ABSTRACT
This paper presents a low cost, robust, portable and automated cataract detection system which can detect the presence of cataract from the colored digital eye images and grade their severity. Ophthalmologists detect cataract through visual screening using ophthalmoscope and slit lamps. Conventionally a patient has to visit an ophthalmologist for eye screening and treatment follows the course. Developing countries lack the proper health infrastructure and face huge scarcity of trained medical professionals as well as technicians. The condition is not very satisfactory with the rural and remote areas of developed nations. To bridge this barrier between the patient and the availability of resources, current work focuses on the development of portable low-cost, robust cataract screening and grading system. Similar works use fundus and retinal images which use costly imaging modules and image based detection algorithms which use much complex neural network models. Current work derives its benefit from the advancements in digital image processing techniques. A set of preprocessing has been done on the colored eye image and later texture information in form of mean intensity, uniformity, standard deviation and randomness has been calculated and mapped with the diagnostic opinion of doctor for cataract screening of over 200 patients. For different grades of cataract severity edge pixel count was calculated as per doctor’s opinion and later these data are used for calculating the thresholds using hybrid k-means algorithm, for giving a decision on the presence of cataract and grade its severity. Low value of uniformity and high value of other texture parameters confirm the presence of cataract as clouding in eye lens causes the uniformity function to take lower value due to presence of coarse texture. Higher the edge pixel count value, this confirms the presence of starting of cataract as solidified regions in lens are nonuniform. Lower value corresponds to fully solidified region or matured cataract. Proposed algorithm was initially developed on MATLAB, and tested on over 300 patients in an eye camp. The system has shown more than 98% accuracy in detection and grading of cataract. Later a cloud based system was developed with 3D printed image acquisition module to manifest an automated, portable and efficient cataract detection system for Tele-Ophthalmology. The proposed system uses a very simple and efficient technique by mapping the diagnostic opinion of the doctor as well, giving very promising results which suggest its potential use in teleophthalmology applications to reduce the cost of delivering eye care services and increasing its reach effectively. Developed system is simple in design and easy to operate and suitable for mass screening of cataracts. Due to non-invasive and non-mydriatic and mountable nature of device, in person screening is not required. Hence, social distancing norms are easy to follow and device is very useful in COVID-19 like situation.
ABSTRACT
This study aimed to determine the patient demographics, risk factors, which include comorbidities, medications used to treat COVID-19, and presenting symptoms and signs, and the management outcome of COVID-19-associated invasive fungal sinusitis. A retrospective, propensity score-matched, comparative study was conducted at a tertiary care center, involving 124 patients with invasive fungal sinusitis admitted between April 2021 and September 2021, suffering from or having a history of COVID-19 infection. Among the 124 patients, 87 were male, and 37 were female. A total of 72.6% of patients received steroids, while 73.4% received antibiotics, and 55.6% received oxygen during COVID-19 management. The most common comorbidities were diabetes mellitus (83.9%) and hypertension (30.6%). A total of 92.2% had mucor, 16.9% had aspergillus, 12.9% had both, and one patient had hyalohyphomycosis on fungal smear and culture. The comparative study showed the significant role of serum ferritin, glycemic control, steroid use, and duration in COVID-19-associated invasive fungal disease (p < 0.001). Headache and facial pain (68, 54.8%) were the most common symptoms. The most involved sinonasal site was the maxillary sinus (90, 72.6%). The overall survival rate at the three-month follow-up was 79.9%. COVID-19-related aggressive inflammatory response, uncontrolled glycemic level, and rampant use of steroids are the most important predisposing factors in developing COVID-19-associated invasive fungal sinusitis.