Point of Care Detection and Diagnosis of Oral Cancer using a Low Cost Imaging Module enabled by AI
Project Number1U01CA291666-01
Contact PI/Project LeaderGINTY, FIONA
Awardee OrganizationGE MEDICAL SYSTEMS INFORMATION TECHNOLOGIES, INC
Description
Abstract Text
PROJECT SUMMARY/ABSTRACT
The overall vision of the proposed project is to to develop and deploy an affordable automated point-of-care
(POC) telecytology platform for oral cancer screening that will reliably establish a diagnosis of oral cancer in the
community setting and establish an immediate referral care pathway. Oral cancer is a significant public health
problem in India; 77,000 new cases and 52,000 deaths are reported annually, which is approximately one-fourth
of global incidences. Approximately 70% of cases present at an advanced stage, when the probability of cure is
very low, and a five-year survival rate is around 20%. It has been estimated that early diagnosis, with timely and
proper treatment, could improve the survival rate up to 90%. The current ‘gold standard’ of oral cancer screening
is visual inspection of the mouth by trained individuals, followed by biopsy of suspicious lesions. However, in
India there is a delay of nine months from the onset of symptoms to diagnosis. Of this, seven months are
attributed to the delays within the medical pathway The majority of the population lives in a rural environment,
where access to pathology services and expertise is very limited. Without definitive proof of cancer, patients are
not eligible for state-run insurance programs for treatment. Our proposed approach comprises a portable system
for scanning brush biopsy cytology slides with cloud connectivity for transmission of images to pathologists
and/or automated diagnosis via a validated algorithm for identification of atypical cells. After standard visual
triaging of patients during routine screening, those identified with higher risk lesions will immediately be
directed to undergo brush biopsies on the same day. Samples will be placed on a glass slide, stained with routine
toluidine blue (average time is <4 minutes), and imaged using the portable slide scanner. Initially these images
will be relayed via cloud to a remote pathologist who will immediately report them, while subsequent versions of
the prototype will have in-built artificial intelligence (AI) algorithms for automated reporting in the field. We
believe that this innovative and affordable workflow would successfully expedite diagnosis and provide
significantly earlier treatment for oral cancer patients.
Public Health Relevance Statement
PROJECT NARRATIVE
We will develop and deploy an affordable automated point-of-care (POC) telecytology platform for oral cancer
screening in India that will reliably establish a diagnosis of oral cancer in the community setting and create an
immediate referral care pathway. This will save significant time in reaching a diagnosis (same day vs. ~7 months)
and improve outcomes via earlier patient treatment.
NIH Spending Category
No NIH Spending Category available.
Project Terms
AccreditationAlgorithmsAnnual ReportsArtificial IntelligenceBiopsyBiopsy SpecimenBrazilCancer CenterCancer HospitalCancer PatientCaringCell modelCellsCellular PhoneCessation of lifeChinaClinicalCommunitiesCommunity TrialCustomCytologyDataDevicesDiagnosisDiagnosticEarly DiagnosisEarly treatmentEconomicsEvaluationExpenditureGlassGoalsHeadHead and Neck CancerHead and neck structureHealthHospitalsImageIncidenceIndiaIndividualInsuranceLesionMalignant NeoplasmsMedicalNCI Center for Cancer ResearchNetwork-basedOncologyOncology GroupOpticsOralOral DiagnosisOral cavityOutcomePathologistPathologyPathway interactionsPatient TriagePatientsPerformancePopulationPredictive AnalyticsProbabilityPublic HealthReportingResearchResolutionRunningRuralRural CommunityRussiaSamplingScanningScienceScreening for Oral CancerSecureServicesSlideSouth AfricaStage at DiagnosisStainsSurvival RateSymptomsSystemTimeTolonium chlorideTrainingTriageValidationVisionVisualWorkartificial intelligence algorithmartificial intelligence modelartificial neural networkautomated algorithmcloud platformcommunity settingcostdesigndiagnostic platformdiagnostic toolfabricationhigh riskimprovedimproved outcomeinnovationmalignant mouth neoplasmmid-career facultymobile applicationmultidisciplinaryoutreachpatient screeningperformance testspoint of carepoint-of-care detectionpopulation basedportabilityprototyperisk stratificationroutine screeningrural environmentrural health clinicsample collectionscreeningscreening programsocialtertiary caretooltransmission processtreatment planningtreatment programvibration
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