This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: The Ohio State University. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Innovations by Jonathan Kadowaki in Malaria Detection
Introduction
Jonathan Kadowaki is an innovative inventor based in Chardon, Ohio. He has made significant contributions to the field of medical diagnostics, particularly in the detection of malaria parasites in blood samples. His work utilizes advanced technology to improve the accuracy and efficiency of malaria detection.
Latest Patents
Kadowaki holds a patent for "Methods and apparatus for making a determination about a presence or an absence of a parasite in a blood sample." This patent describes methods and apparatus that determine whether a blood sample is infected with a malaria parasite. The determination is made using a trained machine-learning algorithm. A microfluidic chip is employed to concentrate red blood cells infected with the parasites from uninfected cells. The blood sample is stained to differentially highlight infected and uninfected red blood cells. The microfluidic chip is then inserted into an optical subsystem that magnifies the image created from transmitted light microscopy. A camera captures the magnified image, and the trained machine-learning algorithm assesses whether the sample contains the parasite. Notably, the algorithm can be executed on a portable computing device, such as a smartphone.
Career Highlights
Kadowaki is affiliated with The Ohio State University, where he continues to advance his research and innovations. His work has the potential to significantly impact malaria diagnosis and treatment, especially in regions where the disease is prevalent.
Collaborations
Kadowaki collaborates with Vishwanath Subramaniam, enhancing the research and development of his innovative methods for malaria detection.
Conclusion
Jonathan Kadowaki's contributions to malaria detection through innovative technology exemplify the intersection of medicine and machine learning. His work not only advances scientific understanding but also has the potential to save lives through improved diagnostic methods.
