首页|Study Results from Shri Ramdeobaba College of Engineering & Manage ment Update Understanding of Machine Learning (Machine-learning-assisted Blood P arameter Sensing Platform for Rapid Next Generation Biomedical and Healthcare Ap plications)
Study Results from Shri Ramdeobaba College of Engineering & Manage ment Update Understanding of Machine Learning (Machine-learning-assisted Blood P arameter Sensing Platform for Rapid Next Generation Biomedical and Healthcare Ap plications)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting originating from Maharashtra, India , by NewsRx correspondents, research stated, "The pursuit of rapid diagnosis has resulted in considerable advances in blood parameter sensing technologies. As a dvances in technology, there may be challenges in equitable access for all indiv iduals due to economic constraints, advanced expertise, limited accessibility in particular places, or insufficient infrastructure." Financial support for this research came from RCOEM YFRF Scheme. Our news editors obtained a quote from the research from the Shri Ramdeobaba Col lege of Engineering & Management, "Hence, simple, cost efficient, benchtop biochemical blood-sensing platform was developed for detecting crucial blood parameters for multiple disease diagnosis. Colorimetric and image processi ng techniques is used to evaluate color intensity. CMOS image sensor is utilized to capture images to calculate optical density for sensing. The platform is ass essed with blood serum samples, including Albumin, Gamma Glutamyl Transferase, A lpha Amylase, Alkaline Phosphatase, Bilirubin, and Total Protein within clinical ly relevant limits. The platform had excellent Limits of Detection (LOD) for the se parameters, which are critical for diagnosing liver and kidney-related diseas es (0.27 g dl-1, 0.86 IU l-1, 1.24 IU l-1, 0.97 IU l-1, 0.24 mg dl-1, 0.35 g dl- 1, respectively). Machine learning (ML) algorithms were used to estimate targete d blood parameter concentrations from optical density readings, with 98.48% accuracy and reduced incubation time by nearly 80%. The proposed pl atform is compared to commercial analyzers, which demonstrate excellent accuracy and reproducibility with remarkable precision (0.03 to 0.71%CV)."
MaharashtraIndiaAsiaCyborgsEmerg ing TechnologiesMachine LearningShri Ramdeobaba College of Engineering & Management
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