Automatic CDR Estimation for Early Glaucoma Diagnosis.
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ABSTRACT:
SUBMITTER: Fernandez-Granero MA
PROVIDER: S-EPMC5723944 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
Automatic CDR Estimation for Early Glaucoma Diagnosis.
Fernandez-Granero M A MA Sarmiento A A Sanchez-Morillo D D Jiménez S S Alemany P P Fondón I I
Journal of healthcare engineering 20171127
Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE <i>L</i><sup>∗</sup><i>a</i><sup>∗</sup><i>b</i><sup>∗</sup> colour ...[more]
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Project description:BACKGROUND: Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease. OBJECTIVE: To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening. MATERIALS AND METHODS: 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features. RESULTS AND DISCUSSION: Receiver operating characteristic curves were plotted to compare AGLAIA-MII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure. CONCLUSIONS: AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.
| S-EPMC3822106 | biostudies-literature
Project description:PurposeTo investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CNN) classifier, for early-glaucoma vs. control discrimination.MethodTwo data sets of VFs acquired with the OCTOPUS 101 G1 program and the Humphrey Field Analyzer 24-2 pattern were subdivided into control and early-glaucomatous groups, and converted into a new image using a novel voronoi representation to train a custom-designed CNN so to discriminate between control and early-glaucomatous eyes. Saliency maps that highlight what regions of the VF are contributing maximally to the classification decision were computed to provide classification justification. Model fitting was cross-validated and average precision (AP) score performances were computed for our method, Mean Defect (MD), square-root of Loss Variance (sLV), their combination (MD+sLV), and a Neural Network (NN) that does not use convolutional features.ResultsCNN achieved the best AP score (0.874±0.095) across all test folds for one data set compared to others (MD = 0.869±0.064, sLV = 0.775±0.137, MD+sLV = 0.839±0.085, NN = 0.843±0.089) and the third best AP score (0.986 ±0.019) on the other one with slight difference from the other methods (MD = 0.986±0.023, sLV = 0.992±0.016, MD+sLV = 0.987±0.017, NN = 0.985±0.017). In general, CNN consistently led to high AP across different data sets. Qualitatively, computed saliency maps appeared to provide clinically relevant information on the CNN decision for individual VFs.ConclusionThe proposed CNN offers high classification performance for the discrimination of control and early-glaucoma VFs when compared with standard clinical decision measures. The CNN classification, aided by saliency visualization, may support clinicians in the automatic discrimination of early-glaucomatous and normal VFs.
| S-EPMC6261540 | biostudies-literature
Project description:Compare performance of normalized reflectance index (NRI) and retinal nerve fiber layer thickness (RNFLT) parameters determined from optical coherence tomography (OCT) images for glaucoma and glaucoma suspect diagnosis.Seventy-five eyes from 71 human subjects were studied: 33 controls, 24 glaucomatous, and 18 glaucoma-suspects. RNFLT and NRI maps were measured using 2 custom-built OCT systems and the commercial instrument RTVue. Using area under the receiver operating characteristic curve, RNFLT and NRI measured in 7 RNFL locations were analyzed to distinguish between control, glaucomatous, and glaucoma-suspect eyes.The mean NRI of the control group was significantly larger than the means of glaucomatous and glaucoma-suspect groups in most RNFL locations for all 3 OCT systems (P<0.05 for all comparisons). NRI performs significantly better than RNFLT at distinguishing between glaucoma-suspect and control eyes using RTVue OCT (P=0.008). The performances of NRI and RNFLT for classifying glaucoma-suspect versus control eyes were statistically indistinguishable for PS-OCT-EIA (P=0.101) and PS-OCT-DEC (P=0.227). The performances of NRI and RNFLT for classifying glaucomatous versus control eyes were statistically indistinguishable (PS-OCT-EIA: P=0.379; PS-OCT-DEC: P=0.338; RTVue OCT: P=0.877).NRI is a promising measure for distinguishing between glaucoma-suspect and control eyes and may indicate disease in the preperimetric stage. Results of this pilot clinical study warrant a larger study to confirm the diagnostic power of NRI for diagnosing preperimetric glaucoma.
| S-EPMC3844555 | biostudies-literature
Project description:The pathogenesis of glaucoma is strongly associated with the occurrence of autoimmune-mediated loss of retinal ganglion cells (RGCs) and additionally, recent evidence shows that specific antibody-derived signature peptides are significantly differentially expressed in sera of primary-open angle glaucoma patients (POAG) compared to healthy controls. Synthetically antibody-derived peptides can modulate various effector functions of the immune system and act as antimicrobial or antiviral molecules. In an ex vivo adolescent glaucoma model, this study, for the first time, demonstrates that polyclonal-derived complementarity-determining regions (CDRs) can significantly increase the survival rate of RGCs (p = 0.013). We subsequently performed affinity capture experiments that verified the mitochondrial serine protease HTRA2 (gene name: HTRA2) as a high-affinity retinal epitope target of CDR1 sequence motif ASGYTFTNYGLSWVR. Quantitative proteomic analysis of the CDR-treated retinal explants revealed increased expression of various anti-apoptotic and anti-oxidative proteins (e.g., VDAC2 and TXN) compared to untreated controls (p < 0.05) as well as decreased expression levels of cellular stress response markers (e.g., HSPE1 and HSP90AA1). Mitochondrial dysfunction, the protein ubiquitination pathway and oxidative phosphorylation were annotated as the most significantly affected signaling pathways and possibly can be traced back to the CDR-induced inhibition or modulation of the master regulator HTRA2. These findings emphasize the great potential of synthetic polyclonal-derived CDR peptides as therapeutic agents in future glaucoma therapy and provide an excellent basis for affinity-based biomarker discovery purposes.
| S-EPMC6723090 | biostudies-literature
Project description:Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen binding function. The key area for antigen binding and the main area of structural variation in antibodies is concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a step change in our ability to predict protein structures. In this work we present ABlooper, an end-to-end equivariant deep-learning based CDR loop structure prediction tool. ABlooper rapidly predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average CDR-H3 RMSD of 2.49 Å, which drops to 2.05 Å when considering only its 75% most confident predictions. https://github.com/oxpig/ABlooper. Supplementary data are available at Bioinformatics online.
| S-EPMC8963302 | biostudies-literature
Project description:The pathogenesis of glaucoma is strongly associated with the occurrence of autoimmune-mediated loss of retinal ganglion cells (RGCs) and recently it was proven that specific antibody-derived signature peptides are significantly differentially expressed in sera of primary open angle glaucoma patients (POAG) compared to healthy controls. Synthetic antibody-derived peptides are known since several years to modulate various effector functions of the immune system and to act as antimicrobial or antiviral molecules. The present study shows for the first time that polyclonal-derived complementarity-determining regions (CDRs) significantly increased the survival rate of RGCs in an ex vivo glaucoma model (P=0.013) by using immunohistochemical staining techniques. Affinity capture experiments verified serine protease HTRA2 (mitochondrial) as high-confident retinal epitope target of CDR1 sequence motive ASGYTFTNYGLSWVR. Quantitative proteomic analysis of the CDR-treated retinal explants revealed increased expression of various anti-apoptotic and anti-oxidative proteins (e.g. VDAC2 and TXN) compared to untreated controls (P<0.05) and decreased expression levels of cellular stress response markers (e.g. HSPE1 and HSP90AA1) were observed. Mitochondrial dysfunction, protein ubiquitination pathway and oxidative phosphorylation were annotated as the most significantly affected signaling pathways and can possibly be traced back to the CDR-induced inhibition or modulation of the master regulator HTRA2. These findings emphasize the great potential of synthetic polyclonal-derived CDR peptides as therapeutic agents in future glaucoma therapy and provide an excellent basis for affinity-based biomarker discovery purposes.
2019-08-15 | PXD014718 | Pride
Project description:Expectiles induce a law-invariant risk measure that has recently gained popularity in actuarial and financial risk management applications. Unlike quantiles or the quantile-based Expected Shortfall, the expectile risk measure is coherent and elicitable. The estimation of extreme expectiles in the heavy-tailed framework, which is reasonable for extreme financial or actuarial risk management, is not without difficulties; currently available estimators of extreme expectiles are typically biased and hence may show poor finite-sample performance even in fairly large samples. We focus here on the construction of bias-reduced extreme expectile estimators for heavy-tailed distributions. The rationale for our construction hinges on a careful investigation of the asymptotic proportionality relationship between extreme expectiles and their quantile counterparts, as well as of the extrapolation formula motivated by the heavy-tailed context. We accurately quantify and estimate the bias incurred by the use of these relationships when constructing extreme expectile estimators. This motivates the introduction of classes of bias-reduced estimators whose asymptotic properties are rigorously shown, and whose finite-sample properties are assessed on a simulation study and three samples of real data from economics, insurance and finance.Supplementary informationThe online version contains supplementary material available at 10.1007/s11222-022-10118-x.
| S-EPMC9362073 | biostudies-literature
Project description:BackgroundTo develop a deep neural network able to differentiate glaucoma from non-glaucoma visual fields based on visual filed (VF) test results, we collected VF tests from 3 different ophthalmic centers in mainland China.MethodsVisual fields obtained by both Humphrey 30-2 and 24-2 tests were collected. Reliability criteria were established as fixation losses less than 2/13, false positive and false negative rates of less than 15%.ResultsWe split a total of 4012 PD images from 1352 patients into two sets, 3712 for training and another 300 for validation. There is no significant difference between left to right ratio (P = 0.6211), while age (P = 0.0022), VFI (P = 0.0001), MD (P = 0.0039) and PSD (P = 0.0001) exhibited obvious statistical differences. On the validation set of 300 VFs, CNN achieves the accuracy of 0.876, while the specificity and sensitivity are 0.826 and 0.932, respectively. For ophthalmologists, the average accuracies are 0.607, 0.585 and 0.626 for resident ophthalmologists, attending ophthalmologists and glaucoma experts, respectively. AGIS and GSS2 achieved accuracy of 0.459 and 0.523 respectively. Three traditional machine learning algorithms, namely support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN) were also implemented and evaluated in the experiments, which achieved accuracy of 0.670, 0.644, and 0.591 respectively.ConclusionsOur algorithm based on CNN has achieved higher accuracy compared to human ophthalmologists and traditional rules (AGIS and GSS2) in differentiation of glaucoma and non-glaucoma VFs.
| S-EPMC6172715 | biostudies-literature
Project description:Conventional magnetic resonance imaging (cMRI) is poorly sensitive to pathological changes related to multiple sclerosis (MS) in normal-appearing white matter (NAWM) and gray matter (GM), with the added difficulty of not being very reproducible. Quantitative MRI (qMRI), on the other hand, attempts to represent the physical properties of tissues, making it an ideal candidate for quantitative medical image analysis or radiomics. We therefore hypothesized that qMRI-based radiomic features have added diagnostic value in MS compared to cMRI. This study investigated the ability of cMRI (T1w) and qMRI features extracted from white matter (WM), NAWM, and GM to distinguish between MS patients (MSP) and healthy control subjects (HCS). We developed exploratory radiomic classification models on a dataset comprising 36 MSP and 36 HCS recruited in CHU Liege, Belgium, acquired with cMRI and qMRI. For each image type and region of interest, qMRI radiomic models for MS diagnosis were developed on a training subset and validated on a testing subset. Radiomic models based on cMRI were developed on the entire training dataset and externally validated on open-source datasets with 167 HCS and 10 MSP. Ranked by region of interest, the best diagnostic performance was achieved in the whole WM. Here the model based on magnetization transfer imaging (a type of qMRI) features yielded a median area under the receiver operating characteristic curve (AUC) of 1.00 in the testing sub-cohort. Ranked by image type, the best performance was achieved by the magnetization transfer models, with median AUCs of 0.79 (0.69-0.90, 90% CI) in NAWM and 0.81 (0.71-0.90) in GM. The external validation of the T1w models yielded an AUC of 0.78 (0.47-1.00) in the whole WM, demonstrating a large 95% CI and a low sensitivity of 0.30 (0.10-0.70). This exploratory study indicates that qMRI radiomics could provide efficient diagnostic information using NAWM and GM analysis in MSP. T1w radiomics could be useful for a fast and automated check of conventional MRI for WM abnormalities once acquisition and reconstruction heterogeneities have been overcome. Further prospective validation is needed, involving more data for better interpretation and generalization of the results.
| S-EPMC8374240 | biostudies-literature
Project description:IntroductionEarly diagnosis and etiological treatment can effectively improve the prognosis of patients with autoimmune encephalitis (AE). However, anti-neuronal antibody tests which provide the definitive diagnosis require time and are not always abnormal. By using natural language processing (NLP) technology, our study proposes an assisted diagnostic method for early clinical diagnosis of AE and compares its sensitivity with that of previously established criteria.MethodsOur model is based on the text classification model trained by the history of present illness (HPI) in electronic medical records (EMRs) that present a definite pathological diagnosis of AE or infectious encephalitis (IE). The definitive diagnosis of IE was based on the results of traditional etiological examinations. The definitive diagnosis of AE was based on the results of neuronal antibodies, and the diagnostic criteria of definite autoimmune limbic encephalitis proposed by Graus et al. used as the reference standard for antibody-negative AE. First, we automatically recognized and extracted symptoms for all HPI texts in EMRs by training a dataset of 552 cases. Second, four text classification models trained by a dataset of 199 cases were established for differential diagnosis of AE and IE based on a post-structuring text dataset of every HPI, which was completed using symptoms in English language after the process of normalization of synonyms. The optimal model was identified by evaluating and comparing the performance of the four models. Finally, combined with three typical symptoms and the results of standard paraclinical tests such as cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), or electroencephalogram (EEG) proposed from Graus criteria, an assisted early diagnostic model for AE was established on the basis of the text classification model with the best performance.ResultsThe comparison results for the four models applied to the independent testing dataset showed the naïve Bayesian classifier with bag of words achieved the best performance, with an area under the receiver operating characteristic curve of 0.85, accuracy of 84.5% (95% confidence interval [CI] 74.0-92.0%), sensitivity of 86.7% (95% CI 69.3-96.2%), and specificity of 82.9% (95% CI 67.9-92.8%), respectively. Compared with the diagnostic criteria proposed previously, the early diagnostic sensitivity for possible AE using the assisted diagnostic model based on the independent testing dataset was improved from 73.3% (95% CI 54.1-87.7%) to 86.7% (95% CI 69.3-96.2%).ConclusionsThe assisted diagnostic model could effectively increase the early diagnostic sensitivity for AE compared to previous diagnostic criteria, assist physicians in establishing the diagnosis of AE automatically after inputting the HPI and the results of standard paraclinical tests according to their narrative habits for describing symptoms, avoiding misdiagnosis and allowing for prompt initiation of specific treatment.
| S-EPMC9338198 | biostudies-literature