Development and Validation of an Artificial-Intelligence-enabled Portable Colposcopy Device for Optimizing Triage Alternatives for HPV-based Cervical Cancer Screening
开发和验证人工智能便携式阴道镜设备,用于优化基于 HPV 的宫颈癌筛查的分诊方案
基本信息
- 批准号:10416639
- 负责人:
- 金额:$ 60.41万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-05-20 至 2027-04-30
- 项目状态:未结题
- 来源:
- 关键词:Acetic AcidsAddressAdoptedAffectAlgorithmsAmbulatory CareArtificial IntelligenceBiopsyCaringCause of DeathCellular PhoneCervicalCervical Cancer ScreeningCervix UteriCessation of lifeCharacteristicsClinicClinicalClinical ResearchColposcopesColposcopyComputer softwareCountryCoupledCytologyDataDatabasesDecision AidDevelopmentDevicesDiagnosisDiagnosticDiseaseEducational StatusEffectivenessEnsureEvaluationGenerationsGoalsGuidelinesHPV-High RiskHandHealthHealth care facilityHealthcareHemorrhageHuman PapillomavirusHuman ResourcesImageIncidenceIncomeInfertilityInfrastructureInterventionKenyaKnowledgeLettersLightLocationMalignant NeoplasmsMalignant neoplasm of cervix uteriMedical ResearchMethodsModelingNational Cancer InstitutePathologyPerformancePhysiciansPilot ProjectsPopulationPredictive ValuePrevention strategyProcessProviderPublic HealthROC CurveResearchResearch InstituteResolutionResourcesRetrievalRiskSensitivity and SpecificitySeriesSiteTestingTrainingTriageValidationVisitVisualWomanWorkWorld Health Organizationbaseburden of illnesscervical cancer preventionclinical research sitecomparative effectivenesscontrast imagingconvolutional neural networkcostdeep learningdeep learning algorithmdiagnostic accuracydiagnostic strategyexperiencefollow-upglobal healthimpressionimprovedinnovationlow and middle-income countriesmHealthmachine learning algorithmmortalityovertreatmentportabilityprospectiveprospective testprototypereal world applicationrisk prediction modelrisk stratificationscreeningstandard caresuccesstechnology developmenttoolvirtual
项目摘要
Abstract
Cervical cancer is the second leading cause of death for women worldwide. Alarmingly, 85% of deaths occur in
low and middle-income countries (LMICs), as they lack the health care infrastructure required for cytology-based
screening, referral colposcopy diagnosis, and expert physicians, which have dramatically reduced the disease
burden in high income countries (HICs). Highly sensitive human papillomavirus (HPV) testing has been effective
at reducing the incidence and mortality from cervical cancer when directly coupled with treatment; however, a
majority of women with HPV do not have cervical precancer, making HPV testing a poor triage test as
overtreatment carries risks like
hemorrhage and infertility.
Colposcopy followed by biopsy, the preferred triage
method in HICs, is untenable in most LMIC settings due to the cost of colposcopes and pathology facilities to
process and interpret biopsy results. To make matters worse, women are lost to follow up in LMIC settings when
a multi-visit model for cervical cancer screening is used. Visual Inspection with Acetic Acid (VIA), the World
Health Organization recommended triage test following HPV testing, has widely varied sensitivity and specificity
depending on the training level of the provider. In this proposal we are proposing a single visit model for precision
diagnosis and treatment in LMICs for cervical cancer prevention. Two major technological tools are needed to
implement this model: a low-cost method to perform imaging of the cervix and a machine learning algorithm to
automate diagnosis in the absence of a provider. We have previously developed the Pocket Colposcope, which
has shown high concordance with standard colposcopy at a fraction of the cost and validated it on thousands of
women across nearly every continent. We are now in the process of developing a state-of-the-art convolutional
neural network (CNN), called Colposcopy Automated Risk Evaluation (CARE), trained with Pocket colposcopy
images to automate the diagnostic process. Our current prototype algorithm has been highly successful at
classifying cervical pre-cancers from Pocket Colposcope images retrospectively. Our goals for this proposal are
fourfold: 1) improve and generalize the performance of Pocket CARE using >10,000 National Cancer Institute
(NCI) standard colposcopy images; 2) generate synthetic images to address domain shifts due to environmental
and personnel changes between different clinical sites; 3) embed the CARE algorithm into our existing software
to enable high quality image capture with the Pocket Colposcope for automated diagnosis 4) validate the
performance of Pocket CARE prospectively with a clinical study in Kisumu, Kenya, a site where Pocket CARE
would ultimately be adopted\. The deliverables for this proposal will be a fully validated Pocket CARE software
ready for scale to different clinical scenarios based on location-specific cultural contexts and infrastructure
and a comparative effectiveness of Pocket CARE to other publicly available algorithms and standard RI care.
摘要
宫颈癌是全球女性的第二大死因。令人震惊的是,85%的死亡发生在
低收入和中等收入国家(LMIC),因为它们缺乏以细胞学为基础的卫生保健基础设施
筛查、转诊阴道镜诊断和专家医生,极大地减少了疾病
高收入国家(HIC)的负担。高度敏感的人类乳头瘤病毒(HPV)检测有效
在直接配合治疗的情况下降低宫颈癌的发病率和死亡率;然而,
大多数感染HPV的妇女没有宫颈癌前病变,这使得HPV检测成为一种糟糕的分诊测试,因为
过度治疗有风险,比如
出血和不孕不育。
阴道镜检查后进行活检,这是首选的分诊方法
在HICS中,由于镜和病理设施的成本,在大多数LMIC环境中是不能成立的
处理和解释活组织检查结果。更糟糕的是,在LMIC环境下,女性无法跟进
宫颈癌筛查采用多次就诊模式。冰醋酸目视检查(VIA),世界
卫生组织推荐的HPV检测后的分流检测,其敏感性和特异性差异很大
取决于提供商的培训水平。在本提案中,我们提出了一种精确的单次访问模型
用于宫颈癌预防的低分子集成电路的诊断和治疗。需要两个主要的技术工具来实现
实现这个模型:一种低成本的宫颈成像方法和一个机器学习算法
在没有提供商的情况下自动诊断。我们之前已经开发了袖珍镜,它
显示出与标准阴道镜检查的高度一致性,而成本只有标准阴道镜检查的一小部分,并在数千个
几乎每个大洲的女性。我们现在正在开发一种最先进的卷积
神经网络(CNN),称为阴道镜自动风险评估(CARE),使用袖珍阴道镜进行训练
图像以自动执行诊断过程。我们目前的原型算法在以下方面非常成功
从袖珍镜图像对宫颈癌前病变进行回顾性分类。我们对这项提议的目标是
四个方面:1)使用>;10,000国家癌症研究所改善和推广袖珍护理的绩效
(NCI)标准阴道镜图像;2)生成合成图像以解决因环境原因引起的域移动
以及不同临床站点之间的人员变动;3)将护理算法嵌入我们现有的软件中
要使用袖珍镜实现高质量的图像捕获以进行自动诊断4)验证
在肯尼亚基苏木的一项临床研究中,袖珍护理的前瞻性表现
最终会被采纳。此建议书的交付内容将是经过充分验证的Pocket Care软件
可根据特定位置的文化背景和基础设施扩展到不同的临床方案
以及Pocket CARE与其他公开可用的算法和标准RI CARE的有效性的比较。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Elizabeth Anne BUKUSI其他文献
Elizabeth Anne BUKUSI的其他文献
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{{ truncateString('Elizabeth Anne BUKUSI', 18)}}的其他基金
Sustainable Development for Improved HIV Health and Prevention in Kenya (SD4H-Kenya)
肯尼亚改善艾滋病毒健康和预防的可持续发展(SD4H-肯尼亚)
- 批准号:
10872887 - 财政年份:2023
- 资助金额:
$ 60.41万 - 项目类别:
Simplifying PrEP delivery: One-stop service pathway to improve PrEP care efficiency and continuation in Kenya
简化 PrEP 交付:提高肯尼亚 PrEP 护理效率和持续性的一站式服务途径
- 批准号:
10547902 - 财政年份:2022
- 资助金额:
$ 60.41万 - 项目类别:
Simplifying PrEP delivery: One-stop service pathway to improve PrEP care efficiency and continuation in Kenya
简化 PrEP 交付:提高肯尼亚 PrEP 护理效率和持续性的一站式服务途径
- 批准号:
10688130 - 财政年份:2022
- 资助金额:
$ 60.41万 - 项目类别:
Enhancing PrEP outcomes among Kenyan adolescent girls and young women with a novel pharmacy-based PrEP delivery platform
通过基于药房的新型 PrEP 交付平台提高肯尼亚少女和年轻女性的 PrEP 效果
- 批准号:
10402054 - 财政年份:2021
- 资助金额:
$ 60.41万 - 项目类别:
Evaluating sexually transmitted infections among adolescent girls and young women within a pharmacy-based PrEP delivery model in Kenya.
在肯尼亚基于药房的 PrEP 交付模式中评估青春期女孩和年轻女性的性传播感染情况。
- 批准号:
10878139 - 财政年份:2021
- 资助金额:
$ 60.41万 - 项目类别:
SD4H Training Grant Supplement to Promote Diversity, Equity and Inclusion
SD4H 培训补助金补充,以促进多元化、公平和包容性
- 批准号:
10874195 - 财政年份:2020
- 资助金额:
$ 60.41万 - 项目类别:
Sustainable Development for Improved HIV Health and Prevention in Kenya (SD4H-Kenya)
肯尼亚改善艾滋病毒健康和预防的可持续发展(SD4H-肯尼亚)
- 批准号:
10348189 - 财政年份:2020
- 资助金额:
$ 60.41万 - 项目类别:
Sustainable Development for Improved HIV Health and Prevention in Kenya (SD4H-Kenya)
肯尼亚改善艾滋病毒健康和预防的可持续发展(SD4H-肯尼亚)
- 批准号:
10544044 - 财政年份:2020
- 资助金额:
$ 60.41万 - 项目类别:
Sustainable Development for Improved HIV Health and Prevention in Kenya (SD4H-Kenya)
肯尼亚改善艾滋病毒健康和预防的可持续发展(SD4H-肯尼亚)
- 批准号:
10254375 - 财政年份:2020
- 资助金额:
$ 60.41万 - 项目类别:
PrEP and dPEP: Doxycycline post-exposure prophylaxis for prevention of sexually transmitted infections among Kenyan women using HIV pre-exposure prophylaxis
PrEP 和 dPEP:强力霉素暴露后预防,用于使用 HIV 暴露前预防来预防肯尼亚妇女的性传播感染
- 批准号:
10223161 - 财政年份:2019
- 资助金额:
$ 60.41万 - 项目类别:
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