Intelligent framework for diagnosis of frozen shoulder using cross sectional survey and case studies.

OBJECTIVES: Frozen shoulder is a disease in which shoulder becomes stiff. Accurate diagnosis of frozen shoulder is helpful in providing economical and effective treatment for patients. This research provides the classification of unstructured data using data mining techniques. Prediction results are validated by K-fold cross-validation method. It also provides accurate diagnosis of frozen shoulder using Naïve Bayesian and Random Forest models. At the end results are presented by performance measure techniques. METHODS: In this research, 145 respondents (patients) with a severe finding of frozen shoulder are included. They are selected on premise of (clinical) assessment confirmed after by MRI. This data is taken from the department of Orthopedics (Pakistan Institute of Medical Sciences Islamabad and Railway Hospital Rawalpindi) between September 2014 to November 2015. Frozen shoulder is categorized on the basis of MRI result. The predictor variables are taken from patient survey and patient reports, which consisted of 35+ variables. The outcome variable is coded into numeric system of "intact" and "no-intact". The outcome variable is assigned into numeric code, 1 for "intact" and 0 for "no-intact". "Intact" group is used as an indication that tissue is damaged badly and "no-intact" is classified as normal. Distribution of result is 110 patients for "Intact" group and 35 patients for "No-Intact" group (false positive rate was 24 %). In this research we have utilized two methods i.e. Naive Bayes and Random Forest. A statistics regression model (Logistic regression) to categorize frozen shoulder finding into "intact" and "no-intact" classes. In the end, we validated our results by Bayesian theorem. This gives a rough estimate about the probability of frozen shoulder. RESULTS: In this research, our anticipated and predictive procedures gave better outcome as compared to statistical techniques. The specificity and sensitivity ratio of predicting a frozen shoulder are better in the Naïve Bayes as compared to Random Forest. In end the likelihood ratio results are used with Bayesian theorem for final evaluation of the results, from this we conclude predictive model is valid model for classification of frozen shoulder. CONCLUSIONS: We have used three predictive models in our study to classify frozen shoulder. Then we validated our predictive results by Bayesian theorem to give a rough estimate about the probability of occurrence of disease or not. This enhances the clinical decision making regarding frozen shoulder.

Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images.

Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively.

Detection and classification of retinal lesions for grading of diabetic retinopathy.

Diabetic Retinopathy (DR) is an eye abnormality in which the human retina is affected due to an increasing amount of insulin in blood. The early detection and diagnosis of DR is vital to save the vision of diabetes patients. The early signs of DR which appear on the surface of the retina are microaneurysms, haemorrhages, and exudates. In this paper, we propose a system consisting of a novel hybrid classifier for the detection of retinal lesions. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. The candidate lesion detection phase extracts, using filter banks, all regions which may possibly have any type of lesion. A feature set based on different descriptors, such as shape, intensity, and statistics, is formulated for each possible candidate region: this further helps in classifying that region. This paper presents an extension of the m-Mediods based modeling approach, and combines it with a Gaussian Mixture Model in an ensemble to form a hybrid classifier to improve the accuracy of the classification. The proposed system is assessed using standard fundus image databases with the help of performance parameters, such as, sensitivity, specificity, accuracy, and the Receiver Operating Characteristics curves for statistical analysis.

Detection of neovascularization in retinal images using multivariate m-Mediods based classifier.

Diabetic retinopathy is a progressive eye disease and one of the leading causes of blindness all over the world. New blood vessels (neovascularization) start growing at advance stage of diabetic retinopathy known as proliferative diabetic retinopathy. Early and accurate detection of proliferative diabetic retinopathy is very important and crucial for protection of patient's vision. Automated systems for detection of proliferative diabetic retinopathy should identify between normal and abnormal vessels present in digital retinal image. In this paper, we proposed a new method for detection of abnormal blood vessels and grading of proliferative diabetic retinopathy using multivariate m-Mediods based classifier. The system extracts the vascular pattern and optic disc using a multilayered thresholding technique and Hough transform respectively. It grades the fundus image in different categories of proliferative diabetic retinopathy using classification and optic disc coordinates. The proposed method is evaluated using publicly available retinal image databases and results show that the proposed system detects and grades proliferative diabetic retinopathy with high accuracy.