Allergic rhinitis (AR) is the most common chronic pediatric disorder. The International Study for Asthma and Allergies in Childhood phase III found that the global average of current rhinoconjunctivitis symptoms in the 13-14 year age-group was 14.6% and the average prevalence of rhinoconjunctivitis symptoms in the 6-7 year age-group was 8.5%. In addition to classical symptoms, AR is associated with a multidimensional impact on the health related quality of life in children. AR affects the quality of sleep in children and frequently leads to day-time fatigue as well as sleepiness. It is also thought to be a risk factor for sleep disordered breathing. AR results in increased school absenteeism and distraction during class hours. These children are often embarrassed in school and have decreased social interaction which significantly hampers the process of learning and school performance. All these aspects upset the family too. Multiple co-morbidities like sinusitis, asthma, conjunctivitis, eczema, eustachian tube dysfunction and otitis media are generally associated with AR. These mostly remain undiagnosed and untreated adding to the morbidity. To compound the problems, medications have bothersome side effects which cause the children to resist therapy. Children customarily do not complain while parents and health care professionals, more often than not, fail to accord the attention that this not so trivial disease deserves. AR, especially in developing countries, continues to remain a neglected disorder.
Absenteeism ; Asthma ; Child ; Conjunctivitis ; Delivery of Health Care ; Developing Countries ; Eczema ; Eustachian Tube ; Fatigue ; Humans ; Hypersensitivity ; Interpersonal Relations ; Learning ; Learning Disorders ; Otitis Media ; Parents ; Prevalence ; Quality of Life ; Rhinitis, Allergic ; Risk Factors ; Sinusitis ; Sleep Apnea Syndromes

PURPOSE: To design software with a novel algorithm, which analyzes the tortuosity and vascular dilatation in fundal images of retinopathy of prematurity (ROP) patients with an acceptable accuracy for detecting plus disease. METHODS: Eighty-seven well-focused fundal images taken with RetCam were classified to three groups of plus, non-plus, and pre-plus by agreement between three ROP experts. Automated algorithms in this study were designed based on two methods: the curvature measure and distance transform for assessment of tortuosity and vascular dilatation, respectively as two major parameters of plus disease detection. RESULTS: Thirty-eight plus, 12 pre-plus, and 37 non-plus images, which were classified by three experts, were tested by an automated algorithm and software evaluated the correct grouping of images in comparison to expert voting with three different classifiers, k-nearest neighbor, support vector machine and multilayer perceptron network. The plus, pre-plus, and non-plus images were analyzed with 72.3%, 83.7%, and 84.4% accuracy, respectively. CONCLUSIONS: The new automated algorithm used in this pilot scheme for diagnosis and screening of patients with plus ROP has acceptable accuracy. With more improvements, it may become particularly useful, especially in centers without a skilled person in the ROP field.
Diagnosis ; Dilatation ; Humans ; Mass Screening ; Neural Networks (Computer) ; Politics ; Retinopathy of Prematurity ; Support Vector Machine ; Telemedicine