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.

开发快速评估出生时胎盘图像的软件 项目概要 胎盘是了解怀孕事件以及母亲和婴儿健康状况的窗口，但仅约 在美国，20% 的胎盘通过病理学检查进行评估，而胎盘数据经常被忽视 妊娠研究。由于母亲和胎儿都有助于并调节胎盘发育 功能，胎盘检查数据可以为母亲和母亲的短期和长期临床护理提供信息 孩子。由于所需的时间、成本、专业知识和设施，胎盘病理学仍然没有得到充分利用，即使在 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、 和研究人员，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。先前的工作使用摄影图像来测量形状和绳索卷绕等特征 并将这些特征与具有临床重要性的胎盘诊断和结果相关联。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) 最新进展的力量可创建用于全面胎盘分析的软件 根据胎盘大体图像进行评估。该软件可以满足广泛、简单的需求 胎盘评估，特别是当迫切需要信息时，病理学家没有接受过严格的胎盘评估培训 胎盘病理学，或资源仅允许检查一小部分胎盘的情况。这 研究团队在胎盘病理学和研究、临床护理、医疗方面拥有丰富的专业知识 信息学/人工智能和图像理解已经开发出一个初步原型，并取得了有希望的结果 预测一些有临床影响的诊断。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数据 以确定胎盘疾病。该提案的目标是开发和验证软件来评估 任何分娩环境下数码照片中的胎盘。一个广泛的、史无前例的数据集将是 由三个大医院创建，包括怀孕时的图像和专家病理报告 异常和健康结果 (n>50,000)。这些网站包括一系列跨收入的特征， 种族/民族、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、脊髓和膜中获取信息，并准确识别特定特征（例如形状）和诊断 （例如，绒毛膜羊膜炎）。即时信息可能会影响出院前的临床护理，并且 易于使用将允许纳入妊娠研究。该软件具有增强传统功能的能力 通过标准化和增强收集的数据，为病理学检查提供更好的信息 病理学家。随着技术的巨大进步，出生时的胎盘评估已不再可见 不必要或太困难。当在一系列分娩环境中得到充分开发和临床验证后， 软件可以影响世界各地数百万母亲和儿童的护理。