AI Tracking Solutions LLC — Department of Health and Human Services STTR Phase I: 400

Description

Abstract Particle tracking PT is a powerful biophysical tool for elucidating molecular interactions transport phenomena and rheological properties in complex biological environments Unfortunately PT remains a niche tool in life and physical sciences with a limited user base in large part due to significant time and technical constraints in extracting accurate time variant positional data from recorded movies These constraints are exacerbated in experiments with low signal to noise ratios or substantial heterogeneity as frequently encountered with nanoparticles and pathogens in biological fluids Currently available software that attempts to automate the movie analysis process rely almost exclusively on assigning static image filters based on specific intensity pixel size and signal to noise ratio thresholds Unfortunately when applied to actual experimental data with substantial spatial and temporal heterogeneity the current software generally produces substantial numbers of false positives i e tracking artifacts or false negatives i e missing actual traces and frequently both Frequent user intervention is thus required to ensure accurate tracking even when using sophisticated tracking software markedly reducing experimental throughput and resulting in substantial user to user variations in analyzed data The time required for accurate particle tracking analysis makes PT experiments exceedingly expensive compared to other commonly used experimental techniques in life sciences These same tracking analysis limitations have effectively precluded investigators from undertaking more sophisticated D PT even though the microscopy capability to obtain such movies is readily available and critical scientific insights can be gained from D PT To circumvent the challenges with currently available particle tracking software we have developed a new approach for particle identification and tracking based on machine learning and convolutional neural networks CNN CNN is a type of feed forward artificial neural network designed to process information in a layered network of connections that mimics the organization of real neural networks in the mammalian retina and visual cortex Unlike most CNN imaging models that are trained to make predictions on static images we have trained our CNN to input adjacent frames so that each prediction includes information from the past and future thus effectively performing convolutions in both space and time to infer particle locations Similar principles of image analysis are now being harnessed by developers of autonomous vehicle technologies to distinguish the motions of different objects on the road We have applied our CNN tracking algorithm to a wide range of D movies capturing dynamic motions of nanoparticles viruses and highly motile bacteria achieving at least fold time savings with virtually no need for human intervention while maintaining robust tracking performance i e low false positive and low false negative rates In this STTR proposal we seek to focus on further optimization and testing of our neural network tracking platform for D PT including the use of cloud computing Aim and extending our neural network tracker to enable accurate D PT Aim Our vision is to popularize PT as a research tool among researchers by minimizing the time and labor costs associated with PT analysis Narrative Particle tracking is a powerful biophysical tool in life and physical sciences but unfortunately its application has been strongly limited by inefficiencies in accurately extracting particle traces from raw movies Unlike conventional particle tracking methods we have combined artificial intelligence and machine learning to create a computational neural network that can recognize objects in much the same way as the human eye and which consistently provided superior and truly automated tracking performance compared to current alternatives This STTR will establish the feasibility of using our computational neural network for robust D and D particle tracking analysis