Long-Term Effectiveness of Cilostazol in Patients with Hemodialysis with Peripheral Artery Disease.

AIM: The aim of this study was to investigate the effects of continuous cilostazol use on emergency department (ED) visits, hospitalizations, and vascular outcomes in patients with hemodialysis (HD) with peripheral artery disease (PAD). METHODS: This retrospective cohort study recruited 558 adult patients, who had received chronic HD for at least 90 days between January 1, 2008 and December 31, 2012, from the National Health Insurance Research Database. Eligible patients were divided into two groups based on continuing or discontinuing cilostazol treatment. Outcome measures were ED visits, hospitalizations, mortality, and vascular outcomes such as percutaneous transluminal angioplasty, surgical bypass, lower leg amputation, ischemic stroke, hemorrhagic stroke, and cardiovascular events. RESULTS: Patients with continuous cilostazol use had significantly higher prevalence of stroke, cancer, vintage, and the use of angiotensin receptor blocker and ß-blocker, but significantly lower incidence of ischemic stroke and cardiovascular events, as well as lower mortality, than those without continuous cilostazol use (all p＜.05). Continuous cilostazol use was independently associated with lower risk of ED visits, hemorrhagic stroke, and cardiovascular events (adjusted hazard ratios: 0.79, 0.29, and 0.67; 95% confidence intervals: 0.62-0.98, 0.10-0.84, and 0.48-0.96, respectively; all p＜.05). Continuous cilostazol use was significantly associated with higher ED visit-free and cardiovascular event-free rates (log-rank test; p＜.05). CONCLUSION: Continuous treatment of cilostazol in patients with HD with PAD significantly decreases the risk of ED visits, hemorrhagic stroke, and cardiovascular events and improves ED visit-free and cardiovascular event-free rates during long-term follow-up.

Classifying Temporomandibular Disorder with Artificial Intelligent Architecture Using Magnetic Resonance Imaging.

This study proposes a new diagnostic tool for automatically extracting discriminative features and detecting temporomandibular joint disc displacement (TMJDD) accurately with artificial intelligence. We analyzed the structural magnetic resonance imaging (MRI) images of 52 patients with TMJDD and 32 healthy controls. The data were split into training and test sets, and only the training sets were used for model construction. U-net was trained with 100 sagittal MRI images of the TMJ to detect the joint cavity between the temporal bone and the mandibular condyle, which was used as the region of interest, and classify the images into binary categories using four convolutional neural networks: InceptionResNetV2, InceptionV3, DenseNet169, and VGG16. The best models were InceptionV3 and DenseNet169; the results of InceptionV3 for recall, precision, accuracy, and F1 score were 1, 0.81, 0.85, and 0.9, respectively, and the corresponding results of DenseNet169 were 0.92, 0.86, 0.85, and 0.89, respectively. Automated detection of TMJDD from sagittal MRI images is a promising technique that involves using deep learning neural networks. It can be used to support clinicians in diagnosing patients as having TMJDD.

Impact of Amyloid Pathology in Mild Cognitive Impairment Subjects: The Longitudinal Cognition and Surface Morphometry Data.

The amyloid framework forms the central medical theory related to Alzheimer disease (AD), and the in vivo demonstration of amyloid positivity is essential for diagnosing AD. On the basis of a longitudinal cohort design, the study investigated clinical progressive patterns by obtaining cognitive and structural measurements from a group of patients with amnestic mild cognitive impairment (MCI); the measurements were classified by the positivity (Aß+) or absence (Aß-) of the amyloid biomarker. We enrolled 185 patients (64 controls, 121 patients with MCI). The patients with MCI were classified into two groups on the basis of their [18F]flubetaben or [18F]florbetapir amyloid positron-emission tomography scan (Aß+ vs. Aß-, 67 vs. 54 patients) results. Data from annual cognitive measurements and three-dimensional T1 magnetic resonance imaging scans were used for between-group comparisons. To obtain longitudinal cognitive test scores, generalized estimating equations were applied. A linear mixed effects model was used to compare the time effect of cortical thickness degeneration. The cognitive decline trajectory of the Aß+ group was obvious, whereas the Aß- and control groups did not exhibit a noticeable decline over time. The group effects of cortical thickness indicated decreased entorhinal cortex in the Aß+ group and supramarginal gyrus in the Aß- group. The topology of neurodegeneration in the Aß- group was emphasized in posterior cortical regions. A comparison of the changes in the Aß+ and Aß- groups over time revealed a higher rate of cortical thickness decline in the Aß+ group than in the Aß- group in the default mode network. The Aß+ and Aß- groups experienced different APOE ε4 effects. For cortical-cognitive correlations, the regions associated with cognitive decline in the Aß+ group were mainly localized in the perisylvian and anterior cingulate regions. By contrast, the degenerative topography of Aß- MCI was scattered. The memory learning curves, cognitive decline patterns, and cortical degeneration topographies of the two MCI groups were revealed to be different, suggesting a difference in pathophysiology. Longitudinal analysis may help to differentiate between these two MCI groups if biomarker access is unavailable in clinical settings.

Deep learning to infer visual acuity from optical coherence tomography in diabetic macular edema.

Purpose: Diabetic macular edema (DME) is one of the leading causes of visual impairment in diabetic retinopathy (DR). Physicians rely on optical coherence tomography (OCT) and baseline visual acuity (VA) to tailor therapeutic regimen. However, best-corrected visual acuity (BCVA) from chart-based examinations may not wholly reflect DME status. Chart-based examinations are subjected findings dependent on the patient's recognition functions and are often confounded by concurrent corneal, lens, retinal, optic nerve, or extraocular disorders. The ability to infer VA from objective optical coherence tomography (OCT) images provides the predicted VA from objective macular structures directly and a better understanding of diabetic macular health. Deviations from chart-based and artificial intelligence (AI) image-based VA will prompt physicians to assess other ocular abnormalities affecting the patients VA and whether pursuing anti-VEGF treatment will likely yield increment in VA. Materials and methods: We enrolled a retrospective cohort of 251 DME patients from Big Data Center (BDC) of Taipei Veteran General Hospital (TVGH) from February 2011 and August 2019. A total of 3,920 OCT images, labeled as "visually impaired" or "adequate" according to baseline VA, were grouped into training (2,826), validation (779), and testing cohort (315). We applied confusion matrix and receiver operating characteristic (ROC) curve to evaluate the performance. Results: We developed an OCT-based convolutional neuronal network (CNN) model that could classify two VA classes by the threshold of 0.50 (decimal notation) with an accuracy of 75.9%, a sensitivity of 78.9%, and an area under the ROC curve of 80.1% on the testing cohort. Conclusion: This study demonstrated the feasibility of inferring VA from routine objective retinal images. Translational relevance: Serves as a pilot study to encourage further use of deep learning in deriving functional outcomes and secondary surrogate endpoints for retinal diseases.

High Inferior Vena Cava Diameter with High Left Ventricular End Systolic Diameter as a Risk Factor for Major Adverse Cardiovascular Events, Cardiovascular and Overall Mortality among Chronic Hemodialysis Patients.

Background: Little is known about the association of inferior vena cava diameter (IVCD) and left ventricular end-systolic diameter (LVESD) with mortality in patients undergoing hemodialysis (HD). Methods: The single medical center observational cohort study enrolled 241 adult chronic HD patients from 1 October 2018 to 31 December 2018. Echocardiography results of IVCD and LVESD prior to dialysis were retrieved and patients were divided into high IVCD and low IVCD groups. Patients who received HD via a tunneled cuffed catheter were excluded. Study outcomes included all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events (MACE). Subgroup analyses of HD patients with high and low LVESD were also performed. Results: The incidence of all-cause mortality, cardiovascular mortality, and MACE were higher in chronic HD patients with high IVCD (p < 0.01). High IVCD patients had significantly greater all-cause mortality, cardiovascular mortality, and MACE (log-rank test; p < 0.05). High IVCD patients are also associated with an increased risk of all-cause mortality and MACE relative to low IVCD patients (aHRs, 2.88 and 3.42; 95% CIs, 1.06−7.86 and 1.73−6.77, respectively; all p < 0.05). In the subgroup analysis of patients with high or low LVESD, the high IVCD remained a significant risk factor for all-cause mortality and MACE, and the HR is especially high in the high LVESD group. Conclusions: Dilated IVCD is a risk factor for all-cause mortality and MACE in chronic HD patients. In addition, these patients with high LVESD also have a significantly higher HR of all-cause mortality and MACE.

Automatic Segmentation of Retinal Fluid and Photoreceptor Layer from Optical Coherence Tomography Images of Diabetic Macular Edema Patients Using Deep Learning and Associations with Visual Acuity.

Diabetic macular edema (DME) is a highly common cause of vision loss in patients with diabetes. Optical coherence tomography (OCT) is crucial in classifying DME and tracking the results of DME treatment. The presence of intraretinal cystoid fluid (IRC) and subretinal fluid (SRF) and the disruption of the ellipsoid zone (EZ), which is part of the photoreceptor layer, are three crucial factors affecting the best corrected visual acuity (BCVA). However, the manual segmentation of retinal fluid and the EZ from retinal OCT images is laborious and time-consuming. Current methods focus only on the segmentation of retinal features, lacking a correlation with visual acuity. Therefore, we proposed a modified U-net, a deep learning algorithm, to segment these features from OCT images of patients with DME. We also correlated these features with visual acuity. The IRC, SRF, and EZ of the OCT retinal images were manually labeled and checked by doctors. We trained the modified U-net model on these labeled images. Our model achieved Sørensen-Dice coefficients of 0.80 and 0.89 for IRC and SRF, respectively. The area under the receiver operating characteristic curve (ROC) for EZ disruption was 0.88. Linear regression indicated that EZ disruption was the factor most strongly correlated with BCVA. This finding agrees with that of previous studies on OCT images. Thus, we demonstrate that our segmentation network can be feasibly applied to OCT image segmentation and assist physicians in assessing the severity of the disease.

Identification of common neural substrates with connectomic abnormalities in four major psychiatric disorders: A connectome-wide association study.

BACKGROUND: Recent imaging studies of large datasets suggested that psychiatric disorders have common biological substrates. This study aimed to identify all the common neural substrates with connectomic abnormalities across four major psychiatric disorders by using the data-driven connectome-wide association method of multivariate distance matrix regression (MDMR). METHODS: This study analyzed a resting functional magnetic resonance imaging dataset of 100 patients with schizophrenia, 100 patients with bipolar I disorder, 100 patients with bipolar II disorder, 100 patients with major depressive disorder, and 100 healthy controls (HCs). We calculated a voxel-wise 4,330 × 4,330 matrix of whole-brain functional connectivity (FC) with 8-mm isotropic resolution for each participant and then performed MDMR to identify structures where the overall multivariate pattern of FC was significantly different between each patient group and the HC group. A conjunction analysis was performed to identify common neural regions with FC abnormalities across these four psychiatric disorders. RESULTS: The conjunction of the MDMR maps revealed that the four groups of patients shared connectomic abnormalities in distributed cortical and subcortical structures, which included bilateral thalamus, cerebellum, frontal pole, supramarginal gyrus, postcentral gyrus, lingual gyrus, lateral occipital cortex, and parahippocampus. The follow-up analysis based on pair-wise FC of these regions demonstrated that these psychiatric disorders also shared similar patterns of FC abnormalities characterized by sensory/subcortical hyperconnectivity, association/subcortical hypoconnectivity, and sensory/association hyperconnectivity. CONCLUSIONS: These findings suggest that major psychiatric disorders share common connectomic abnormalities in distributed cortical and subcortical regions and provide crucial support for the common network hypothesis of major psychiatric disorders.

Smartphone-based diabetic macula edema screening with an offline artificial intelligence.

BACKGROUND: Diabetic macular edema (DME) is a sight-threatening condition that needs regular examinations and remedies. Optical coherence tomography (OCT) is the most common used examination to evaluate the structure and thickness of the macula, but the software in the OCT machine does not tell the clinicians whether DME exists directly. Recently, artificial intelligence (AI) is expected to aid in diagnosis generation and therapy selection. We thus develop a smartphone-based offline AI system that provides diagnostic suggestions and medical strategies through analyzing OCT images from diabetic patients at the risk of developing DME. METHODS: DME patients receiving treatments in 2017 at Taipei Veterans General Hospital were included in this study. We retrospectively collected the OCT images of these patients from January 2008 to July 2018. We established the AI model based on MobileNet architecture to classify the OCT images conditions. The confusion matrix has been applied to present the performance of the trained AI model. RESULTS: Based on the convolutional neural network with the MobileNet model, our AI system achieved a high DME diagnostic accuracy of 90.02%, which is comparable to other AI systems such as InceptionV3 and VGG16. We further developed a mobile-application based on this AI model available at https://aicl.ddns.net/DME.apk. CONCLUSION: We successful integrated an AI model into the mobile device to provide an offline method to provide the diagnosis for quickly screening the risk of developing DME. With the offline property, our model could help those nonophthalmological healthcare providers in offshore islands or underdeveloped countries.

Effect of Renin-Angiotensin-Aldosterone System Blockade on Long-Term Outcomes in Postacute Kidney Injury Patients With Hypertension.

OBJECTIVES: Renal replacement therapy-requiring acute kidney injury frequently occurs in ICUs, which require evidence-based medical attention. However, in the postacute kidney injury patient population, the evidence regarding effective therapies to improve patient outcomes is lacking. Therefore, we aimed to examine whether the renin-angiotensin-aldosterone system blockade is effective in improving renal outcomes in postacute kidney injury patients who experienced temporary renal replacement therapy and have hypertension. DESIGN: A retrospective cohort study. SETTING: A nationwide database in Taiwan. PATIENTS: From January 1, 2000, to December 31, 2013, we identified 8,558 acute kidney injury patients with hypertension in the national registry database. All these patients experienced an acute kidney injury episode, which required temporary renal replacement therapy for at least once. INTERVENTIONS: Users (n = 3,885) and nonusers (n = 4,673) of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers. MEASUREMENTS AND MAIN RESULTS: We used Cox proportional hazards regression models to analyze hazard ratios for the commencement of end-stage renal disease and all-cause mortality for angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker users (n = 3,885) and nonusers (n = 4,673).In a median follow-up of 4.3 years, 5,880 patients (68.7%) required long-term dialysis, and 4,841 patients (56.6%) died. Compared with postacute kidney injury patients who did not use angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker users are marginally less likely to progress to end-stage renal disease (adjusted hazard ratio 0.95; 95% CI 0.90-1.01; p = 0.06) and significantly less likely to suffer from all-cause mortality (adjusted hazard ratio 0.93; 95% CI 0.87-0.98; p = 0.011). CONCLUSIONS: In patients who experienced renal replacement therapy-requiring acute kidney injury and have hypertension, angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker use is associated with better survival outcomes compared with nonuser.

Increased Proinflammatory Cytokines, Executive Dysfunction, and Reduced Gray Matter Volumes In First-Episode Bipolar Disorder and Major Depressive Disorder.

BACKGROUNDS: The association between systemic inflammation, executive dysfunction, and gray matter (GM) volume difference in first-episode affective disorders, including bipolar and major depressive disorders, is unclear. METHODS: Twenty-two patients with first-episode bipolar disorder, 22 age- and sex-matched patients with first-episode major depressive disorder, and 22 matched controls were enrolled in our study; all patients underwent comprehensive assessments, including clinical assessment, executive function examination (Wisconsin card sorting test [WCST]), proinflammatory cytokine receptors (soluble interleukin-6 receptor and tumor necrosis factor-α receptor 1 [TNFR1]), and brain magnetic resonance imaging. Voxel-based morphometry was performed to analyze the GM volume difference between bipolar and major depressive disorders. RESULTS: Patients with bipolar disorder were more likely to exhibit higher levels of TNFR1 (P = .038), more number of deficits in WCST (P < .05), and smaller GM volume in the middle frontal cortex (uncorrected voxel level P < .001) compared with those with major depressive disorder and healthy controls. Positive associations were observed between the middle frontal cortex volume, executive function, and the TNFR1 level. DISCUSSION: GM volume reduction in the middle frontal cortex, a greater level of systemic inflammation, and executive dysfunction were observed in first-episode affective disorders, especially bipolar disorder. A positive correlation between middle frontal cortex volume, executive function, and the TNFR1 level may indicate a divergent effect of brain and systemic inflammation functioning in the early phase (first episode) of affective disorder.

The era of artificial intelligence-based individualized telemedicine is coming.

Artificial intelligence (AI), Internet of Things (IoT), and telemedicine are deeply involved in our daily life and have also been extensively applied in the medical field, especially in ophthalmology. Clinical ophthalmologists are required to perform a vast array of image exams and analyze images containing complicated information, which allows them to diagnose the disease type and grade, make a decision on remedy, and predict treatment outcomes. AI has a great potential to assist ophthalmologists in their daily routine of image analysis and relieve their work burden. However, in spite of these prospects, the application of AI may also be controversial and associated with several legal, ethical, and sociological concerns. In spite of these issues, AI has indeed become an irresistible trend and is widely used by medical specialists in their daily routines in what we can call now, the era of AI. This review will encompass those issues and focus on recent research on the AI application in ophthalmology and telemedicine.

A novelty route for smartphone-based artificial intelligence approach to ophthalmic screening.

Artificial intelligence (AI) has been widely applied in the medical field and achieved enormous milestones in helping specialists to make diagnosis and remedy decisions, particularly in the field of eye diseases and ophthalmic screening. With the development of AI-based systems, the enormous hardware and software resources are required for optimal performance. In reality, there are many places on the planet where such resources are highly limited. Hence, the smartphone-based AI systems can be used to provide a remote control route to quickly screen eye diseases such as diabetic-related retinopathy or diabetic macular edema. However, the performance of such mobile-based AI systems is still uncharted territory. In this article, we discuss the issues of computing resource consumption and performance of the mobile device-based AI systems and highlight recent research on the feasibility and future potential of application of the mobile device-based AI systems in telemedicine.

Optical coherence tomography-based diabetic macula edema screening with artificial intelligence.

BACKGROUND: Optical coherence tomography (OCT) is considered as a sensitive and noninvasive tool to evaluate the macular lesions. In patients with diabetes mellitus (DM), the existence of diabetic macular edema (DME) can cause significant vision impairment and further intravitreal injection (IVI) of anti-vascular endothelial growth factor (VEGF) is needed. However, the increasing number of DM patients makes it a big burden for clinicians to manually determine whether DME exists in the OCT images. The artificial intelligence (AI) now enormously applied to many medical territories may help reduce the burden on clinicians. METHODS: We selected DME patients receiving IVI of anti-VEGF or corticosteroid at Taipei Veterans General Hospital in 2017. All macular cross-sectional scan OCT images were collected retrospectively from the eyes of these patients from January 2008 to July 2018. We further established AI models based on convolutional neural network architecture to determine whether the DM patients have DME by OCT images. RESULTS: Based on the convolutional neural networks, InceptionV3 and VGG16, our AI system achieved a high DME diagnostic accuracy of 93.09% and 92.82%, respectively. The sensitivity of the VGG16 and InceptionV3 models was 96.48% and 95.15%., respectively. The specificity was corresponding to 86.67% and 89.63% for VGG16 and InceptionV3, respectively. We further developed an OCT-driven platform based on these AI models. CONCLUSION: We successfully set up AI models to provide an accurate diagnosis of DME by OCT images. These models may assist clinicians in screening DME in DM patients in the future.