UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic.

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Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

Otolaryngology ◽

10.1177/0194599820939071 ◽

2020 ◽

Vol 163 (6) ◽

pp. 1156-1165

Author(s):

Juan Xiao ◽

Qiang Xiao ◽

Wei Cong ◽

Ting Li ◽

Shouluan Ding ◽

...

Keyword(s):

Thyroid Nodules ◽

Characteristic Curve ◽

Area Under The Curve ◽

Diagnostic Study ◽

Diagnostic Efficiency ◽

Training Set ◽

Multivariable Logistic Regression Model ◽

Predictive Values ◽

Validation Set ◽

Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

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Validation of the efficacy of the NUTRISCORE for the nutritional screening of cancer patients in China

BMC Cancer ◽

10.1186/s12885-021-09135-2 ◽

2022 ◽

Vol 22 (1) ◽

Author(s):

Junren Kang ◽

Hailong Li ◽

Xiaodong Shi ◽

Enling Ma ◽

Wei Chen

Keyword(s):

Cancer Patients ◽

Screening Tool ◽

Clinical Teaching ◽

Characteristic Curve ◽

Area Under The Curve ◽

Teaching Hospitals ◽

Kappa Index ◽

Nutritional Screening ◽

Cross Sectional ◽

Sensitivity Specificity

Abstract Background Malnutrition is common in cancer patients. The NUTRISCORE is a newly developed cancer-specific nutritional screening tool and was validated by comparison with the Patient-Generated Subjective Global Assessment (PG-SGA) and Malnutrition Screening Tool (MST) in Spain. We aimed to evaluate the performance of the NUTRISCORE, MST, and PG-SGA in estimating the risk of malnutrition in Chinese cancer patients. Methods Data from an open parallel and multicenter cross-sectional study in 29 clinical teaching hospitals in 14 Chinese cities were used. Cancer patients were assessed for malnutrition using the PG-SGA, NUTRISCORE, and MST. The sensitivity, specificity, and areas under the receiver operating characteristic curve were estimated for the NUTRISCORE and MST using the PG-SGA as a reference. Results A total of 1000 cancer patients were included. The mean age was 55.9 (19 to 92 years), and 47.5% were male. Of these patients, 450 (45.0%) had PG-SGA B and C, 29 (2.9%) had a NUTRISCORE ≥5, and 367 (36.7%) had an MST ≥ 2. Using the PG-SGA as a reference, the sensitivity, specificity, and area under the curve values of the NUTRISCORE were found to be 6.2, 99.8%, and 0.53, respectively. The sensitivity, specificity, and area under the curve values of the MST were 50.9, 74.9%, and 0.63, respectively. The kappa index between the NUTRISCORE and PG-SGA was 0.066, and that between the MST and PG-SGA was 0.262 (P < 0.05). Conclusions The NUTRISCORE had an extremely low sensitivity in cancer patients in China compared with the MST when the PG-SGA was used as a reference.

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Diagnostic Value of T-SPOT.TB Assay for Tuberculous Peritonitis: A Meta-Analysis

Frontiers in Medicine ◽

10.3389/fmed.2020.585180 ◽

2020 ◽

Vol 7 ◽

Author(s):

Ying Luo ◽

Ying Xue ◽

Liyan Mao ◽

Qun Lin ◽

Guoxing Tang ◽

...

Keyword(s):

Likelihood Ratio ◽

Meta Analysis ◽

Characteristic Curve ◽

Area Under The Curve ◽

Positive Likelihood Ratio ◽

Negative Likelihood Ratio ◽

Abdominal Tuberculosis ◽

Tuberculous Peritonitis ◽

Summary Receiver Operating Characteristic ◽

Sensitivity Specificity

Background: Tuberculous peritonitis (TP) is a common form of abdominal tuberculosis (TB). Diagnosing TP remains challenging in clinical practice. The aim of the present meta-analysis was to evaluate the diagnostic accuracy of peripheral blood (PB) T-SPOT and peritoneal fluid (PF) T-SPOT for diagnosing TP.Methods: PubMed, EmBase, Cochrane, Scopus, Google scholar, China national knowledge internet, and Wan-Fang databases were searched for relevant articles from August 1, 2005 to July 5, 2020. Statistical analysis was performed using Stata, Revman, and Meta-Disc software. Diagnostic parameters including pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were determined. Summary receiver operating characteristic curve was used to determine the area under the curve (AUC).Results: Twelve studies were eligible and included in the meta-analysis. The analysis showed that the pooled sensitivity and specificity of PB T-SPOT in diagnosing TP were 0.91 (95% CI, 0.88–0.94) and 0.78 (95% CI, 0.73–0.81), respectively, while the pooled PLR, NLR, and DOR were 4.05 (95% CI, 2.73–6.01), 0.13 (95% CI, 0.07–0.23), and 37.8 (95% CI, 15.04–94.98), respectively. On the other hand, the summary estimates of sensitivity, specificity, PLR, NLR, and DOR of PF T-SPOT for TP diagnosis were 0.90 (95% CI, 0.85–0.94), 0.78 (95% CI, 0.72–0.83), 6.35 (95% CI, 2.67–15.07), 0.14 (95% CI, 0.09–0.21), and 58.22 (95% CI, 28.76–117.83), respectively. Furthermore, the AUC of PB T-SPOT and PF T-SPOT for TP diagnosis were 0.91 and 0.94, respectively.Conclusions: Our results indicate that both PB T-SPOT and PF T-SPOT can be served as sensitive approaches for the diagnosis of TP. However, the unsatisfactory specificities of these two methods limit their application as rule-in tests for TP diagnosis. Furthermore, the standardization of the operating procedure of PF T-SPOT is further needed.

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Diabetic Retinopathy Diagnosing using Fuzzy Image Processing

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9240.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 4210-4215

Keyword(s):

Image Processing ◽

Fuzzy Logic ◽

Diabetic Retinopathy ◽

Expert System ◽

Diagnostic System ◽

Diabetic Patients ◽

Input Output ◽

Fuzzy Image ◽

Retina Images ◽

Oct Image

Aim: To design diagnostic expert system using fuzzy image processing for diabetic retinopathy, measures diabetic eye morbidity. Method: From this research paper, diagnosing diabetic retinopathy using fuzzy image processing for diabetic patients. Firstly collection of OCT images of the patient who has diabetic retinopathy. Author’s proposed method finds out the edge detection of the OCT image. Then fuzzy logic is applied on that result of image processing. Design a fuzzy rules and input- output parameter. This method gives accurate diagnosing the diabetic retinopathy from the image of the patient’s retina images. Result: This diagnostic system gives patient’s eye morbidity, vision threatening of the diabetic patients. In the result, edges of the retina images, and from that retinal ruptures, thickness of the proliferative in the retina. From these result, diagnostic of diabetic retinopathy conditions such as PDR, NPDR, and NORMAL, and CSME in the diabetic patients. Conclusion: author has design diagnostic system for endocrinologist and ophthalmology to diagnosed diabetic retinopathy in the patients. From this system doctors don’t need patients for diagnosing purposed.

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Validation of the Indonesian Version of Modified Checklist for Autism in Toddlers: a diagnostic study

Paediatrica Indonesiana ◽

10.14238/pi60.3.2020.159-65 ◽

2020 ◽

Vol 60 (3) ◽

pp. 159-65

Author(s):

Hendra Salim ◽

Soetjiningsih Soetjiningsih ◽

I Gusti Ayu Trisna Windiani ◽

I Gede Raka Widiana ◽

PITIKA ASPR

Keyword(s):

Predictive Value ◽

Characteristic Curve ◽

Area Under The Curve ◽

Negative Likelihood Ratio ◽

Autism Spectrum ◽

Diagnostic Study ◽

Diagnostic Validity ◽

Dsm Iv ◽

Pediatric Outpatients ◽

Sensitivity Specificity

Background Autism is a developmental disorder for which early detection in toddlers is recommended because of its increased prevalence. The Modified Checklist for Autism in Toddlers (M-CHAT) is an easy-to-interprete tool that can be filled out by parents. It has been translated into the Indonesian language but needs to be validated. Objective To evaluate the diagnostic validity of the Indonesian version of M-CHAT in detection of autism spectrum disorder in Indonesia. Methods A diagnostic study was conducted at Sanglah Hospital, Denpasar, Bali, from March 2011 to August 2013. Pediatric outpatients aged 18 to 48 months were included. The Indonesian version of the M-CHAT tool was filled by parents. Autism assessment was done according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV-TR). The assessment results were analyzed with the MedCalc program software, in several steps: (i) reliability of M-CHAT; (ii) description, distribution, and proportion to determine the characteristics of the subjects of research; and (iii) validity of M-CHAT compared to the gold standard DSM-IV-TR by a receiver operating characteristic curve and several area under the curve cut-off points, in order to assess the sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratio, accompanied by the 95% confidence interval of each value. Results The Indonesian version of M-CHAT in toddlers had 82.35% sensitivity and 89.68% specificity, using the cut-off point of more than 6 failed questions. Conclusion The Indonesian version M-CHAT translated by Soetjiningsih has optimal diagnostic validity for detection of autism in toddlers.

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Reliability of Using the Proposed International Consensus Video Signs of Potential Concussion for National Rugby League Head Impact Events

Neurosurgery ◽

10.1093/neuros/nyaa437 ◽

2020 ◽

Author(s):

Andrew J Gardner

Keyword(s):

Characteristic Curve ◽

Area Under The Curve ◽

Rugby League ◽

Head Impact ◽

International Consensus ◽

Global Consistency ◽

Definition Of ◽

Sensitivity Specificity ◽

Individual Sports ◽

Impact Events

Abstract BACKGROUND Consensus on the definition of extant video signs of concussion have recently been proposed by representatives of international sporting codes for global consistency across professional leagues. OBJECTIVE To review the reliability of the proposed international consensus video signs of concussion in National Rugby League (NRL) head impact events (HIEs). METHODS The video signs of concussion were coded for every HIE during the 2019 NRL season. Coding was conducted blinded to the concussion status. Frequency, sensitivity, specificity, and a receiver operating characteristic curve were calculated. RESULTS There were 943 HIEs identified over the 2019 NRL season, of which 106 resulted in a diagnosed concussion. The most frequently observed video sign in concussed athletes was blank/vacant look (54%), which was also the most sensitive video sign (0.54, CI: 0.44-0.63), while the most specific was tonic posturing (0.99, CI: 0.99-1.00). In 43.4% of diagnosed concussions none of the 6 video signs were present. The 6 video signs demonstrated a “fair” ability to discriminate between concussion and nonconcussion HIEs (area under the curve = 0.76). CONCLUSION International consensus agreement between collision sports for extant video signs of concussion and the definition of those extant video signs are clinically important. The selection of signs requires rigorous assessment to examine their predictive value across all sports and within individual sports, and to determine further video signs to compliment and improve the identification of possible concussion events within various sports. The current study demonstrated that, for NRL-related HIEs, the diagnostic accuracy of video signs varies.

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External Validation of Different Scoring Systems for Suspected Choledocholithiasis

Digestive Surgery ◽

10.1159/000495696 ◽

2019 ◽

Vol 36 (6) ◽

pp. 530-538

Author(s):

Nicolò Tamini ◽

Davide Paolo Bernasconi ◽

Luca Gianotti

Keyword(s):

Operating Characteristic ◽

Bile Duct Stone ◽

Characteristic Curve ◽

External Validation ◽

Area Under The Curve ◽

Scoring Systems ◽

Youden Index ◽

Predictive Values ◽

Sensitivity Specificity ◽

Symptoms And Signs

Aim of the Study: The diagnosis of choledocholithiasis is challenging. Previously published scoring systems designed to calculate the risk of choledocholithiasis were evaluated to appraise the diagnostic performance. Patients and Methods: Data of patients who were admitted between 2013 and 2015 with the following characteristics were retrieved: bile stone-related symptoms and signs, and indication to laparoscopic cholecystectomy. To validate and appraise the performance of the 6 scoring systems, the acknowledged domains of each metrics were applied to the present cohort. Sensitivity, specificity, positive, negative predictive, Youden index, and receiver operating characteristic curve with the area under the curve (AUC) values of the scores were calculated. Results: Two-hundred patients were analyzed. The highest sensitivity and specificity were obtained from the Menezes’ (96.6%) and Telem’s (99.3%) metrics respectively. The Telem’s and Menezes’ scores had the best positive (75.0%) and negative (96.4%) predictive values respectively. The best accuracy, as computed by the Youden index and AUC, was found for the Soltan’s scoring system (0.628 and 0.88, respectively). Conclusion: The available scoring systems are precise only in identifying patients with a negligible risk of common bile duct stone, but overall insufficiently accurate to suggest the routine use in clinical practice.

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An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study (Preprint)

10.2196/preprints.16528 ◽

2019 ◽

Author(s):

Yi-Lien Lee ◽

Willy Chou ◽

Tsair-Wei Chien ◽

Po-Hsin Chou ◽

Yu-Tsen Yeh ◽

...

Keyword(s):

Predictive Accuracy ◽

Early Stage ◽

Medical Center ◽

Characteristic Curve ◽

Area Under The Curve ◽

Maslach Burnout Inventory ◽

Well Being ◽

Population Based ◽

Psychological Status ◽

Sensitivity Specificity

BACKGROUND Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute’s well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature. OBJECTIVE The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory–Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage. METHODS We recruited 1002 nurses working in a medical center in Taiwan to complete the Chinese version of the 20-item MBI-HSS in August 2016. The k-mean and CNN were used as unsupervised and supervised learnings for dividing nurses into two classes (n=531 and n=471 of suspicious BO+ and BO−, respectively) and building a BO predictive model to estimate 38 parameters. Data were separated into training and testing sets in a proportion 70%:30%, and the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve) across studies for comparison. An app predicting respondent BO was developed involving the model’s 38 estimated parameters for a website assessment. RESULTS We observed that (1) the 20-item model yields a higher accuracy rate (0.95) with an area under the curve of 0.97 (95% CI 0.94-0.95) based on the 1002 cases, (2) the scheme named matching personal response to adapt for the correct classification in model drives the prior model’s predictive accuracy at 100%, (3) the 700-case training set with 0.96 accuracy predicts the 302-case testing set reaching an accuracy of 0.91, and (4) an available MBI-HSS app for nurses predicting BO was successfully developed and demonstrated in this study. CONCLUSIONS The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of nurse BO has been particularly demonstrated in Excel (Microsoft Corp). An app developed for helping nurses to self-assess job BO at an early stage is required for application in the future.

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Classification of Diabetic Retinopathy Features using Bag of Feature Model

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4145.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 381-385

Keyword(s):

Diabetic Retinopathy ◽

Blood Vessels ◽

Early Stage ◽

Medical Image Analysis ◽

Feature Learning ◽

Initial Step ◽

Feature Model ◽

Automated System ◽

Morphological Operations ◽

Speeded Up Robust Features

Deep learning (DL) as well as feature learning by unsupervised methods have made tremendous consideration in the past decades because of its great and dynamic capacity to change input data into high level depictions by means of various machine learning (ML) methods and approaches. Therefore these interests have also showed a fast and steady growth in the arena of medical image analysis, especially in Diabetic Retinopathy (DR) classification. On contradiction, manual interpretation involves excessive processing time, large amount of expertise and work. Sternness of the DR is analyzed relative to the existence of Microaneurysms (MAs), Exudates (EXs) and Hemorrhages(HEs). Spotting of DR in its early stage is crucial and important to avoid blindness. This paper proposes an algorithm to build an automated system to extract the above mentioned DR features which are the elemental and initial signs of diabetic retinopathy. Initial step in this algorithm is preprocessing of the original image. The next step in this features extraction algorithms is elimination of optic disc (OD) and blood vessels which have similar characteristic with these features. Blood vessels are segmented using Multi-Level Adaptive Thresholding. OD is segmented using morphological operations. Feature extraction and classification is achieved by using deep Bag of Feature (BoF) model which uses Speeded Up Robust Features Our method achieved 100% acuuracy in DRIVE database and over 90% accuracy for e-OPTHA database. Thus, the proposed methodology represents a track towards precise and highly automated DR diagnosis on a large substantial scale along with better sensitivity and specificity.

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Predictors of Early Neurologic Deterioration in Acute Pontine Infarction

Stroke ◽

10.1161/strokeaha.119.027239 ◽

2020 ◽

Vol 51 (2) ◽

pp. 637-640 ◽

Cited By ~ 4

Author(s):

Haiyan Li ◽

Yongqiang Dai ◽

Haotian Wu ◽

Lingyun Luo ◽

Lei Wei ◽

...

Keyword(s):

Characteristic Curve ◽

Area Under The Curve ◽

National Institutes Of Health ◽

Maximum Length ◽

Early Neurological Deterioration ◽

Pontine Infarction ◽

Adjusted Logistic Regression ◽

Sensitivity Specificity ◽

The Relationship ◽

Neurological Progression

Background and Purpose— The relationship between infarct dimensions and neurological progression in patients with acute pontine infarctions remains unclear. This study aimed to investigate the morphometric predictive value of magnetic resonance imaging for early neurological deterioration (END) in acute pontine infarction. Methods— We included all patients admitted to our department having an acute ischemic stroke in the pons. The ventrodorsal length multiplied by thickness was measured as parameters of infarct size. END was defined as an incremental increase in the National Institutes of Health Stroke Scale score by ≥1 point in motor power, or ≥2 points in the total score within the first week after admission. Results— We enrolled 407 patients, and 114 (28.0%) patients were diagnosed with END. Adjusted logistic regression analyses showed the maximum length multiplied by thickness was independently associated with END (odds ratio, 4.580 [95% CI, 2.909–7.210]). The sensitivity, specificity, and area under the curve were 77.2%, 79.2%, and 0.843, respectively, in the receiver operating characteristic curve analysis of maximum length multiplied by thickness for predicting END. Conclusions— These results suggest that the maximum length multiplied by thickness may be a possible predictor in the evaluation of progression with isolated acute pontine infarction. The extent of the pontine infarction along the conduction tract may contribute to deterioration.

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