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

项目摘要 脓毒症是一种严重的疾病，通常由细菌病原体感染引起，导致器官损伤 甚至是死亡尽管多年来医学取得了许多进步，但这种情况仍然影响着数百万人 在发达国家和发展中国家。在美国，败血症每年影响170万人，并导致超过265，000人死亡。 发展中国家的败血症死亡率要高得多。在人口统计学方面，败血症影响 所有年龄和种族的人，但在年龄极端（婴儿和老年人）和患者中最明显 由于其他疾病或免疫系统弱化疗法， 例如，在一个实施例中，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，血液培养可能需要24-48小时才能获得结果。在此期间，患者 如果治疗不当，可能会受到不可逆的伤害。不幸的是，精确有效的 抗生素治疗需要了解引起脓毒症的病原体。很长一段时间才得到答案， 血培养通常表现出惊人的高假阴性（未能检测到引起败血症的病原体）， 通常不能精确地识别引起败血症的病原体。 因此，已经有几项努力，旨在检测和确定广泛的潜在病原体 导致败血症并避免了血培养的需要。然而，最近提出的许多方法 用于检测和诊断脓毒症表现出一个或多个以下缺点：（i）它们缺乏高灵敏度 （检测病原体的能力）;（ii）它们不能从单个SAM中准确地识别广泛的病原体- （iii）花费（相对）长时间;（iv）需要大量血液;或（v）不能实时使用 监测脓毒症（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）。 抗微生物处理）。 我们提出了一种新的脓毒症检测方法，结合低通量成像显微镜（高通量 用于对数百万个微观颗粒进行成像的技术）和基于深度学习的图像分析（技术 在面部识别和自动驾驶汽车中利用）来克服上述限制。的方法 已经证明能够在不到20分钟的时间内检测到低浓度小鼠血液中的多种细菌物种。 1小时的处理时间，仅需50 L血液。在这项提案中，我们的目标之一是优化我们的 接近并量化人体血液中检测的准确度和限度。我们正在申请专利的方法 也被授权给一个主要的制造商，低成像显微镜。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结合，旨在提供一种紧凑的自 包含的设备，可以部署在世界各地的许多医院。我们平台的实施 应该对医院所有区域的抗菌治疗产生重大影响。

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.