Diabetic wounds and artificial intelligence: A mini-review.

Diabetic wound takes longer time to heal due to micro and macro-vascular ailment. This longer healing time can lead to infections and other health complications. Foot ulcers are one of the most common diabetic wounds. These are one of the leading cause of amputations. Medical science is continuously striving for improving quality of human life. A recent trend of amalgamation of knowledge, efforts and technological advancement of medical science experts and artificial intelligence researchers, has made tremendous success in diagnosis, prognosis and treatment of a variety of diseases. Diabetic wounds are no exception, as artificial intelligence experts are putting their research efforts to apply latest technological advancements in the field to help medical care personnel to deal with diabetic wounds in more effective manner. The presented study reviews the diagnostic and treatment research under the umbrella of Artificial Intelligence and computational science, for diabetic wound healing. Framework for diabetic wound assessment using artificial intelligence is presented. Moreover, this review is focused on existing and potential contribution of artificial intelligence to improve medical services for diabetic wound patients. The article also discusses the future directions for the betterment of the field that can lead to facilitate both, clinician and patients.

Outcome of Chronic Kidney Disease Patients on the Basis of Referral to Nephrologist: A One-Year Follow-up Study.

OBJECTIVE: To determine the outcome of chronic kidney disease (CKD) patients presenting for dialysis on the basis of referral to nephrologist. STUDY DESIGN: Observational study. PLACE AND DURATION OF STUDY: Nephrology Department of King Edward Medical University/Mayo Hospital, Lahore, from January 2014 to January 2016. METHODOLOGY: All patients who were presented in nephrology outpatients department and with the indication of dialysis were included in study. Patients who refused dialysis, and with acute kidney failure were excluded from the study. Proforma was designed for demographics, vital signs, volume status, and laboratory data (hemoglobin, urea, creatinine, albumin, bicarbonate etc.) of all the patients. On the basis of referral, patients were divided into two groups, i.e. early referral and late referral. Early referrals were those patients who were referred to a nephrologist more than three months before dialysis initiation. Late referrals were those patients who were referred to a nephrologist less than three months before dialysis initiation. Patients were followed up at one, three, six, and 12 months for outcome, i.e. still on dialysis or died. RESULTS: One hundred and seventy-six patients were enrolled in the study, and 141 were followed up to one year. Seventy- two (51.1%) patients were male, 69 (48.9%) were female and most (n=69, 48.9%) were in the middle age group. Major causes of end-stage renal disease (ESRD) were hypertension 70 (49.6%) and diabetes mellitus 66 (46.8%). Seventy-six (53.9%) patients were in fluid overload and acidotic (n=123, 87.2%). Twenty-seven (19.1%) patients were referred early and 114 (80.9%) were referred late. Overall mortality was 78 (55.3%) at one year. Factors affecting mortality were financial status and metabolic acidosis, but not referral. Temporary access for hemodialysis has 1.38 times more risk for mortality than the patients with permanent access. CONCLUSION: There is no difference on the outcome of dialysis patients on the basis referral to nephrologist. Factors affecting overall mortality in both groups were financial status, metabolic acidosis, and temporary access for dialysis. Most of the patients were referred late to the nephrologists.

Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD.

Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.

Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images.

Maculopathy is the excessive damage to macula that leads to blindness. It mostly occurs due to retinal edema (RE), central serous chorioretinopathy (CSCR), or age related macular degeneration (ARMD). Optical coherence tomography (OCT) imaging is the latest eye testing technique that can detect these syndromes in early stages. Many researchers have used OCT images to detect retinal abnormalities. However, to the best of our knowledge, no research that presents a fully automated system to detect all of these macular syndromes is reported. This paper presents the world's first ever decision support system to automatically detect RE, CSCR, and ARMD retinal pathologies and healthy retina from OCT images. The automated disease diagnosis in our proposed system is based on multilayered support vector machines (SVM) classifier trained on 40 labeled OCT scans (10 healthy, 10 RE, 10 CSCR, and 10 ARMD). After training, SVM forms an accurate decision about the type of retinal pathology using 9 extracted features. We have tested our proposed system on 2819 OCT scans (1437 healthy, 640 RE, and 742 CSCR) of 502 patients from two different datasets and our proposed system correctly diagnosed 2817/2819 subjects with the accuracy, sensitivity, and specificity ratings of 99.92%, 100%, and 99.86%, respectively.

Automated detection of glaucoma using structural and non structural features.

Glaucoma is a chronic disease often called "silent thief of sight" as it has no symptoms and if not detected at an early stage it may cause permanent blindness. Glaucoma progression precedes some structural changes in the retina which aid ophthalmologists to detect glaucoma at an early stage and stop its progression. Fundoscopy is among one of the biomedical imaging techniques to analyze the internal structure of retina. Our proposed technique provides a novel algorithm to detect glaucoma from digital fundus image using a hybrid feature set. This paper proposes a novel combination of structural (cup to disc ratio) and non-structural (texture and intensity) features to improve the accuracy of automated diagnosis of glaucoma. The proposed method introduces a suspect class in automated diagnosis in case of any conflict in decision from structural and non-structural features. The evaluation of proposed algorithm is performed using a local database containing fundus images from 100 patients. This system is designed to refer glaucoma cases from rural areas to specialists and the motivation behind introducing suspect class is to ensure high sensitivity of proposed system. The average sensitivity and specificity of proposed system are 100 and 87 % respectively.

Improved guided image fusion for magnetic resonance and computed tomography imaging.

Improved guided image fusion for magnetic resonance and computed tomography imaging is proposed. Existing guided filtering scheme uses Gaussian filter and two-level weight maps due to which the scheme has limited performance for images having noise. Different modifications in filter (based on linear minimum mean square error estimator) and weight maps (with different levels) are proposed to overcome these limitations. Simulation results based on visual and quantitative analysis show the significance of proposed scheme.