Improving placenta imaging in women living with HIV

改善艾滋病毒感染女性的胎盘成像

• 批准号: 10852602
• 负责人: Alison D Gernand
• 金额: $ 6.1万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10852602
• 关键词: Address; Algorithms; Artificial Intelligence; Assessment tool; Birth; Birth Weight; Caring; Characteristics; Chicago; Child; Chronic Disease; Clinical; Clinical Data; Computational Technique; Computer Vision Systems; Computer software; Data; Data Set; Devices; Diagnosis; Ethnic Origin; Event; Fetal Development; Fetus; Future; Gestational Age; Glean; Goals; HIV; Health; Healthcare; Hemorrhage; Hospitals; Image; Income; Infant; Infection; Kidney; Knowledge; Life; Lighting; Link; Liver; Low income; Lung; Maternal and Child Health; Measures; Medical; Medical Informatics; Membrane; Modeling; Morbidity - disease rate; Morphology; Mothers; Newborn Infant; Organ; Outcome; Pathologist; Pathology; Pathology Report; Patients; Photography; Placenta; Placenta Diseases; Placentation; Population; Pregnancy; Pregnancy Complications; Process; Protocols documentation; Public Health; Race; Research; Research Personnel; Resources; Retained Placenta; Risk; Sensitivity and Specificity; Sepsis; Shapes; Site; Software Validation; Standardization; Technology; Testing; Time; Training; Uganda; Umbilical cord structure; Variant; Visual; Woman; Work; adverse outcome; clinical care; clinically significant; cost; data mining; design; digital; digital imaging; health of the mother; improved; intraamniotic infection; mathematical model; mortality; neglect; novel; overtreatment; prototype; screening; sex; software development; tool

Development of Software to Rapidly Assess Placenta Images at Birth Project Summary The placenta is a window into the events of pregnancy and the health of the mother and baby, yet only about 20% of placentas in the US are assessed by pathology exams and placental data is often neglected in pregnancy research. Since both the mother and fetus contribute to and modulate placental development and function, data from placental examination may inform short- and long-term clinical care of both mother and child. Placental pathology remains under-used due to the time, cost, expertise, and facilities needed, even in high-resource settings. Placental assessment can and should be more accessible to pathologists, clinicians, and researchers, and assessment at birth can more readily aid clinical decisions and relate findings to patients. Prior work has used photographic images to measure characteristics such as shape and cord coiling and related these characteristics to placental diagnoses and outcomes of clinical importance. This project aims to leverage the simplicity and low cost of digital photographs and the computational and decision power of recent advances in artificial intelligence (AI) to create software for comprehensive placental assessment from images of gross placentas. The software could address the need for widespread, simple placenta assessment, particularly when information is needed urgently, pathologists are not highly trained for placental pathology, or where resources only allow a small fraction of placentas to be reviewed. The investigative team, with extensive expertise in placental pathology and research, clinical care, medical informatics/AI, and image understanding, has developed an initial prototype with promising results for predicting several clinically impactful diagnoses. Our preliminary data demonstrates that extensive data can be collected from placental photos and that computational techniques allow the connection of abstracted data to identify placental disease. The goal of this proposal is to develop and validate software to assess placentas from digital photographs in any delivery setting. An extensive, first-of-its-kind dataset will be created from three large hospitals including images and expert pathology reports from pregnancies with abnormal and healthy outcomes (n>50,000). These sites include a range of characteristics across income, race/ethnicity, health risks, and hospital resources. The resulting software will glean visual characteristics from the disc, cord, and membranes and accurately identify specific features (e.g., shape) and diagnoses (e.g., chorioamnionitis). The immediate information could impact clinical care before hospital discharge, and ease-of-use will allow inclusion in pregnancy research. This software has the ability to strengthen traditional pathology exams by standardizing and enhancing the data collected, providing better information to pathologists. With such huge advances in technology, placental assessment at birth can no longer be viewed as nonessential or too difficult. When fully developed and validated clinically in a range of birth settings, this software could have the power to impact the care of millions of mothers and children around the world.

开发软件以快速评估出生时胎盘图像 项目摘要 胎盘是进入怀孕事件和母亲和婴儿健康的窗口，但仅有关 美国有20％的胎盘通过病理考试评估，并且胎盘数据经常被忽略 怀孕研究。由于母亲和胎儿都为胎盘发育做出贡献，并 功能，胎盘检查数据可能会为母亲和长期临床护理提供信息 孩子。由于时间，成本，专业知识和设施，胎盘病理的使用量仍然不足 高资源设置。胎盘评估可以而且应该更容易被病理学家，临床医生， 研究人员以及出生时的评估可以更容易地帮助临床决策，并将发现与 患者。先前的工作使用了摄影图像来测量特征，例如形状和绳索盘绕 并将这些特征与胎盘诊断和临床重要性的结果有关。这个项目 旨在利用数码照片的简单性和低成本以及计算和决策 人工智能（AI）最新进步的力量创建用于综合胎盘的软件 评估总胎盘的图像。该软件可以解决广泛，简单的需求 胎盘评估，尤其是在紧急需要信息时，病理学家没有受过高度训练 胎盘病理学，或者资源仅允许胎盘的一小部分进行审查。这 调查团队，在胎盘病理学和研究，临床护理，医疗方面具有广泛的专业知识 信息学/AI和图像理解已开发出最初的原型，并具有令人鼓舞的结果 预测几种临床上有影响力的诊断。我们的初步数据表明，大量数据可以 从胎盘照片中收集，计算技术允许连接抽象的数据 确定胎盘疾病。该建议的目的是开发和验证软件以评估 在任何交付环境中的数码照片中的胎盘。广泛的，首先的数据集将是 由三家大型医院创建，包括图像和专家病理学报告 异常和健康的结果（n> 50,000）。这些站点包括跨收入的一系列特征， 种族/种族，健康风险和医院资源。由此产生的软件将收集视觉特征 从椎间盘，绳索和膜上，准确地识别特定特征（例如形状）和诊断 （例如，绒毛膜膜炎）。即时信息可能会在出院前影响临床护理，并且 易用性将允许纳入妊娠研究。该软件具有加强传统的能力 病理考试通过标准化和增强收集的数据，提供更好的信息 病理学家。随着技术的如此巨大的进步，不再可以查看出生时的胎盘评估 不必要或太困难。当在一系列出生环境中充分开发和临床验证时， 软件可以有权影响全球数百万母亲和儿童的照顾。