Intraoperative integration of artificial intelligence during cystoscopic surgery
膀胱镜手术中人工智能的术中整合
基本信息
- 批准号:10365872
- 负责人:
- 金额:$ 51.4万
- 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:2022-01-01 至 2026-12-31
- 项目状态:未结题
- 来源:
- 关键词:AffectAlgorithmsAppearanceArtificial IntelligenceBase SequenceBenignBladderBladder NeoplasmCancer DetectionCancer DiagnosticsCancer PatientCancerousCessation of lifeClinicClinicalCommunitiesComplexComputer Vision SystemsCystoscopyDataData SetDetectionDiagnosisDiagnosticEnsureEnvironmentEquipmentExcisionFutureGoalsHealthcareHistologicHospitalsHumanImageImage AnalysisInflammatoryInterventionKnowledgeLearningLesionLightMalignant NeoplasmsMalignant neoplasm of urinary bladderMedical ImagingMedical centerModelingMorbidity - disease rateMorphologyOperating RoomsOperative Surgical ProceduresOutcomePapillaryPathologicPatientsPerformancePhysician AssistantsPredictive ValueProcessProtocols documentationProviderRecurrenceResearchRoleSensitivity and SpecificitySiteStagingStandardizationSurgeonTechnologyTestingThe Cancer Imaging ArchiveTimeTrainingTranslatingTransurethral ResectionUnited StatesUniversitiesUrologistUrologyValidationWashingtonWorkannotation systemaugmented intelligenceautomated segmentationbasebody systemcancer diagnosiscancer imagingcancer recurrencecancer riskcancer surgerycloud basedconvolutional neural networkcost effectivedeep learning algorithmdeep neural networkdemographicsdesignexperiencehigh riskimage guidedimage processingimprovedimproved outcomeindexingmillisecondmortalitymultidisciplinarynovelpatient stratificationprospectiverecruitrecurrent neural networkrelating to nervous systemrisk stratificationscreeningsegmentation algorithmtooltumortumor progression
项目摘要
PROJECT SUMMARY
Bladder cancer is the sixth most common cancer in the U.S., has one of the highest recurrence rates of all
cancers, and is the most expensive cancer to treat from diagnosis to death. Current standard for bladder
cancer diagnosis relies on clinic-based white light cystoscopy for initial screening, followed by transurethral
resection of bladder tumor in the operating room for pathologic diagnosis and local staging. White light
cystoscopy has several well recognized shortcomings, particularly incomplete detection, thereby leading to
suboptimal resection and contributing to cancer recurrence and progression. Our goal is to improve outcomes
for bladder cancer patients through integration of a deep learning algorithm to improve cystoscopic detection
and enhance surgical resection.
Artificial intelligence (AI)-based on deep neural networks have demonstrated remarkable capacity to learn
complex relationships and incorporate existing knowledge into the inference model. We hypothesize that AI-
augmented detection of bladder tumor will improve diagnostic cystoscopy in the clinic setting to identify
suspicious lesions and improve the quality of transurethral resection in the operating room, thereby reducing
overall cancer recurrence and outcome. Towards the goal of establishing a paradigm of AI-based framework
for augmented detection of bladder cancer, we will leverage our strong preliminary data and outstanding
environment in AI research. We propose three specific aims: 1) To curate a high-quality annotated cystoscopy
imaging dataset to optimize deep neural network CystoNet; 2) To design and optimize CystoNet for real-time
cystoscopic navigation and cancer detection; and 3) To conduct a prospective multicenter validation of
CystoNet during bladder cancer surgery.
Successful completion of the studies proposed here will serve to translate deep learning algorithm to the
dynamic environment of cystoscopic surgery without the need for specialized instrumentaitons. We foresee
our approach will improve the outcome of a major cancer and genearlizable to other organ systems amenable
for endsocopic interventions.
项目总结
项目成果
期刊论文数量(0)
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{{ truncateString('JOSEPH C LIAO', 18)}}的其他基金
Intraoperative integration of artificial intelligence during cystoscopic surgery
膀胱镜手术中人工智能的术中整合
- 批准号:
10544344 - 财政年份:2022
- 资助金额:
$ 51.4万 - 项目类别:
BCCMA: Basic and Translational Mechanisms of Cancer Initiation of the Urothelium in Veterans Exposed to Carcinogens: Leveraging Artificial Neural Networks to Enhance Detection of Carcinoma in situ
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- 批准号:
10260145 - 财政年份:2021
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$ 51.4万 - 项目类别:
MagSToNE - a magnetic system for kidney stone fragment elimination
MagSToNE - 用于消除肾结石碎片的磁性系统
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10354258 - 财政年份:2021
- 资助金额:
$ 51.4万 - 项目类别:
MagSToNE - a magnetic system for kidney stone fragment elimination
MagSToNE - 用于消除肾结石碎片的磁性系统
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BCCMA: Basic and Translational Mechanisms of Cancer Initiation of the Urothelium in Veterans Exposed to Carcinogens: Leveraging Artificial Neural Networks to Enhance Detection of Carcinoma in situ
BCCMA:暴露于致癌物的退伍军人尿路上皮癌症发生的基本和转化机制:利用人工神经网络增强原位癌的检测
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10513315 - 财政年份:2021
- 资助金额:
$ 51.4万 - 项目类别:
Personalized assessment of bladder cancer treatment response using urinary molecular biomarkers
使用尿液分子生物标志物对膀胱癌治疗反应进行个性化评估
- 批准号:
10514572 - 财政年份:2020
- 资助金额:
$ 51.4万 - 项目类别:
Personalized assessment of bladder cancer treatment response using urinary molecular biomarkers
使用尿液分子生物标志物对膀胱癌治疗反应进行个性化评估
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10293596 - 财政年份:2020
- 资助金额:
$ 51.4万 - 项目类别:
Personalized assessment of bladder cancer treatment response using urinary molecular biomarkers
使用尿液分子生物标志物对膀胱癌治疗反应进行个性化评估
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10015540 - 财政年份:2020
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9038992 - 财政年份:2015
- 资助金额:
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