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.

项目概要 脓毒症是由感染（通常是细菌病原体）引起的严重疾病，导致器官损伤 尽管多年来医学取得了巨大进步，但这种疾病仍然影响着数百万人。 在美国，败血症每年影响 170 万人并导致超过 265,000 人死亡。 就人口统计而言，脓毒症影响发展中国家的脓毒症死亡率要高得多。 所有年龄和种族的人，但在极端年龄（婴儿和老年人）和患者中最为明显 由于其他疾病或免疫系统削弱疗法，其免疫系统已经处于紧张状态， 例如，正在接受化疗的癌症患者。 血培养是目前用于检测和诊断脓毒症根本原因的默认技术。 然而，血培养可能需要长达 24-48 小时才能获得结果，在此期间，患者。 不幸的是，如果治疗不当，可能会因病情而遭受不可逆转的伤害。 抗生素治疗需要了解引起败血症的病原体，才能得到答案。 血培养常常表现出惊人的高假阴性（未能检测到病原体败血症）并且 通常不能准确识别引起败血症的病原体。 因此，已经做出了一些努力来检测和识别广泛的潜在病原体 然而，许多最近提出的方法可能会导致败血症并避免进行血培养。 用于检测和诊断败血症表现出以下一个或多个缺陷：（i）它们缺乏高灵敏度 （检测病原体的能力）；（ii）他们无法从单一样本中准确识别多种病原体。 ple；（iii）需要（相对）长时间；（iv）需要大量血液；或（v）不能实时使用； 脓毒症监测（检测已知引起脓毒症的病原体或量化患者对脓毒症的反应） 抗菌处理）。 我们提出了一种新的脓毒症检测方法，结合流式成像显微镜（一种高通量 对数百万微观粒子进行成像的技术）和基于深度学习的图像分析（技术 用于面部识别和自动驾驶汽车）来克服上述限制。 已被证明能够在不到 5 分钟的时间内检测低浓度小鼠血液中的多种细菌 只需 50 L 血液即可处理 1 小时 在本提案中，我们的目标之一是优化我们的流程。 我们正在申请专利的方法已经接近并量化了人类血液中检测的准确性和限度。 还获得了流成像显微镜主要制造商的许可。这项研究的另一个目的是开始。 将我们的技术与现有的商业仪器相集成，旨在提供紧凑的自 包含可在全球众多医院部署的设备。 应该会对医院所有领域的抗菌治疗产生重大影响。