Browsing by Author "Senanayake, K"

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Open Access
Affordable and Reliable Video Laryngoscope with Wireless Connectivity
(SLIIT, 2022-02-11) Dassanayake, I; Senanayake, K; Katakumbura, I; Senaviratne, L
Along with the pandemic situation across the world, the resilience of using ordinary medical devices is limited. Also the demand for medical devices which are used to diagnose COVID-related diseases rapidly increased. Taking all these facts into consideration, a new laryngoscope device was developed with exciting functionalities. The proposed device is affordable and comes with remote monitoring capabilities. An ordinary video laryngoscope uses an expensive camera module to capture video streams, and the proposed device uses ordinary USB camera modules which are cheap and affordable. Therefore, it is even possible to replace/discard camera modules each time after investigating COVID-related patients. The proposed USB camera and Laryngoscopic blades can be easily replaced for a very affordable cost. In the proposed system, a real-time video stream can be remotely monitored in multiple displays, including personal mobile devices. Since the live video footage can be streamed across the world, expertise in the field can monitor and consult promptly. This device broadcasts video wirelessly through LAN (local area) and WAN (wide area) networks in real-time. So far, the device transmits live video streams wirelessly with less than 250ms latency on LAN networks, and 500ms to 1000ms latency in WAN networks. An inbuilt rechargeable power supply will power up the device for ~ 45min in continuous use.
Embargo
Ai-Based Urine Microscopy Image Analysis for Predicting Urinary Tract and Renal Diseases
(Institute of Electrical and Electronics Engineers, 2026-05-29) Senanayake, K; Panagoda, P; Dharmapriya, S; Chathurya, R; Wijendra, D; De Silva, H; Jayawardana, D
The manual microscopic examination of urine is a crucial step in diagnosis of urinary tract and renal diseases. However, the process is time-consuming and operator dependent. Most of the existing automated urinalysis techniques only consider the microscopic examination of individual components or the black-box-based prediction models. There is a lack of a comprehensive framework that incorporates the microscopic examination of the microscopic components with the clinical diagnostic logic. In this regard, the present work proposes an artificial intelligence-based urinalysis system for the microscopic examination of the components in the urine sample to generate diagnostic outcomes. In the proposed system, the microscopic components like white blood cells, red blood cells, bacteria, yeast, crystals, and casts are detected and analyzed to generate the diagnostic outcomes for the diagnosis of urinary tract infection, kidney stone risk, hematuria causes, and casts-related renal diseases. In the proposed system, efficient lightweight models ensure precision and effectiveness in identifying various biological entities. White blood cells are detected with a mAP@0.5 score of 0.96, yeast with over 0.94, and crystals with more than 0.91, yielding a classification accuracy of 99.41% for crystals. The system detects microscopic elements like casts with a mAP@0.5 score of 0.80. The system also incorporates auxiliary clinical data to enhance diagnostic results for various diseases.