DEVELOPMENT AND VALIDATION OF AN EXPLAINABLE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR MACULAR DISEASE DIAGNOSIS BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

PURPOSE: To develop and validate an artificial intelligence framework for identifying multiple retinal lesions at image level and performing an explainable macular disease diagnosis at eye level in optical coherence tomography images. METHODS: A total of 26,815 optical coherence tomography images were collected from 865 eyes, and 9 retinal lesions and 3 macular diseases were labeled by ophthalmologists, including diabetic macular edema and dry/wet age-related macular degeneration. We applied deep learning to classify retinal lesions at image level and random forests to achieve an explainable disease diagnosis at eye level. The performance of the integrated two-stage framework was evaluated and compared with human experts. RESULTS: On testing data set of 2,480 optical coherence tomography images from 80 eyes, the deep learning model achieved an average area under curve of 0.978 (95% confidence interval, 0.971-0.983) for lesion classification. In addition, random forests performed accurate disease diagnosis with a 0% error rate, which achieved the same accuracy as one of the human experts and was better than the other three experts. It also revealed that the detection of specific lesions in the center of macular region had more contribution to macular disease diagnosis. CONCLUSION: The integrated method achieved high accuracy and interpretability in retinal lesion classification and macular disease diagnosis in optical coherence tomography images and could have the potential to facilitate the clinical diagnosis.

Development and quantitative assessment of deep learning-based image enhancement for optical coherence tomography.

PURPOSE: To develop a deep learning-based framework to improve the image quality of optical coherence tomography (OCT) and evaluate its image enhancement effect with the traditional image averaging method from a clinical perspective. METHODS: 359 normal eyes and 456 eyes with various retinal conditions were included. A deep learning framework with high-resolution representation was developed to achieve image quality enhancement for OCT images. The quantitative comparisons, including expert subjective scores from ophthalmologists and three objective metrics of image quality (structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR) and contrast-to-noise ratio (CNR)), were performed between deep learning method and traditional image averaging. RESULTS: With the increase of frame count from 1 to 20, our deep learning method always obtained higher SSIM and PSNR values than the image averaging method while importing the same number of frames. When we selected 5 frames as inputs, the local objective assessment with CNR illustrated that the deep learning method had more obvious tissue contrast enhancement than averaging method. The subjective scores of image quality were all highest in our deep learning method, both for normal retinal structure and various retinal lesions. All the objective and subjective indicators had significant statistical differences (P < 0.05). CONCLUSION: Compared to traditional image averaging methods, our proposed deep learning enhancement framework can achieve a reasonable trade-off between image quality and scanning times, reducing the number of repeated scans.

Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema.

OBJECTIVES: To evaluate for the first time the performance of a deep learning method based on no-new-Net for fully automated segmentation and volumetric measurements of intracerebral hemorrhage (ICH), intraventricular extension of intracerebral hemorrhage (IVH), and perihematomal edema (PHE) in primary ICH on CT. METHODS: Three hundred and eighty primary ICH patients who underwent CT at hospital arrival were divided into a training cohort (n = 300) and a validation cohort (n = 80). An independent cohort with 80 patients was used for testing. Ground truth (segmentation masks) was manually generated by radiologists. Model performance on lesion segmentation and volumetric measurement of ICH, IVH, and PHE were evaluated by comparing the model results with the segmentations performed by radiologists. RESULTS: In the test cohort, the Dice scores of lesion segmentation were 0.92, 0.79, and 0.71 for ICH, IVH, and PHE, respectively. The sensitivities were 0.93 for ICH, 0.88 for IVH, and 0.81 for PHE. The positive predictive values were 0.92, 0.76, and 0.69 for ICH, IVH, and PHE, respectively. Excellent concordance (concordance correlation coefficients [CCCs] ≥ 0.98) of ICH and IVH and good concordance of PHE (CCCs ≥ 0.92) were demonstrated between manually and automatically measured volumes. The model took approximately 15 s to provide automatic segmentation and volume analysis for each patient. CONCLUSION: Our model demonstrates good reliability for automatic segmentation and volume measurement of ICH, IVH, and PHE in primary ICH, which can be useful to reduce the effort and time of doctors to calculate volumes of ICH, IVH, and PHE. KEY POINTS: • Deep learning algorithms can provide automatic and reliable assessment of intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT. • Non-contrast CT-based deep learning method can be helpful to provide efficient and accurate measurements of ICH, IVH, and PHE in primary ICH patients, thereby reducing the effort and time of doctors to segment and calculate volumes of ICH, IVH, and PHE in primary ICH patients.

Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.

Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time-consuming tasks require a more accurate and reproducible quantitative analysis method. We established derivation and validation cohorts composed of 400 Chinese patients with immunoglobulin A nephropathy (IgAN) retrospectively. Deep convolutional neural networks and biomedical image processing algorithms were implemented to locate glomeruli, identify glomerular lesions (global and segmental glomerular sclerosis, crescent, and none of the above), identify and quantify different intrinsic glomerular cells, and assess a network-based mesangial hypercellularity score in periodic acid-Schiff (PAS)-stained slides. Our framework achieved 93.1% average precision and 94.9% average recall for location of glomeruli, and a total Cohen's kappa of 0.912 [95% confidence interval (CI), 0.892-0.932] for glomerular lesion classification. The evaluation of global, segmental glomerular sclerosis, and crescents achieved Cohen's kappa values of 1.0, 0.776, 0.861, and 95% CI of (1.0, 1.0), (0.727, 0.825), (0.824, 0.898), respectively. The well-designed neural network can identify three kinds of intrinsic glomerular cells with 92.2% accuracy, surpassing the about 5-11% average accuracy of junior pathologists. Statistical interpretation shows that there was a significant difference (P value < 0.0001) between this analytic renal pathology system (ARPS) and four junior pathologists for identifying mesangial and endothelial cells, while that for podocytes was similar, with P value = 0.0602. In addition, this study indicated that the ratio of mesangial cells, endothelial cells, and podocytes within glomeruli from IgAN was 0.41:0.36:0.23, and the performance of mesangial score assessment reached a Cohen's kappa of 0.42 and 95% CI (0.18, 0.69). The proposed computer-aided diagnosis system has feasibility for quantitative analysis and auxiliary recognition of glomerular pathological features. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Optical coherence tomography-based short-term effect prediction of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration using sensitive structure guided network.

PURPOSE: To predict short-term anti-vascular endothelial growth factor (anti-VEGF) treatment responder/non-responder for neovascular age-related macular degeneration (nAMD) patients based on optical coherence tomography (OCT) images. METHODS: A total of 4944 OCT scans from 206 patients with nAMD were involved to develop and evaluate a responder/non-responder prediction method for the short-term effect of anti-VEGF therapy. A deep learning architecture named sensitive structure guided network (SSG-Net) was proposed to make the prediction leveraging a sensitive structure guidance module trained from pre- and post-treatment images. To verify its clinical efficiency, other 2 deep learning methods and 4 experienced ophthalmologists were involved to evaluate the performance of the developed model. RESULTS: For the testing dataset, SSG-Net could predict the response by an accuracy of 84.6% and an area under the receiver curve (AUC) of 0.83, with a sensitivity of 0.692 and specificity of 1. In contrast, the 2 compared deep learning methods achieved an accuracy of 65.4% with a sensitivity of 0.461 and specificity of 0.846, and an accuracy of 73.1% with a sensitivity of 0.692 and specificity of 0.846, respectively. The predicted accuracy for 4 experienced ophthalmologists was 53.8 to 76.9%, with sensitivity of 0.538 to 0.923 and specificity of 0.385 to 0.846, respectively. CONCLUSION: Our proposed SSG-Net shows effective prediction on the short-term efficacy of anti-VEGF treatment for nAMD patients. This technique could potentially help clinicians explain the necessity of anti-VEGF treatment to the potential responder and avoid unnecessary treatment for the non-responder.

The ethyl acetate extract of Wenxia Changfu Formula inhibits the carcinogenesis of lung adenocarcinoma by regulating PI3K-AKT signaling pathway.

Lung adenocarcinoma is the most common type of lung cancer. With a rise in new cases worldwide each year, early diagnosis and treatment are very important. Network pharmacology provides the effective way to evaluate poly-pharmacological effects and anticancer molecular mechanisms of drugs. The aim of the present study was to explore the anti-tumor mechanism of ethyl acetate extract of Wenxia Changfu Formula (WFEA) in lung adenocarcinoma by using analytical chemistry, network pharmacology and molecular biology. A total of 193 compounds were identified from WFEA, mainly including esters, phenols, ketones and alkaloids. Totally, 374 targets were regarded as potential targets of WFEA against lung adenocarcinoma. Interestingly, PI3K-AKT was found to be one of the significantly enriched signaling pathways of targets of WFEA against lung adenocarcinoma. AKT1, MMP3, CASP3 and BCL2 had strong binding effect with compound molecules of WFEA. Some combinations with the best docking binding were identified, including quercetin/oleanolic_acid/emodin/aloe_emodin/catechin-AKT1 and quercetin-MMP3. In lung adenocarcinoma cells, the WFEA inhibited the proliferation, migration and invasion, and promoted the apoptosis. Moreover, the WFEA inhibited the mRNA expression of MMP3 and Bcl-2 and promoted the mRNA expression of Caspase3. In addition, WFEA inhibited the protein phosphorylation of AKT and PI3K. The WFEA had a significant inhibitory effect on lung adenocarcinoma cells, which could inhibit cell proliferation, invasion and metastasis, and induce cell apoptosis. The mechanism of action of WFEA may be involved in the regulation of the PI3K-AKT signaling pathway in the lung adenocarcinoma.

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images.

Background: Functional adrenal tumors (FATs) are mainly diagnosed by biochemical analysis. Traditional imaging tests have limitations and cannot be used alone to diagnose FATs. In this study, we aimed to establish an artificially intelligent diagnostic model based on computed tomography (CT) images to distinguish different types of FATs. Methods: A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by transferring data from the training set into the deep learning network. The testing set was then used to evaluate the accuracy of the model compared to that of physicians' judgments. The retrospective trial set was used to evaluate the quantification and distinction performance. Results: The deep learning model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.915, and the AUCs in all three FAT types were greater than 0.882. The AUC of the model tested on the retrospective dataset reached above 0.849. In the quantitative evaluation of tumor lesion area recognition, the diagnostic model also obtained a segmentation Dice coefficient of 0.69. With the help of the proposed model, clinicians reached 92.5% accuracy in distinguishing FATs, compared to 80.6% accuracy when using only their judgment (P<0.05). Conclusions: The result of our study shows that the diagnostic model based on a deep learning network can distinguish and quantify three common FAT types based on texture features of contrast-enhanced CT images. The model can quantify and distinguish functional tumors without any endocrine tests and can assist clinicians in the diagnostic procedure.

Effect of Quercetin in the 1-Methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine-Induced Mouse Model of Parkinson's Disease.

In this paper, the protective effect of the bioflavonoid quercetin on behaviors, antioxidases, and neurotransmitters in 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine-(MPTP-) induced Parkinson's disease (PD) was investigated. Quercetin treatment (50 mg/kg, 100 mg/kg and 200 mg/kg body weight) was orally administered for 14 consecutive days. The results show that quercetin treatment markedly improves the motor balance and coordination of MPTP-treated mice. Significant increases were observed in the activities of glutathione peroxidase (GPx), superoxide dismutase (SOD), and Na(+), K(+)-ATPase, AchE, the content of dopamine (DA) in the quercetin plus MPTP groups compared to those in the MPTP group. Significant reduction the 4-hydroxy-2-nonenal (4-HNE) immunoreactivity in striatum of brains was observed in the quercetin plus MPTP groups in comparison to the MPTP group. Taken together, we propose that quercetin has shown antiparkinsonian properties in our studies. More work is needed to explore detailed mechanisms of action.

Suppressive effect of berberine on experimental dextran sulfate sodium-induced colitis.

The anti-inflammatory effect of berberine was evaluated in murine model of acute experimental colitis induced by dextran sulfate sodium (DSS). Berberine, given orally at 40, 20, 10 mg/kg for 10 days, ameliorated all the supposed inflammatory symptoms of the induced colitis, such as body weightloss, blood hemoglobin reduction, high myeloperoxidase levels, and malondialdehyde content-inflamed mucosa. Furthermore, the cytokine production of splenic lymphocytes was analyzed. The results showed the IFN-γ and IL-12 were increased, but IL-4 and IL-10 were decreased in DSS-induced colitis,when those were compared with the normal control. But the administration of berberine to DSS-induced colitis mice showed lower production of IFN-γ and IL-12 and higher production of IL-4 and IL-10 than the DSS-induced colitis mice. The results suggest that the protective effects of berberine against the DSS-induced colitis may be associated with the regulation of cytokine production.

Deep-Learning-Based Hemoglobin Concentration Prediction and Anemia Screening Using Ultra-Wide Field Fundus Images.

Background: Anemia is the most common hematological disorder. The purpose of this study was to establish and validate a deep-learning model to predict Hgb concentrations and screen anemia using ultra-wide-field (UWF) fundus images. Methods: The study was conducted at Peking Union Medical College Hospital. Optos color images taken between January 2017 and June 2021 were screened for building the dataset. ASModel_UWF using UWF images was developed. Mean absolute error (MAE) and area under the receiver operating characteristics curve (AUC) were used to evaluate its performance. Saliency maps were generated to make the visual explanation of the model. Results: ASModel_UWF acquired the MAE of the prediction task of 0.83 g/dl (95%CI: 0.81-0.85 g/dl) and the AUC of the screening task of 0.93 (95%CI: 0.92-0.95). Compared with other screening approaches, it achieved the best performance of AUC and sensitivity when the test dataset size was larger than 1000. The model tended to focus on the area around the optic disc, retinal vessels, and some regions located at the peripheral area of the retina, which were undetected by non-UWF imaging. Conclusion: The deep-learning model ASModel_UWF could both predict Hgb concentration and screen anemia in a non-invasive and accurate way with high efficiency.

Evaluation of an OCT-AI-Based Telemedicine Platform for Retinal Disease Screening and Referral in a Primary Care Setting.

PURPOSE: To evaluate the performance of a telemedicine platform integrated with optical coherence tomography (OCT) and artificial intelligence (AI) techniques for retinal disease screening and referral. METHODS: We constructed an OCT-AI-based telemedicine platform and deployed it at four primary care stations located in Jing'an district, Shanghai, to detect retinal disease cases among aged groups and refer them to Shanghai Tenth People's Hospital (TENTH Hospital). Two ophthalmologists jointly graded the data set collected from this pilot application, and then the performance of this platform was analyzed from multiple aspects. RESULTS: This study included 1257 participants between July 2020 and September 2020, of whom 394 had retinal pathologies and 146 were even considered urgent cases by the ophthalmologists. The OCT-AI models achieved a sensitivity of 96.6% (95% confidence interval [CI], 91.8%-98.7%) and specificity of 98.8% (95% CI, 98.0%-99.3%) for detecting urgent cases and a sensitivity of 98.5% (95% CI, 96.5%-99.4%) and specificity of 96.2% (95% CI, 94.6%-97.3%) for detecting both urgent and routine cases. Coupled with AI, our platform reduced the workload of human consultation by 96.2% for massive normal cases. The detected disease cases received online medical suggestions at an average time of 21.4 hours via this platform. CONCLUSIONS: This platform can automatically identify patients with retinal disease with high sensitivity and specificity, support timely human consultation, and bring necessary referrals. TRANSLATIONAL RELEVANCE: The OCT-AI-based telemedicine platform shows great practical value for retinal disease screening and referral in a real-world primary care setting.

Scrutinizing high-risk patients from ASC-US cytology via a deep learning model.

BACKGROUND: Atypical squamous cells of undetermined significance (ASC-US) is the most frequent but ambiguous abnormal Papanicolaou (Pap) interpretation and is generally triaged by high-risk human papillomavirus (hrHPV) testing before colposcopy. This study aimed to evaluate the performance of an artificial intelligence (AI)-based triage system to predict ASC-US cytology for cervical intraepithelial neoplasia 2+ lesions (CIN2+). METHODS: More than 60,000 images were used to train this proposed deep learning-based ASC-US triage system, where both cell-level and slide-level information were extracted. In total, 1967 consecutive ASC-US Paps from 2017 to 2019 were included in this study. Histological follow-ups were retrieved to compare the triage performance between the AI system and hrHPV in 622 patients with simultaneous hrHPV testing. RESULTS: In the triage of women with ASC-US cytology for CIN2+, our system attained equivalent sensitivity (92.9%; 95% confidence interval [CI], 75.0%-98.8%) and higher specificity (49.7%; 95% CI, 45.6%-53.8%) than hrHPV testing (sensitivity: 89.3%; 95% CI, 70.6%-97.2%; specificity: 34.3%; 95% CI, 30.6%-38.3%) without requiring additional patient examination or testing. Additionally, the independence of this system from hrHPV testing (κ = 0.138) indicated that these 2 different methods could be used to triage ASC-US as an alternative way. CONCLUSION: This de novo deep learning-based system can triage ASC-US cytology for CIN2+ with a performance superior to hrHPV testing and without incurring additional expenses.

Automated detection of myopic maculopathy from color fundus photographs using deep convolutional neural networks.

BACKGROUND: Myopic maculopathy (MM) has become a major cause of visual impairment and blindness worldwide, especially in East Asian countries. Deep learning approaches such as deep convolutional neural networks (DCNN) have been successfully applied to identify some common retinal diseases and show great potential for the intelligent analysis of MM. This study aimed to build a reliable approach for automated detection of MM from retinal fundus images using DCNN models. METHODS: A dual-stream DCNN (DCNN-DS) model that perceives features from both original images and corresponding processed images by color histogram distribution optimization method was designed for classification of no MM, tessellated fundus (TF), and pathologic myopia (PM). A total of 36,515 gradable images from four hospitals were used for DCNN model development, and 14,986 gradable images from the other two hospitals for external testing. We also compared the performance of the DCNN-DS model and four ophthalmologists on 3000 randomly sampled fundus images. RESULTS: The DCNN-DS model achieved sensitivities of 93.3% and 91.0%, specificities of 99.6% and 98.7%, areas under the receiver operating characteristic curves (AUC) of 0.998 and 0.994 for detecting PM, whereas sensitivities of 98.8% and 92.8%, specificities of 95.6% and 94.1%, AUCs of 0.986 and 0.970 for detecting TF in two external testing datasets. In the sampled testing dataset, the sensitivities of four ophthalmologists ranged from 88.3% to 95.8% and 81.1% to 89.1%, and the specificities ranged from 95.9% to 99.2% and 77.8% to 97.3% for detecting PM and TF, respectively. Meanwhile, the DCNN-DS model achieved sensitivities of 90.8% and 97.9% and specificities of 99.1% and 94.0% for detecting PM and TF, respectively. CONCLUSIONS: The proposed DCNN-DS approach demonstrated reliable performance with high sensitivity, specificity, and AUC to classify different MM levels on fundus photographs sourced from clinics. It can help identify MM automatically among the large myopic groups and show great potential for real-life applications.

Two-step artificial intelligence system for endoscopic gastric biopsy improves the diagnostic accuracy of pathologists.

Background: Endoscopic biopsy is the pivotal procedure for the diagnosis of gastric cancer. In this study, we applied whole-slide images (WSIs) of endoscopic gastric biopsy specimens to develop an endoscopic gastric biopsy assistant system (EGBAS). Methods: The EGBAS was trained using 2373 WSIs expertly annotated and internally validated on 245 WSIs. A large-scale, multicenter test dataset of 2003 WSIs was used to externally evaluate EGBAS. Eight pathologists were compared with the EGBAS using a man-machine comparison test dataset. The fully manual performance of the pathologists was also compared with semi-manual performance using EGBAS assistance. Results: The average area under the curve of the EGBAS was 0·979 (0·958-0·990). For the diagnosis of all four categories, the overall accuracy of EGBAS was 86·95%, which was significantly higher than pathologists (P< 0·05). The EGBAS achieved a higher κ score (0·880, very good κ) than junior and senior pathologists (0·641 ± 0·088 and 0·729 ± 0·056). With EGBAS assistance, the overall accuracy (four-tier classification) of the pathologists increased from 66·49 ± 7·73% to 73·83 ± 5·73% (P< 0·05). The length of time for pathologists to manually complete the dataset was 461·44 ± 117·96 minutes; this time was reduced to 305·71 ± 82·43 minutes with EGBAS assistance (P = 0·00). Conclusions: The EGBAS is a promising system for improving the diagnosis ability and reducing the workload of pathologists.

Suppression of autophagy through JAK2/STAT3 contributes to the therapeutic action of rhynchophylline on asthma.

BACKGROUND: Asthma is a chronic inflammatory disease characterized by airway remodeling and inflammation. Rhynchophylline is a kind of indole alkaloid isolated from Uncaria rhynchophylla. Here we investigated the effect of rhynchophylline on autophagy in asthma. METHODS: A mice model of asthma was established by ovalbumin challenge. Histopathological changes were assessed by hematoxylin-eosin staining, periodic acid-schiff staining and Masson staining. The levels of IgE in serum, interleukin-6 and interleukin-13 in bronchoalveolar lavage fluid, as well as the activities of superoxide dismutase and catalase in lung tissues were detected. The expression of autophagy-related genes and Janus kinase (JAK) 2/ signal transducer and activator of transcription (STAT) 3 signal was detected by western blot and immunofluorescence. Airway smooth muscle cells (ASMCs) were isolated, and the effect rhynchophylline on autophagy in ASMCs was explored. RESULTS: Our data showed that rhynchophylline treatment alleviated inflammation, airway remodeling, and oxidative stress in asthma. In addition, autophagy, which was implicated in asthma, was suppressed by rhynchophylline with decreased level of autophagy-related proteins. Furthermore, rhynchophylline suppressed the JAK2/STAT3 signaling pathway, which was activated in asthma. In vitro study showed that rhynchophylline suppressed ASMC autophagy through suppressing the activation of JAK2/STAT3 signal. CONCLUSIONS: Our study demonstrated that rhynchophylline can alleviate asthma through suppressing autophagy in asthma, and that JAK2/STAT3 signal was involved in this effect of rhynchophylline. This study indicates that rhynchophylline may become a promising drug for the treatment of asthma.

Solid lipid nanoparticle delivery of rhynchophylline enhanced the efficiency of allergic asthma treatment via the upregulation of suppressor of cytokine signaling 1 by repressing the p38 signaling pathway.

Allergic asthma is one of the most common chronic airway diseases, and there is still a lack of effective drugs for the treatment of allergic asthma. The purpose of this work is to formulate rhynchophylline (Rhy)-solid lipid nanoparticles (SLNs) to improve their therapeutic efficacy in a mice allergic model of asthma. A solvent injection method was employed to prepare the Rhy-SLNs. Physicochemical characterization of Rhy-SLNs was measured, and the release assessment was investigated, followed by the release kinetics. Next, a model of murine experimental asthma was established. Mice were subcutaneously injected with 20 µg ovalbumin mixed with 1 mg aluminum hydroxide on days 0, 14, 28, and 42 and administrated aerosolized 1% ovalbumin (w/v) by inhalation from day 21 to day 42. Mice were intraperitoneally injected with 20 mg/kg Rhy-SLNs or Rhy at one hour before the airway challenge with ovalbumin. The results showed that Rhy-SLNs revealed a mean particle size of 62.06 ± 1.62 nm with a zeta potential value of -6.53 ± 0.04 mV and 82.6 ± 1.8% drug entrapment efficiency. The release curve of Rhy-SLNs was much higher than the drug released in phosphate buffer saline at 0, 1, 1.5, 2, 4, or 6 h. Moreover, Rhy-SLNs exerted better effects on inhibiting ovalbumin-induced airway inflammation, oxidative stress, airway remodeling (including collagen deposition and mucus gland hyperplasia) than Rhy in murine experimental asthma. Subsequently, we found that Rhy-SLNs relieved allergic asthma via the upregulation of the suppressor of cytokine signaling 1 by repressing the p38 signaling pathway.

Dissecting the Profile of Corneal Thickness With Keratoconus Progression Based on Anterior Segment Optical Coherence Tomography.

PURPOSE: To characterize the corneal and epithelial thickness at different stages of keratoconus (KC), using a deep learning based corneal segmentation algorithm for anterior segment optical coherence tomography (AS-OCT). METHODS: An AS-OCT dataset was constructed in this study with 1,430 images from 715 eyes, which included 118 normal eyes, 134 mild KC, 239 moderate KC, 153 severe KC, and 71 scarring KC. A deep learning based corneal segmentation algorithm was applied to isolate the epithelial and corneal tissues from the background. Based on the segmentation results, the thickness of epithelial and corneal tissues was automatically measured in the center 6 mm area. One-way ANOVA and linear regression were performed in 20 equally divided zones to explore the trend of the thickness changes at different locations with the KC progression. The 95% confidence intervals (CI) of epithelial thickness and corneal thickness in a specific zone were calculated to reveal the difference of thickness distribution among different groups. RESULTS: Our data showed that the deep learning based corneal segmentation algorithm can achieve accurate tissue segmentation and the error range of measured thickness was less than 4 µm between our method and the results from clinical experts, which is approximately one image pixel. Statistical analyses revealed significant corneal thickness differences in all the divided zones (P < 0.05). The entire corneal thickness grew gradually thinner with the progression of the KC, and their trends were more pronounced around the pupil center with a slight shift toward the temporal and inferior side. Especially the epithelial thicknesses were thinner gradually from a normal eye to severe KC. Due to the formation of the corneal scarring, epithelial thickness had irregular fluctuations in the scarring KC. CONCLUSION: Our study demonstrates that our deep learning method based on AS-OCT images could accurately delineate the corneal tissues and further successfully characterize the epithelial and corneal thickness changes at different stages of the KC progression.

Lesion synthesis to improve intracranial hemorrhage detection and classification for CT images.

Computer-aided diagnosis (CAD) for intracranial hemorrhage (ICH) is needed due to its high mortality rate and time sensitivity. Training a stable and robust deep learning-based model usually requires enough training examples, which may be impractical in many real-world scenarios. Lesion synthesis offers a possible solution to solve this problem, especially for the issue of the lack of micro bleedings. In this paper, we propose a novel strategy to generate artificial lesions on non-lesion CT images so as to produce additional labeled training examples. Artificial masks in any location, size, or shape can be generated through Artificial Mask Generator (AMG) and then be converted into hemorrhage lesions through Lesion Synthesis Network (LSN). Images with and without artificial lesions are combined for training an ICH detection with a novel Residual Score. We evaluate our method by the auxiliary diagnosis task of ICH. Our experiments demonstrate that the proposed approach can improve the AUC value from 84% to 91% in the ICH detection task and from 89% to 96% in the classification task. Moreover, by adding artificial lesions of small size, the sensitivity of micro bleeding is remarkably improved from 49% to 70%. Besides, the proposed method overcomes the other three synthetic approaches by a large margin.

Relationships Between Retinal Vascular Characteristics and Renal Function in Patients With Type 2 Diabetes Mellitus.

Purpose: To develop a deep learning-based method to achieve vessel segmentation and measurement on fundus images, and explore the quantitative relationships between retinal vascular characteristics and the clinical indicators of renal function. Methods: We recruited patients with type 2 diabetes mellitus with different stages of diabetic retinopathy (DR), collecting their fundus photographs and results of renal function tests. A deep learning framework for retinal vessel segmentation and measurement was developed. The correlation between the renal function indicators and the severity of DR were explored, then the correlation coefficients between indicators of renal function and retinal vascular characteristics were analyzed. Results: We included 418 patients (eyes) with type 2 diabetes mellitus. The albumin to creatinine ratio, blood uric acid, blood creatinine, blood albumin, and estimated glomerular filtration rate were significantly correlated with the progression of DR (P < 0.05); no correlation existed in other metrics (P > 0.05). The fractal dimension was found to significantly correlate with most of the clinical parameters of renal function (P < 0.05). Conclusions: The albumin to creatinine ratio, blood uric acid, blood creatinine, blood albumin, and estimated glomerular filtration rate have significant correlation with the progression of moderate to proliferative DR. Through deep learning-based vessel segmentation and measurement, the fractal dimension was found to significantly correlate with most clinical parameters of renal function. Translational Relevance: Deep learning-based vessel segmentation and measurement on color fundus photographs could explore the relationships between retinal characteristics and renal function, facilitating earlier detection and intervention of type 2 diabetes mellitus complications.

Correlation-Guided Network for Fine-Grained Classification of Glomerular lesions in Kidney Histopathology Images.

Chronic Kidney Disease has become a worldwide public health problem which demands careful assessments by pathologists. In this paper, we propose a novel architecture for fine-grained classification of glomerular lesions in renal pathology images sampling from patients with IgA nephropathy. The adversarial correlation loss is innovatively presented to guide a parallel convolutional neural network. In this well- designed loss function, bias between the prediction and the label was take into account while the relationship among different categories is well-aligned. Glomerular lesions in this study are divided into five subcategories, Neg (Negative samples such as tubule and artery), SS (sclerosis involving a portion of the glomerular tuft), GS (sclerosis involving 100% of the tuft), C (build-up of more than two layers of cells within Bowman's space, often with fibrin and collagen deposition) and NOA (none of above). Our model with 93.0% accuracy and 92.9% Fl-score for these five categories has proved superior to other models through experimental results.