Non-invasive medical imaging technology for the diagnosis of burn depth.

Currently, the clinical diagnosis of burn depth primarily relies on physicians' judgements based on patients' symptoms and physical signs, particularly the morphological characteristics of the wound. This method highly depends on individual doctors' clinical experience, proving challenging for less experienced or primary care physicians, with results often varying from one practitioner to another. Therefore, scholars have been exploring an objective and quantitative auxiliary examination technique to enhance the accuracy and consistency of burn depth diagnosis. Non-invasive medical imaging technology, with its significant advantages in examining tissue surface morphology, blood flow in deep and changes in structure and composition, has become a hot topic in burn diagnostic technology research in recent years. This paper reviews various non-invasive medical imaging technologies that have shown potential in burn depth diagnosis. These technologies are summarized and synthesized in terms of imaging principles, current research status, advantages and limitations, aiming to provide a reference for clinical application or research for burn specialists.

Comprehensive retinal vascular measurements: time in range is associated with peripheral retinal venular calibers in type 2 diabetes in China.

AIM: Retinal vascular parameters are biomarkers of diabetic microangiopathy. We aimed to investigate the relationship between time in range (TIR) assessed by continuous glucose monitoring (CGM) and retinal vascular parameters in patients with type 2 diabetes in China. METHODS: The TIR assessed by CGM and retinal photographs were obtained at the same time from adults with type 2 diabetes who were recruited. Retinal vascular parameters were extracted from retinal photographs by a validated fully automated computer program, and TIR was defined as between 3.9-7.8 mmol/L over a 24-h period. The association between TIR and caliber of retinal vessels distributed in different zones were analyzed using multivariable linear regression analyses. RESULTS: For retinal vascular parameters measurements, the peripheral arteriovenous and middle venular calibers widen with decreasing TIR quartiles (P < 0.05). Lower TIR was associated with wider peripheral venule after adjusting for potential confounders. Even after further correction for GV, there was still a significant correlation between TIR and peripheral vascular calibers (CV: ß = - 0.015 [- 0.027, - 0.003], P = 0.013; MAGE: ß = - 0.013 [- 0.025, - 0.001], P = 0.038) and SD: ß = - 0.013 [- 0.026, - 0.001], P = 0.004). Similar findings were not found for the middle and central venular calibers or arterial calibers located in different zones. CONCLUSIONS: The TIR was associated with adverse changes to peripheral retinal venules but not central and middle vessels in patients with type 2 diabetes, suggesting that peripheral retinal vascular calibers may be affected by glycemic fluctuations earlier.

Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images.

Background: Ultra-Wide-Field (UWF) fundus imaging is an essential diagnostic tool for identifying ophthalmologic diseases, as it captures detailed retinal structures within a wider field of view (FOV). However, the presence of eyelashes along the edge of the eyelids can cast shadows and obscure the view of fundus imaging, which hinders reliable interpretation and subsequent screening of fundus diseases. Despite its limitations, there are currently no effective methods or datasets available for removing eyelash artifacts from UWF fundus images. This research aims to develop an effective approach for eyelash artifact removal and thus improve the visual quality of UWF fundus images for accurate analysis and diagnosis. Methods: To address this issue, we first constructed two UWF fundus datasets: the paired synthetic eyelashes (PSE) dataset and the unpaired real eyelashes (uPRE) dataset. Then we proposed a deep learning architecture called Joint Conditional Generative Adversarial Networks (JcGAN) to remove eyelash artifacts from UWF fundus images. JcGAN employs a shared generator with two discriminators for joint learning of both real and synthetic eyelash artifacts. Furthermore, we designed a background refinement module that refines background information and is trained with the generator in an end-to-end manner. Results: Experimental results on both PSE and uPRE datasets demonstrate the superiority of the proposed JcGAN over several state-of-the-art deep learning approaches. Compared with the best existing method, JcGAN improves PSNR and SSIM by 4.82% and 0.23%, respectively. In addition, we also verified that eyelash artifact removal via JcGAN could significantly improve vessel segmentation performance in UWF fundus images. Assessment via vessel segmentation illustrates that the sensitivity, Dice coefficient and area under curve (AUC) of ResU-Net have respectively increased by 3.64%, 1.54%, and 1.43% after eyelash artifact removal using JcGAN. Conclusion: The proposed JcGAN effectively removes eyelash artifacts in UWF images, resulting in improved visibility of retinal vessels. Our method can facilitate better processing and analysis of retinal vessels and has the potential to improve diagnostic outcomes.

Improved fully convolutional neuron networks on small retinal vessel segmentation using local phase as attention.

Retinal images have been proven significant in diagnosing multiple diseases such as diabetes, glaucoma, and hypertension. Retinal vessel segmentation is crucial for the quantitative analysis of retinal images. However, current methods mainly concentrate on the segmentation performance of overall retinal vessel structures. The small vessels do not receive enough attention due to their small percentage in the full retinal images. Small retinal vessels are much more sensitive to the blood circulation system and have great significance in the early diagnosis and warning of various diseases. This paper combined two unsupervised methods, local phase congruency (LPC) and orientation scores (OS), with a deep learning network based on the U-Net as attention. And we proposed the U-Net using local phase congruency and orientation scores (UN-LPCOS), which showed a remarkable ability to identify and segment small retinal vessels. A new metric called sensitivity on a small ship (Sesv ) was also proposed to evaluate the methods' performance on the small vessel segmentation. Our strategy was validated on both the DRIVE dataset and the data from Maastricht Study and achieved outstanding segmentation performance on both the overall vessel structure and small vessels.

AV-casNet: Fully Automatic Arteriole-Venule Segmentation and Differentiation in OCT Angiography.

Automatic segmentation and differentiation of retinal arteriole and venule (AV), defined as small blood vessels directly before and after the capillary plexus, are of great importance for the diagnosis of various eye diseases and systemic diseases, such as diabetic retinopathy, hypertension, and cardiovascular diseases. Optical coherence tomography angiography (OCTA) is a recent imaging modality that provides capillary-level blood flow information. However, OCTA does not have the colorimetric and geometric differences between AV as the fundus photography does. Various methods have been proposed to differentiate AV in OCTA, which typically needs the guidance of other imaging modalities. In this study, we propose a cascaded neural network to automatically segment and differentiate AV solely based on OCTA. A convolutional neural network (CNN) module is first applied to generate an initial segmentation, followed by a graph neural network (GNN) to improve the connectivity of the initial segmentation. Various CNN and GNN architectures are employed and compared. The proposed method is evaluated on multi-center clinical datasets, including 3 ×3 mm2 and 6 ×6 mm2 OCTA. The proposed method holds the potential to enrich OCTA image information for the diagnosis of various diseases.

CellVisioner: A Generalizable Cell Virtual Staining Toolbox based on Few-Shot Transfer Learning for Mechanobiological Analysis.

Visualizing cellular structures especially the cytoskeleton and the nucleus is crucial for understanding mechanobiology, but traditional fluorescence staining has inherent limitations such as phototoxicity and photobleaching. Virtual staining techniques provide an alternative approach to addressing these issues but often require substantial amount of user training data. In this study, we develop a generalizable cell virtual staining toolbox (termed CellVisioner) based on few-shot transfer learning that requires substantially reduced user training data. CellVisioner can virtually stain F-actin and nuclei for various types of cells and extract single-cell parameters relevant to mechanobiology research. Taking the label-free single-cell images as input, CellVisioner can predict cell mechanobiological status (e.g., Yes-associated protein nuclear/cytoplasmic ratio) and perform long-term monitoring for living cells. We envision that CellVisioner would be a powerful tool to facilitate on-site mechanobiological research.

Similar color analysis based on deep learning (SCAD) for multiplex digital PCR via a single fluorescent channel.

Digital PCR (dPCR) has recently attracted great interest due to its high sensitivity and accuracy. However, the existing dPCR depends on multicolor fluorescent dyes and multiple fluorescent channels to achieve multiplex detection, resulting in increased detection cost and limited detection throughput. Here, we developed a deep learning-based similar color analysis method, namely SCAD, to achieve multiplex dPCR in a single fluorescent channel. As a demonstration, we designed a microwell chip-based diplex dPCR system for detecting two genes (blaNDM and blaVIM) with two kinds of green fluorescent probes, whose emission colors are difficult to discriminate by traditional fluorescence intensity-based methods. To verify the possibility of deep learning algorithms to distinguish the similar colors, we first applied t-distributed stochastic neighbor embedding (tSNE) to make a clustering map for the microwells with similar fluorescence. Then, we trained a Vision Transformer (ViT) model on 10 000 microwells with two similar colors and tested it with 262 202 microwells. Lastly, the trained model was proven to have highly accurate classification ability (>98% for both the training set and the test set) and precise quantification ability on both blaNDM and blaVIM (ratio difference <0.10). We envision that the developed SCAD method would significantly expand the detection throughput of dPCR without the need for other auxiliary equipment.

Anomaly segmentation in retinal images with poisson-blending data augmentation.

Diabetic retinopathy (DR) is one of the most important complications of diabetes. Accurate segmentation of DR lesions is of great importance for the early diagnosis of DR. However, simultaneous segmentation of multi-type DR lesions is technically challenging because of 1) the lack of pixel-level annotations and 2) the large diversity between different types of DR lesions. In this study, first, we propose a novel Poisson-blending data augmentation (PBDA) algorithm to generate synthetic images, which can be easily utilized to expand the existing training data for lesion segmentation. We perform extensive experiments to recognize the important attributes in the PBDA algorithm. We show that position constraints are of great importance and that the synthesis density of one type of lesion has a joint influence on the segmentation of other types of lesions. Second, we propose a convolutional neural network architecture, named DSR-U-Net++ (i.e., DC-SC residual U-Net++), for the simultaneous segmentation of multi-type DR lesions. Ablation studies showed that the mean area under precision recall curve (AUPR) for all four types of lesions increased by >5% with PBDA. The proposed DSR-U-Net++ with PBDA outperformed the state-of-the-art methods by 1.7%-9.9% on the Indian Diabetic Retinopathy Image Dataset (IDRiD) and 67.3% on the e-ophtha dataset with respect to mean AUPR. The developed method would be an efficient tool to generate large-scale task-specific training data for other medical anomaly segmentation tasks.

Multi-Scale Pathological Fluid Segmentation in OCT With a Novel Curvature Loss in Convolutional Neural Network.

The segmentation of pathological fluid lesions in optical coherence tomography (OCT), including intraretinal fluid, subretinal fluid, and pigment epithelial detachment, is of great importance for the diagnosis and treatment of various eye diseases such as neovascular age-related macular degeneration and diabetic macular edema. Although significant progress has been achieved with the rapid development of fully convolutional neural networks (FCN) in recent years, some important issues remain unsolved. First, pathological fluid lesions in OCT show large variations in location, size, and shape, imposing challenges on the design of FCN architecture. Second, fluid lesions should be continuous regions without holes inside. But the current architectures lack the capability to preserve the shape prior information. In this study, we introduce an FCN architecture for the simultaneous segmentation of three types of pathological fluid lesions in OCT. First, attention gate and spatial pyramid pooling modules are employed to improve the ability of the network to extract multi-scale objects. Then, we introduce a novel curvature regularization term in the loss function to incorporate shape prior information. The proposed method was extensively evaluated on public and clinical datasets with significantly improved performance compared with the state-of-the-art methods.

Deep level set method for optic disc and cup segmentation on fundus images.

Glaucoma is a leading cause of blindness. The measurement of vertical cup-to-disc ratio combined with other clinical features is one of the methods used to screen glaucoma. In this paper, we propose a deep level set method to implement the segmentation of optic cup (OC) and optic disc (OD). We present a multi-scale convolutional neural network as the prediction network to generate level set initial contour and evolution parameters. The initial contour will be further refined based on the evolution parameters. The network is integrated with augmented prior knowledge and supervised by active contour loss, which makes the level set evolution yield more accurate shape and boundary details. The experimental results on the REFUGE dataset show that the IoU of the OC and OD are 93.61% and 96.69%, respectively. To evaluate the robustness of the proposed method, we further test the model on the Drishthi-GS1 dataset. The segmentation results show that the proposed method outperforms the state-of-the-art methods.

Correlation between retinal vascular parameters and cystatin C in patients with type 2 diabetes.

AIMS: To investigate the relationship between retinal vascular parameters and cystatin C in patients with type 2 diabetes in northwestern China. METHODS: This was a cross-sectional study of 1689 patients with type 2 diabetes. A validated fully automated computer program was used to extract retinal vascular parameters from the entire vascular tree. Multiple logistic regression analyses were conducted to investigate the relationship between these vascular measurements and cystatin C. RESULTS: For retinal vascular geometrical measurements, smaller arteriolar fractal dimension was related to high cystatin C after adjusting for multiple variables (odds ratio [OR] 0.149, 95% CI 0.042-0.532). For retinal vascular caliber measurements, narrower central and middle arteriolar calibers were related to high cystatin C after adjusting for multiple variables (central: OR 0.922, 95% CI 0.886-0.960; middle: OR 0.940, 95% CI 0.901-0.981). Wider central, middle and peripheral venular calibers were associated with high cystatin C after adjusting for multiple variables (central: OR 1.058, 95% CI 1.003-1.117; middle: OR 1.094, 95% CI 1.040-1.150; peripheral: OR 1.075, 95% CI 1.023-1.130). CONCLUSIONS: Multiple retinal vascular geometrical and caliber measurements are associated with cystatin C in type 2 diabetic patients. Further studies are needed to explore whether these retinal vascular changes can predict the incidence and progress of diabetic nephropathy.

Dilated retinal large vessels and capillaries associated with diabetic macular edema and photoreceptor loss respectively.

PURPOSE: Previously, we measured retinal large vessels and capillaries separately on optical coherence tomography angiography (OCTA). In the present study, we aim to evaluate the role of these parameters in association to diabetic macular edema (DME) and ellipsoid zone disruption (EZD). METHODS: In this cross-sectional study, 54 eyes from 31 patients (10 females, 31 Asians) with severe non-proliferative diabetic retinopathy (25 eyes) or proliferative diabetic retinopathy (PDR, 29 eyes) were enrolled. All eyes underwent 3 × 3 mm OCTA scans centered on the fovea. Perfusion density (PD), vessel length density (VLD), and vessel diameter index (VDI) were calculated for retinal large vessels and superficial capillaries separately. Other OCTA findings included suspended scattering particles in motion (SSPiM), number of microaneurysms (MA) in all retinal layers, and the area of foveal avascular zone (FAZ) of superficial capillary plexus. DME and EZD were evaluated on B-scans. Both univariate and multivariate analysis were performed. RESULTS: Of the 54 study eyes, 31 (57%) had DME and 21 (40%) had EZD. Multivariate regression model showed that PDR (ß = 27.8, 95% confidence interval (CI): 2.7-282.8, p = 0.005), more MA (ß = 2.5, 95% CI: 1.3-4.5, p = 0.003), and increased VDI of larger vessels (ß = 1.9, 95% CI: 1.0-3.5, p = 0.047) were risk factors for DME. As for EZD, presence of SSPiM (ß = 5.5, 95% CI: 1.2-26.1, p = 0.032) and increased VDI of capillaries (ß = 3.9, 95% CI: 1.1-13.8, p = 0.034) were risk factors. CONCLUSIONS: In eyes with diabetic retinopathy, dilation of retinal larger vessels was associated with macular edema, while dilation of retinal capillaries was associated with ellipsoid zone disruption.

Retinal image measurements and their association with chronic kidney disease in Chinese patients with type 2 diabetes: the NCD study.

AIMS: Retinal and renal microcirculations are known to share similar physiological changes during early diabetes because of abnormal glucose metabolism and other processes. The retinal vasculature therefore may serve as potential biomarker for the early identification of those at high risk of chronic kidney disease (CKD) in diabetes. METHODS: Data from 1925 patients (aged 49.0 ± 10.3) with type 2 diabetes were analyzed. Various retinal image measurements (RIMs) were collected using a validated fully automated computer program. Multiple logistic regressions were performed to investigate the correlation between RIMs and CKD. RESULTS: In logistic regression adjusting for multiple variables, wider venular calibers in the central and middle zones and narrower arteriolar caliber in the central zone were associated with CKD (p < 0.001, p = 0.020, and p < 0.001, respectively). Increased arteriolar tortuosity was associated with CKD (p = 0.035). Multiple image texture measurements were also significantly associated with CKD. CONCLUSIONS: Renal dysfunction in type 2 diabetes was associated with various retinal image measurements. These non-invasive image measurements may serve as potential biomarkers for the early identification and monitoring of individuals at high risk of CKD in the course of diabetes.

Resveratrol Ameliorates Systemic Sclerosis via Suppression of Fibrosis and Inflammation Through Activation of SIRT1/mTOR Signaling.

PURPOSE: Resveratrol (Res) is a natural polyphenolic compound found in several plants and reported as a promising biological molecule with effective anti-fibrosis and anti-inflammatory activities. However, the underlying mechanism of Res on systemic sclerosis (SSc) remains unclear. In the study, we identified the key cellular signaling pathways involved in the Res regulatory process on SSc. METHODS: Res-targeted genes interaction network was constructed using the STITCH database, and the shared Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in both SSc and Res-targeted genes were then identified. The top five enriched KEGG pathways were visualized by GOplot. KEGG pathways associated with Res-targeted genes were established by Pathway Builder Tool 2.0. Quantitative real-time PCR (qRT-PCR) was used to assess the expression of sirtuin 1 (SIRT1), mammalian targeted of rapamycin (mTOR), and cytokines. RESULTS: Enrichment analysis of Res-targeted genes showed 79 associated pathways, 27 of which were also involved in SSc. Particularly, SIRT1/mTOR signaling was found as one of the crucial regulatory pathways. In vitro results suggested that SIRT1-mediated mTOR degradation ameliorated bleomycin (BLM)-induced fibrosis and inflammation. Res was capable of elevating the SIRT1 level in fibroblasts and partially reversing mTOR-dependent induction of fibrosis and inflammation. CONCLUSION: These results indicated that Res is a feasible and effective choice for SSc and therapeutic target of mTOR could be a potential alternative for treatment of SSc.

Comprehensive retinal vascular measurements: a novel association with renal function in type 2 diabetic patients in China.

To examine the association between various retinal vascular measurements and microalbuminuria in patients with type 2 diabetes in a northwestern China study. Data from 911 patients with type 2 diabetes were analyzed. Novel retinal vascular measurements from the whole vascular tree were extracted using a validated fully automatic computer program. Retinal vascular measurements were analyzed continuously and categorically for associations with microalbuminuria using multiple logistic regressions, adjusted for related variables. In logistic regression adjusting for multiple variables, microalbuminuria was associated with smaller peripheral arteriolar caliber, larger peripheral venular caliber, larger arteriolar tortuosity, and smaller arteriolar fractal dimension (p = 0.028, p < 0.001, p = 0.038, p = 0.035, respectively). In further categorical analyses, microalbuminuria was related to smaller peripheral arteriolar caliber [T1 vs. T3: odds ratio (OR) 2.029; 95% confidence interval (CI) 1.186-3.473], larger peripheral venular caliber (T1 vs. T3: OR 0.609; 95% CI 0.362-1.024), and smaller arteriolar fractal dimension (T1 vs. T3: OR 1.659; 95% CI 1.028-2.675). Microalbuminuria in type 2 diabetes is associated with both retinal vascular caliber and geometry. These noninvasive vascular measurements serve as potential preclinical markers to identify populations at high risk of early kidney disease in the course of diabetes.

Graph convolutional network based optic disc and cup segmentation on fundus images.

Calculating the cup-to-disc ratio is one of the methods for glaucoma screening with other clinical features. In this paper, we propose a graph convolutional network (GCN) based method to implement the optic disc (OD) and optic cup (OC) segmentation task. We first present a multi-scale convolutional neural network (CNN) as the feature map extractor to generate feature map. The GCN takes the feature map concatenated with the graph nodes as the input for segmentation task. The experimental results on the REFUGE dataset show that the Jaccard index (Jacc) of the proposed method on OD and OC are 95.64% and 91.60%, respectively, while the Dice similarity coefficients (DSC) are 97.76% and 95.58%, respectively. The proposed method outperforms the state-of-the-art methods on the REFUGE leaderboard. We also evaluate the proposed method on the Drishthi-GS1 dataset. The results show that the proposed method outperforms the state-of-the-art methods.

MiR-16-5p suppresses myofibroblast activation in systemic sclerosis by inhibiting NOTCH signaling.

Systemic sclerosis (SSc) is a prototypic fibrotic disease characterized by localized or diffuse skin thickening and fibrosis. Tissue fibrosis is driven by myofibroblasts, and factors affecting myofibroblast activation may also be involved in the development of SSc. In this study, we examined molecular mechanisms underlying SSc by focusing on myofibroblast activation processes. Bioinformatics analysis conducted to identify differentially expressed miRNAs (DEMs) and genes (DEGs) revealed that microRNA-16-5p (miR-16-5p) was downregulated and NOTCH2 was upregulated in SSc patients. In vitro experiments confirmed that miR-16-5p was able to bind directly to NOTCH2 and inhibit myofibroblast activation. Moreover, miR-16-5p-dependent inhibition of NOTCH2 decreased collagen and α-SMA expression. MiR-16-5p downregulation and NOTCH2 upregulation was also confirmed in vivo in SSc patients, and NOTCH2 activation promoted fibrosis progression in vitro. These results indicate that miR-16-5p suppresses myofibroblast activation by suppressing NOTCH signaling.

Automated quantification of superficial retinal capillaries and large vessels for diabetic retinopathy on optical coherence tomographic angiography.

Optical coherence tomography angiography (OCTA) is a relatively new technique with capillary-level resolution, which has shown great potential for the diagnosis of diabetic retinopathy (DR). A fully automatic algorithm for the quantitative measurement of microcirculatory changes in sight-threatening DR is presented. The foveal avascular zone (FAZ) segmentation was improved with a graph-theoretic method and the large vessels and capillaries were separately identified and analyzed. The method was evaluated in healthy and diabetic eyes with various stages of retinopathy. Results showed that, compared with the healthy group, the diabetic group showed a significantly larger large vessel density, but a significantly smaller capillary density (P < .001). Circularity of FAZ was significantly smaller while nonperfusion area was significantly larger in the diabetic group. The combined variable of all image metrics reached an area under the ROC of 0.853 (95% CI, 0.784-0.923) for mild to moderate nonproliferative DR and 0.950 (95% CI, 0.922-0.979) for proliferative DR. Microvascular and FAZ changes with various DR stages can be accurately delineated using the developed automatic program. Quantitative metrics on OCTA serve as potential biomarkers for the staging of DR.

Influence of Axial Length on Parafoveal and Peripapillary Metrics from Swept Source Optical Coherence Tomography Angiography.

Purpose: To assess the effect of axial length (AL) on the quantification of superficial vessel density of both macular and disc region using swept source optical coherence tomography angiography (SSOCTA). Methods: This is a cross-sectional clinical study. Seventy-five eyes from 75 Chinese healthy participants (56 females) with a mean age of 26.6 ± 6.8 (range 19-50) years were included in this study. All eyes were imaged with SSOCTA, using a 3 × 3mm scan pattern centered on the macular and optic disc, respectively, and the superficial layer was used for evaluation. The image size was corrected with AL using Bennett formula. Outcome measurements included perfusion density (PD), vessel length density (VLD) in parafoveal and disc regions, averaged peripapillary large vessel diameter and area of foveal avascular zone (FAZ). Image processing and measurements was performed using Image J software. Multivariate regression analysis adjusting for age and signal strength was used to assess the influence of AL on the metrics. Results: AL was the only predictive factor for parafoveal PD (ß = -0.273, P = .047) and VLD (ß = -0.396, P = .003). There was no correlation between AL and area of FAZ, large vessel diameter, or the vessel density on any location in disc region. Age was the only predictor for PD (ß = -0.287, P = .024) and VLD (ß = -0.289, P = .023) on optic nerve head. Conclusions: AL was negatively correlated with superficial parafoveal microvasculature, but not correlated with peripapillary capillaries, suggesting that the inner retina stretches more in the distal end of the disc with increased AL.