The AutoScope : an automated point-of-care urinalysis system
Author(s)Primas, Sidney R
Auto Scope : an automated point-of-care urinalysis system
Automated point-of-care urinalysis system
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Charles G. Sodini.
MetadataShow full item record
The goal of this project was to develop an automated, low-cost microscopic urinalysis system that could accurately detect red blood cells (RBCs), white blood cells (WBCs), and other particles in urine. The Autoscope is a proof-of-concept for this end-to-end automated microscopic urinalysis system. A similar system can eventually be applied to microscopic analysis of blood. Urinalysis is one of the most common diagnostic techniques in medicine. Over 200 million urine tests are ordered each year in the US, costing between $800 to $2,000 million in direct costs. 46% of all urinalysis tests include microscopic analysis, which involves identifying and counting each particle found in the urine. Microscopic urinalysis is a costly and complex process done in medical laboratories. An inexpensive and automated cell-counting system would (1) increase access to microscopic urinalysis and (2) shorten the turn-around time for physicians to make diagnostic decisions by permitting the test to be done at the point-of-care. The system we built - the AutoScope - has three parts: the image acquisition system, the segmentation system and the classification system. The image acquisition system used a reversed lens approach to achieve an end-to-end resolution of 5.86-8.29[mu]m for a total bill of materials cost of $57-$92. The automated particle segmentation and particle classification were each performed with different neural networks. We calculated the accuracy, sensitivity, and specificity of the Autoscope system with respect to urine solutions composed of RBCs, WBCs, and microbeads. The specificity and sensitivity was determined by generating 209 digital urine specimens modeled after urine received in medical labs. The Autoscope had a sensitivity of 88% and 91% and a specificity of 89% and 97% for RBCs and WBCs, respectively. Next, we determined the Autoscope's accuracy by fabricating 8 synthetic urine samples with RBCs, WBCs and microbeads. The reference results were confirmed through a medical laboratory. The AutoScope's counts and the reference counts were linearly correlated to each other (r2= 0.980) across all particles. The sensitivity, specificity, and R-squared values for the AutoScope are comparable (and mostly better) than the same metrics for the iQ-200, a $100,000-$150,000 state-of-the-art semi-automated urinalysis system.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 90-93).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.