RICH2 decreases the mitochondrial number and affects mitochondrial localization in diffuse low-grade glioma-related epilepsy.

Epilepsy, a common complication of diffuse low-grade gliomas (DLGGs; diffuse oligodendroglioma and astrocytoma collectively), severely compromises the quality of life of patients. DLGG epileptogenicity may primarily be generated by interactions between the tumor and the neocortex. Neuronal uptake of dysfunctional mitochondria from the extracellular environment can lead to abnormal neuronal discharge. Mitochondrial dysfunction is frequently observed in gliomas that can transmigrate across the plasma membranes. Here, we examined the role of the Rho GTPase-activating protein 44 (RICH2) in mitochondrial dynamics and DLGG-related epilepsy. We investigated the association between mitochondrial and RICH2 expression in human DLGG tissues using immunohistochemistry. We examined the association between RICH2 and epilepsy in nude mouse glioma models by electrophysiology. The effect of RICH2 on mitochondrial morphology and calcium motility were assessed by single cell fluorescence microscopy. Quantitative RT-PCR (qRT-PCR) and Western blot analysis were performed to characterize RICH2 induced expression changes in the genes related to mitochondrial dynamics, mitogenesis and mitochondrial function. We found that RICH2 expression was higher in oligodendroglioma than in astrocytoma and was correlated with better prognosis and higher epilepsy rate in patients. The expression of mitochondria may be associated with clinical DLGG-related epilepsy and reduced by RICH2 overexpression. And RICH2 could promote DLGG-related epilepsy in tumorigenic nude mice. RICH2 overexpression decreased calcium flow and the mitochondria released from glioma cells (SW1088 and U251) into the extracellular environment, potentially via downregulation of MFN-1/MFN-2 levels which suggests reduced mitochondrial fusion. In addition, we observed decreased mitochondrial trafficking into neurons (released from glioma cells and trafficked into neurons), which could explain the higher incidence of DLGG-related epilepsy due to reduced neuroprotection. Furthermore, RICH2 downregulated MAPK/ERK/HIF-1 pathway. In conclusion, these results suggest that RICH2 could promote epilepsy by (i) inhibiting mitochondrial fusion via MFN downregulation and Drp-1 upregulation; (ii) altering the MAPK/ERK/Hif-1 signaling axis. RICH2 may be a potential target in the treatment of DLGG-related epilepsy.

Artificial intelligence assisted pterygium diagnosis: current status and perspectives.

Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm.

AIM: To conduct a classification study of high myopic maculopathy (HMM) using limited datasets, including tessellated fundus, diffuse chorioretinal atrophy, patchy chorioretinal atrophy, and macular atrophy, and minimize annotation costs, and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification. METHODS: The optimized ALFA-Mix algorithm (ALFA-Mix+) was compared with five algorithms, including ALFA-Mix. Four models, including ResNet18, were established. Each algorithm was combined with four models for experiments on the HMM dataset. Each experiment consisted of 20 active learning rounds, with 100 images selected per round. The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+ outperformed other algorithms. Finally, this study employed six models, including EfficientFormer, to classify HMM. The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+ algorithm to achieve satisfactory classification results with a small dataset. RESULTS: ALFA-Mix+ outperforms other algorithms with an average superiority of 16.6, 14.75, 16.8, and 16.7 rounds in terms of accuracy, sensitivity, specificity, and Kappa value, respectively. This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images. The EfficientFormer achieved the best results with an accuracy, sensitivity, specificity, and Kappa value of 0.8821, 0.8334, 0.9693, and 0.8339, respectively. Therefore, by combining ALFA-Mix+ with EfficientFormer, this study achieved results with an accuracy, sensitivity, specificity, and Kappa value of 0.8964, 0.8643, 0.9721, and 0.8537, respectively. CONCLUSION: The ALFA-Mix+ algorithm reduces the required samples without compromising accuracy. Compared to other algorithms, ALFA-Mix+ outperforms in more rounds of experiments. It effectively selects valuable samples compared to other algorithms. In HMM classification, combining ALFA-Mix+ with EfficientFormer enhances model performance, further demonstrating the effectiveness of ALFA-Mix+.

Prediction of spherical equivalent refraction and axial length in children based on machine learning.

Purpose: Recently, the proportion of patients with high myopia has shown a continuous growing trend, more toward the younger age groups. This study aimed to predict the changes in spherical equivalent refraction (SER) and axial length (AL) in children using machine learning methods. Methods: This study is a retrospective study. The cooperative ophthalmology hospital of this study collected data on 179 sets of childhood myopia examinations. The data collected included AL and SER from grades 1 to 6. This study used the six machine learning models to predict AL and SER based on the data. Six evaluation indicators were used to evaluate the prediction results of the models. Results: For predicting SER in grade 6, grade 5, grade 4, grade 3, and grade 2, the best results were obtained through the multilayer perceptron (MLP) algorithm, MLP algorithm, orthogonal matching pursuit (OMP) algorithm, OMP algorithm, and OMP algorithm, respectively. The R2 of the five models were 0.8997, 0.7839, 0.7177, 0.5118, and 0.1758, respectively. For predicting AL in grade 6, grade 5, grade 4, grade 3, and grade 2, the best results were obtained through the Extra Tree (ET) algorithm, MLP algorithm, kernel ridge (KR) algorithm, KR algorithm, and MLP algorithm, respectively. The R2 of the five models were 0.7546, 0.5456, 0.8755, 0.9072, and 0.8534, respectively. Conclusion: Therefore, in predicting SER, the OMP model performed better than the other models in most experiments. In predicting AL, the KR and MLP models were better than the other models in most experiments.

Primary pulmonary hyalinizing clear cell carcinoma with pseudopapillary structures and abundant cysts filled with mucus.

Pulmonary hyalinizing clear cell carcinoma (HCCC) is a new and rare form of lung salivary gland tumor. Only twenty-two cases have been reported in the literature to date. Furthermore, their clinicopathological features have not been fully characterized. In this paper, we describe the clinicopathological characteristics, immunohistochemical features, and molecular genetic changes in two HCCC cases. We also simultaneously reviewed related literature on similar cases reported. Of the two cases, one was of a 58-year-old man with a 4.3 cm lung tumor, which was the largest among all previously reported cases. The tumor showed an infiltrative growth pattern and perineural and vascular invasion microscopically. Moreover, nuclear grooves, high mitotic figures, and comedo necrosis were observed in addition to classic morphological features. More importantly, rare pseudopapillary structures were observed. The second case was of a 60-year-old woman in whom the tumor was mainly composed of multiple cysts filled with mucus. The remaining focal solid areas of the tumor comprised clear and acidophilic cells embedded in the hyalinizing stroma. Immunohistochemical analysis revealed that the tumor cells of both cases were positive for CK5/6, p40, and p63 expression, but negative for napsin A, TTF-1, and SOX10 expression. The HCCC diagnosis in both cases was validated by fluorescence in-situ hybridization (FISH) examination, which showed Ewing sarcoma breakpoint region 1-activating transcription factor 1 (EWSR1-ATF1) gene fusion. Primary pulmonary HCCC is a rare lung tumor originating from the bronchial mucosa, and its histological features may vary, such as rare pseudopapillary structures and abundant cysts. Thus, the diagnosis should be a combined analysis of histopathological characteristics with immunophenotype and molecular examination, including EWSR1-ATF1 gene fusion detection.

Pterygium Screening and Lesion Area Segmentation Based on Deep Learning.

A two-category model and a segmentation model of pterygium were proposed to assist ophthalmologists in establishing the diagnosis of ophthalmic diseases. A total of 367 normal anterior segment images and 367 pterygium anterior segment images were collected at the Affiliated Eye Hospital of Nanjing Medical University. AlexNet, VGG16, ResNet18, and ResNet50 models were used to train the two-category pterygium models. A total of 150 normal and 150 pterygium anterior segment images were used to test the models, and the results were compared. The main evaluation indicators, including sensitivity, specificity, area under the curve, kappa value, and receiver operator characteristic curves of the four models, were compared. Simultaneously, 367 pterygium anterior segment images were used to train two improved pterygium segmentation models based on PSPNet. A total of 150 pterygium images were used to test the models, and the results were compared with those of the other four segmentation models. The main evaluation indicators included mean intersection over union (MIOU), IOU, mean average precision (MPA), and PA. Among the two-category models of pterygium, the best diagnostic result was obtained using the VGG16 model. The diagnostic accuracy, kappa value, diagnostic sensitivity of pterygium, diagnostic specificity of pterygium, and F1-score were 99%, 98%, 98.67%, 99.33%, and 99%, respectively. Among the pterygium segmentation models, the double phase-fusion PSPNet model had the best results, with MIOU, IOU, MPA, and PA of 86.57%, 78.1%, 92.3%, and 86.96%, respectively. This study designed a pterygium two-category model and a pterygium segmentation model for the images of the normal anterior and pterygium anterior segments, which could help patients self-screen easily and assist ophthalmologists in establishing the diagnosis of ophthalmic diseases and marking the actual scope of surgery.

Classification of dry and wet macular degeneration based on the ConvNeXT model.

Purpose: To assess the value of an automated classification model for dry and wet macular degeneration based on the ConvNeXT model. Methods: A total of 672 fundus images of normal, dry, and wet macular degeneration were collected from the Affiliated Eye Hospital of Nanjing Medical University and the fundus images of dry macular degeneration were expanded. The ConvNeXT three-category model was trained on the original and expanded datasets, and compared to the results of the VGG16, ResNet18, ResNet50, EfficientNetB7, and RegNet three-category models. A total of 289 fundus images were used to test the models, and the classification results of the models on different datasets were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), accuracy, and kappa. Results: Using 289 fundus images, three-category models trained on the original and expanded datasets were assessed. The ConvNeXT model trained on the expanded dataset was the most effective, with a diagnostic accuracy of 96.89%, kappa value of 94.99%, and high diagnostic consistency. The sensitivity, specificity, F1-score, and AUC values for normal fundus images were 100.00, 99.41, 99.59, and 99.80%, respectively. The sensitivity, specificity, F1-score, and AUC values for dry macular degeneration diagnosis were 87.50, 98.76, 90.32, and 97.10%, respectively. The sensitivity, specificity, F1-score, and AUC values for wet macular degeneration diagnosis were 97.52, 97.02, 96.72, and 99.10%, respectively. Conclusion: The ConvNeXT-based category model for dry and wet macular degeneration automatically identified dry and wet macular degeneration, aiding rapid, and accurate clinical diagnosis.

Comparison of PSMA-TO-1 and PSMA-617 labeled with gallium-68, lutetium-177 and actinium-225.

BACKGROUND: PSMA-TO-1 ("Tumor-Optimized-1") is a novel PSMA ligand with longer circulation time than PSMA-617. We compared the biodistribution in subcutaneous tumor-bearing mice of PSMA-TO-1, PSMA-617 and PSMA-11 when labeled with 68Ga and 177Lu, and the survival after treatment with 225Ac-PSMA-TO-1/-617 in a murine model of disseminated prostate cancer. We also report dosimetry data of 177Lu-PSMA-TO1/-617 in prostate cancer patients. METHODS: First, PET images of 68Ga-PSMA-TO-1/-617/-11 were acquired on consecutive days in three mice bearing subcutaneous C4-2 xenografts. Second, 50 subcutaneous tumor-bearing mice received either 30 MBq of 177Lu-PSMA-617 or 177Lu-PSMA-TO-1 and were sacrificed at 1, 4, 24, 48 and 168 h for ex vivo gamma counting and biodistribution. Third, mice bearing disseminated lesions via intracardiac inoculation were treated with either 40 kBq of 225Ac-PSMA-617, 225Ac-PSMA-TO-1, or remained untreated and followed for survival. Additionally, 3 metastatic castration-resistant prostate cancer patients received 500 MBq of 177Lu-PSMA-TO-1 under compassionate use for dosimetry purposes. Planar images with an additional SPECT/CT acquisition were acquired for dosimetry calculations. RESULTS: Tumor uptake measured by PET imaging of 68Ga-labeled agents in mice was highest using PSMA-617, followed by PSMA-TO-1 and PSMA-11. 177Lu-PSMA tumor uptake measured by ex vivo gamma counting at subsequent time points tended to be greater for PSMA-TO-1 up to 1 week following treatment (p > 0.13 at all time points). This was, however, accompanied by increased kidney uptake and a 26-fold higher kidney dose of PSMA-TO-1 compared with PSMA-617 in mice. Mice treated with a single-cycle 225Ac-PSMA-TO-1 survived longer than those treated with 225Ac-PSMA-617 and untreated mice, respectively (17.8, 14.5 and 7.7 weeks, respectively; p < 0.0001). Kidney, salivary gland, bone marrow and mean ± SD tumor dose coefficients (Gy/GBq) for 177Lu-PSMA-TO-1 in patients #01/#02/#03 were 2.5/2.4/3.0, 1.0/2.5/2.3, 0.14/0.11/0.10 and 0.42 ± 0.03/4.45 ± 0.07/1.8 ± 0.57, respectively. CONCLUSIONS: PSMA-TO-1 tumor uptake tended to be greater than that of PSMA-617 in both preclinical and clinical settings. Mice treated with 225Ac-PSMA-TO-1 conferred a significant survival benefit compared to 225Ac-PSMA-617 despite the accompanying increased kidney uptake. In humans, PSMA-TO-1 dosimetry estimates suggest increased tumor absorbed doses; however, the kidneys, salivary glands and bone marrow are also exposed to higher radiation doses. Thus, additional preclinical studies are needed before further clinical use.

Role of the mucin-like glycoprotein FCGBP in mucosal immunity and cancer.

IgGFc-binding protein (FCGBP) is a mucin first detected in the intestinal epithelium. It plays an important role in innate mucosal epithelial defense, tumor metastasis, and tumor immunity. FCGBP forms disulfide-linked heterodimers with mucin-2 and members of the trefoil factor family. These formed complexes inhibit bacterial attachment to mucosal surfaces, affect the motility of pathogens, and support their clearance. Altered FCGBP expression levels may be important in the pathologic processes of Crohn's disease and ulcerative colitis. FCGBP is also involved in regulating the infiltration of immune cells into tumor microenvironments. Thus, the molecule is a valuable marker of tumor prognosis. This review summarizes the functional relevance and role of FCGBP in immune responses and disease development, and highlights the potential role in diagnosis and predicting tumor prognosis.

Automated measurement of the disc-fovea angle based on DeepLabv3.

Purpose: To assess the value of automatic disc-fovea angle (DFA) measurement using the DeepLabv3+ segmentation model. Methods: A total of 682 normal fundus image datasets were collected from the Eye Hospital of Nanjing Medical University. The following parts of the images were labeled and subsequently reviewed by ophthalmologists: optic disc center, macular center, optic disc area, and virtual macular area. A total of 477 normal fundus images were used to train DeepLabv3+, U-Net, and PSPNet model, which were used to obtain the optic disc area and virtual macular area. Then, the coordinates of the optic disc center and macular center were obstained by using the minimum outer circle technique. Finally the DFA was calculated. Results: In this study, 205 normal fundus images were used to test the model. The experimental results showed that the errors in automatic DFA measurement using DeepLabv3+, U-Net, and PSPNet segmentation models were 0.76°, 1.4°, and 2.12°, respectively. The mean intersection over union (MIoU), mean pixel accuracy (MPA), average error in the center of the optic disc, and average error in the center of the virtual macula obstained by using DeepLabv3+ model was 94.77%, 97.32%, 10.94 pixels, and 13.44 pixels, respectively. The automatic DFA measurement using DeepLabv3+ got the less error than the errors that using the other segmentation models. Therefore, the DeepLabv3+ segmentation model was finally chosen to measure DFA automatically. Conclusions: The DeepLabv3+ segmentation model -based automatic segmentation techniques can produce accurate and rapid DFA measurements.

Intermuscular coupling network analysis of upper limbs based on R-vine copula transfer entropy.

In the field of neuroscience, it is very important to evaluate the causal coupling characteristics between bioelectrical signals accurately and effectively. Transfer entropy is commonly used to analyze complex data, especially the causal relationship between data with non-linear, multidimensional characteristics. However, traditional transfer entropy needs to estimate the probability density function of the variable, which is computationally complex and unstable. In this paper, a new and effective method for entropy transfer is proposed, by means of applying R-vine copula function estimation. The effectiveness of R-vine copula transfer entropy is first verified on several simulations, and then applied to intermuscular coupling analysis to explore the characteristics of the intermuscular coupling network of muscles in non-fatigue and fatigue conditions. The experiment results show that as the muscle group enters the fatigue state, the community structure can be adjusted and the muscle nodes participating in the exercise are fully activated, enabling the two-way interaction between different communities. Finally, it comes to the conclusion that the proposed method can make accurate inferences about complex causal coupling. Moreover, the characteristics of the intermuscular coupling network in both non-fatigue and fatigue states can provide a new theoretical perspective for the diagnosis of neuromuscular fatigue and sports rehabilitation, which has good application value.

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy.

The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence each other. Numerous systemic diseases lead to special clinical manifestations and complications in the eyes. Typical diseases include diabetic retinopathy, hypertensive retinopathy, thyroid associated ophthalmopathy, optic neuromyelitis, and Behcet's disease. Systemic disease-related ophthalmopathy is usually a chronic disease, and the analysis of imaging markers is helpful for a comprehensive diagnosis of these diseases. Recently, artificial intelligence (AI) technology based on deep learning has rapidly developed, leading to numerous achievements and arousing widespread concern. Presently, AI technology has made significant progress in research on imaging markers of systemic disease-related ophthalmopathy; however, there are also many limitations and challenges. This article reviews the research achievements, limitations, and future prospects of AI image analysis technology in systemic disease-related ophthalmopathy.

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis.

Background: Artificial intelligence (AI) has been used in the research of ophthalmic disease diagnosis, and it may have an impact on medical and ophthalmic practice in the future. This study explores the general application and research frontier of artificial intelligence in ophthalmic disease detection. Methods: Citation data were downloaded from the Web of Science Core Collection database to evaluate the extent of the application of Artificial intelligence in ophthalmic disease diagnosis in publications from 1 January 2012, to 31 December 2021. This information was analyzed using CiteSpace.5.8. R3 and Vosviewer. Results: A total of 1,498 publications from 95 areas were examined, of which the United States was determined to be the most influential country in this research field. The largest cluster labeled "Brownian motion" was used prior to the application of AI for ophthalmic diagnosis from 2007 to 2017, and was an active topic during this period. The burst keywords in the period from 2020 to 2021 were system, disease, and model. Conclusion: The focus of artificial intelligence research in ophthalmic disease diagnosis has transitioned from the development of AI algorithms and the analysis of abnormal eye physiological structure to the investigation of more mature ophthalmic disease diagnosis systems. However, there is a need for further studies in ophthalmology and computer engineering.