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A-EYE: A Mixed Quantitative and Qualitative Study to Develop and Evaluate the Application of Artificial Intelligence (AI) Methods Using Retinal Imaging for the Identification of Adverse Retinal Changes Associated With Cancer Therapies. (A-EYE)

8. november 2022 opdateret af: Tariq Aslam, University of Manchester

This is a data collection study involving the gathering of clinical data and OCT (optical coherence tomography) scans from 350 patients.

The purpose of this study is to gather data to help develop an AI algorithm to detect eye abnormalities specifically those related to certain cancer treatments.

At the end of the study interviews will be held with expert ophthalmologists to assess the acceptability of implementing AI into clinical practice.

Studieoversigt

Status

Rekruttering

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

Many cancer patients will access new treatments through clinical trials. These treatments have often never been tested in humans and therefore, are likely to have unknown side effects. Some of these side effects include changes to the eye, such as blindness.

Ahead of patients taking part in these trials there is often little planning done to manage potential side effects on the eye. Additionally, accessing the expertise of eye specialists is not always available and often referral to a specialist is only given when eye symptoms have become advanced. These delays in identifying side effects on the eye also delays treatment and follow-up management. Providing patients access to this expertise would help in the detection and management of treatment side effects, however, due to demands on resources this access is not always readily available.

The aim of this study is to create an artificial intelligence (AI) program that can detect changes to the eye related to disease, which, in the future, can be specifically used in cancer patient care. Additionally, developing an AI program to detect cancer related side effects to the eye will go a significant way in easing the burden on the health care system and improve side effects from new cancer treatments.

This study will involve the collection of eye scans and medical data from participants at the Manchester Royal Eye Hospital. These will then be used to develop AI methods to detect changes in the eye related to those seen by patients on cancer treatment. The AI will then be compared with the assessments of eye specialists to assess if they give similar results.

Undersøgelsestype

Observationel

Tilmelding (Forventet)

350

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

Studiesteder

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

18 år og ældre (Voksen, Ældre voksen)

Tager imod sunde frivillige

Ingen

Køn, der er berettiget til at studere

Alle

Prøveudtagningsmetode

Sandsynlighedsprøve

Studiebefolkning

Participants will be patients at the Manchester Royal Eye Hospital who meet the eligibility criteria.

Beskrivelse

Inclusion Criteria:

Patients are eligible for the study if all inclusion criteria are met:

  1. Voluntary informed consent.
  2. Aged at least 18 years.
  3. Fully registered patient attending the Manchester Royal Eye Hospital
  4. Patients are having an optical diagnostic imaging as part of their standard of care.

Exclusion Criteria:

Patients are excluded from the study if any of the following criteria apply:

1. Patient who are deemed clinically unable to be scanned by healthcare professional.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Tidsramme
Measure of the diagnostic accuracy of the AI algorithm against gold standard clinical assessment associated with cancer treatment.
Tidsramme: 12 months
12 months

Sekundære resultatmål

Resultatmål
Tidsramme
Sensitivity of the AI in identifying clinically relevant lesions as defined by an ophthalmologist. Specificity of the AI in identifying clinically relevant lesions as defined by an ophthalmologist.
Tidsramme: 12 months
12 months

Andre resultatmål

Resultatmål
Tidsramme
F1 score of the proposed algorithm compared against baseline algorithms.
Tidsramme: 13 months
13 months
Recorded questionnaire/ interview with ophthalmologist and cancer specialists.
Tidsramme: 9 months
9 months
Number of novel relationships identified
Tidsramme: 12 months
12 months

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

University of Manchester

Samarbejdspartnere

Institute of Cancer Research, United Kingdom

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

18. juni 2021

Primær færdiggørelse (Forventet)

31. december 2022

Studieafslutning (Forventet)

31. december 2022

Datoer for studieregistrering

Først indsendt

17. maj 2021

Først indsendt, der opfyldte QC-kriterier

24. maj 2021

Først opslået (Faktiske)

25. maj 2021

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

9. november 2022

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

8. november 2022

Sidst verificeret

1. november 2022

Mere information

Begreber relateret til denne undersøgelse

Andre undersøgelses-id-numre

  • NHS001768

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

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Disse oplysninger blev hentet direkte fra webstedet clinicaltrials.gov uden ændringer. Hvis du har nogen anmodninger om at ændre, fjerne eller opdatere dine undersøgelsesoplysninger, bedes du kontakte register@clinicaltrials.gov. Så snart en ændring er implementeret på clinicaltrials.gov, vil denne også blive opdateret automatisk på vores hjemmeside .

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