A New Tool to Rapidly Diagnose Sepsis using Flow Imaging Microscopy and Machine Learning

使用流成像显微镜和机器学习快速诊断脓毒症的新工具

• 批准号: 10078833
• 负责人: Christopher Peter Calderon
• 金额: $ 22.48万
• 依托单位: URSA ANALYTICS, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2023-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10078833
• 关键词: Address; Affect; Age; Agreement; Algorithms; Antibiotic Therapy; Antibiotics; Area; Automobile Driving; Bacteria; Bedside Testings; Blood; Blood Cells; Blood Volume; Blood specimen; Cancer Patient; Cells; Cessation of life; Classification Scheme; Colony-forming units; Computer Analysis; Computer Systems; Culture Techniques; DNA; Detection; Developed Countries; Developing Countries; Devices; Diagnosis; Elderly; Etiology; Exhibits; Face; Failure; Flow Cytometry; Goals; Growth; Guidelines; Hospital Costs; Hospitals; Hour; Human; Image; Image Analysis; Imaging Techniques; Imaging technology; Immune system; Infant; Infant Mortality; Infection; Intention; Knowledge; Legal patent; Length of Stay; Letters; Licensing; Liquid substance; Machine Learning; Manufacturer Name; Medicine; Memory; Methods; Microbe; Microfluidic Microchips; Microscopic; Microscopy; Mus; Newborn Infant; Optics; Organ; Organism; Pathogenicity; Patients; Plant Roots; Population; Premature Birth; Preparation; Race; Reporting; Research; Sampling; Secure; Sensitivity and Specificity; Sepsis; Source; Speed; Techniques; Technology; Testing; Time; Treatment Effectiveness; age group; antimicrobial; base; chemotherapy; clinically relevant; data exchange; deep learning; demographics; detector; digital imaging; effective therapy; experience; human DNA; human old age (65+); image processing; improved; infant death; instrument; intrapartum; microscopic imaging; mortality; multi-drug resistant pathogen; nano; nanoproducts; neonatal sepsis; older patient; optic flow; particle; pathogen; pathogenic bacteria; patient response; point of care; preterm newborn; processing speed; prototype; rapid diagnosis; real time monitoring; side effect; tool

Project Summary Sepsis is a serious condition induced by an infection, often by a bacterial pathogen, leading to organ damage or even death. Despite numerous advances in medicine over the years, the condition still affects millions of people in both developed and developing countries. In the US, sepsis affects 1.7M and kills over 265,000 people annually. Sepsis mortality rates in developing countries are substantially higher. In terms of demographics, sepsis affects humans of all age and race, but it is most pronounced at the age extremes (infants and the elderly) and patients whose immune system is already under strain due to other illnesses or immune system-weakening therapies, e.g., cancer patients undergoing chemotherapy. Blood cultures are currently the default technique used in detecting and diagnosing the root cause of sepsis. However, blood-cultures can take upwards of 24-48 hours in order to obtain results. In that time, the patient can experience irreversible harm due to the condition if not treated properly. Unfortunately, precise and effective antibiotic treatment requires knowledge of the pathogen causing sepsis. Beyond a long time to get an answer, blood cultures often exhibit alarmingly high false negatives (failure to detect a pathogen causing sepsis) and typically do not precisely identify the pathogen causing sepsis. Hence there have been several efforts aimed at detecting and identifying the broad range of potential pathogens causing sepsis and circumventing the need for blood cultures. However, many of the recently proposed methods for detecting and diagnosing sepsis exhibit one or more of the following drawbacks: (i) they lack high sensitivity (ability to detect a pathogen); (ii) they cannot accurately identify a broad range of pathogens from a single sam- ple; (iii) take a (relatively) long time; (iv) require a large volume of blood; or (v) cannot be used in the real-time monitoring of sepsis (either detecting pathogens known to cause sepsis or quantifying the patient's response to antimicrobial treatment). We are proposing a new sepsis detection method, combining flow imaging microscopy (a high-throughput technique for imaging millions of microscopic particles) and deep learning based image analysis (techniques leveraged in facial recognition and self-driving cars) to overcome the above mentioned limitations. The approach has proven capable of detecting a variety of bacterial species in low concentrations of mouse blood in less than 1 hour of processing time with as little as 50 L of blood. In this proposal, one of our aims is to optimize our approach and quantify the accuracy and limits of detection in human blood. Our patent pending approach has also been licensed to a major manufacturer of flow imaging microscopes. Another aim of this research is to begin integration of our technology with an existing commercial instrument with the intention of providing a compact self- contained device that can be deployed at numerous hospitals world-wide. The implementation of our platform should have a major impact on antimicrobial treatment in all areas of the hospital.

项目总结