This technology relates to a machinelearning-based digital polymerase chain reaction (dPCR) method and system foraccurately analyzing DNA concentration in samples. In particular, it is atechnology designed to simultaneously enhance performance, durability,stability, and applicability based on core materials, structures, processes, ordevice configurations related to the machine learning-based digital polymerasechain reaction analysis method and system.
Itaims to resolve the limitations of existing dPCR methods, such as longdetection times, high error rates, and the need for expensive equipment andcomplex operation. Accordingly, this technology applies a method for analyzingDNA concentration in dPCR using microdroplet detection models such as deepneural networks and masked R-CNN as a core means, and proposes a technicalconcept that implements the use of microdroplet detection models to accuratelydetect and analyze DNA concentration in dPCR.
Accordingly, the present invention isexpected to improve the accuracy and sensitivity of dPCR analysis whilereducing detection time and costs, and can simultaneously enhancereproducibility, scalability, and process suitability in real-world usage environments.Furthermore, it can be utilized as a high-performance material, device,battery, sensor, apparatus, or manufacturing process in related industries,making it advantageous in terms of subsequent commercialization anddemonstration deployment.
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