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Macular HolesMICAINCT05747144

Multimodal Imaging in Vitreo-retinal Surgery and Macular Dystrophies

An observational study of Macular Holes and Epiretinal Membrane, run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS at 1 site in Italy.

Unknown status

Status not confirmed. The sponsor has not verified this record for over two years, so it may be out of date.

Official record
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Trial summary

The aim of the study is to identify morphological and functional biomarkers of post-operative recovery after vitreoretinal surgery, using decisional support systems (DSS), based on multimodal big-data analysis by means of machine learning techniques in daily clinical practice Read more about this study

Who is it for?
Ages 18 months and older
What is being tested?
Biometry, Retinography (Color) + Autofluorescence (AF) and OCT B-scan and OCT angiography (OCTA)
How many people
100 participants
Where
1 siteItaly
Study dates
15 Jan 2021to 16 Jan 2025
Phase
Observational
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 months and older
  • All sexes
  • No healthy volunteers
  • Child, Adult, Older adult

Study population: All patients to undergo vitreo-retinal surgery for macular disease (i.e.Macular hole, epiretinal membranes, Retinal detachment, Macular dystrophies (retinal pre-prosthesis).

Who may be able to join

  • All patients to undergo vitreo-retinal surgery for:
  • Macular hole
  • Epiretinal membranes
  • Retinal detachment
  • Macular dystrophies (retinal pre-prosthesis)

Who cannot join

  • Patients under 18 years of age will be excluded; patients in whom morphological examinations cannot be performed due to poor cooperation or opacity of the dioptric media (e.g. corneal pathology). Quality of morphological images inadequate for post acquisition processing (<6/10).

Trial locations and contacts

1 site in 1 country.

Maria Cristina Savastano, MD,PhD contact

+39 3384443002

mariacristina.savastano@unicatt.it

Alfonso Savastano, MD,PhD contact

alfonso.savastano@policlinicogemelli.it

Italy1 site, 1 recruiting

  • Prof. Stanislao Rizzo

    Rome, 00168

    Stanislao Rizzo, MD,PhD

    Recruiting

Other trials in Italy

About this study

Official title: Multimodal Imaging in Vitreo-retinal Surgery and Macular Dystrophies: Biomarkers of Morpho-Functional Recovery by Artificial Intelligence

The aim of the study is to identify morphological and functional biomarkers of post-operative recovery after vitreoretinal surgery, using decisional support systems (DSS), based on multimodal big-data analysis by means of machine learning techniques in daily clinical practice

Read the detailed description

The aim of the study is to identify morphological and functional biomarkers of post-operative recovery after vitreoretinal surgery. Identifying the biomarkers and assessing the predictivity of recovery will make it possible to highlight the categories of patients who can benefit most from surgical treatment, and to target the patient more precisely for personalised medicine and surgery. The introduction of new decisional support systems (DSS), based on multimodal big-data analysis through machine learning techniques in daily clinical practice, is providing new useful information in patient assessment for personalised surgery.

Being tested
Biometry, Retinography (Color) + Autofluorescence (AF), OCT B-scan and OCT angiography (OCTA), Microperimetry, Electrophysiological exams
Keywords
vitreoretinal surgery, machine learning, decisional support systems
Medical subject headings
Retinal Perforations, Epiretinal Membrane, Retinal Detachment, Macular Degeneration

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • BiometryDiagnostic test

    Biometric measurements performed with IOL Master, if executable Contact or immersion echobiometry if IOL Master cannot be performed

  • Retinography (Color) + Autofluorescence (AF)Diagnostic test

    Colour + AF: EIDON, if available (60° not modulable) Colour: COBRA (60° non-modifiable) AF: Spectralis-Heidelberg (choose 55°) Other if available (choose posterior pole examination between 50-60°)

  • OCT B-scan and OCT angiography (OCTA)Diagnostic test

    OCT B-scan:

    2 scans (6 mm)

    1 cross line

    OCTA:

    3x3 mm + 6x6 mm centred on the fovea 4.5 mm centred on the optic nerve

  • MicroperimetryDiagnostic test

    1. fixation pattern 2) retinal sensitivity map

  • Electrophysiological examsDiagnostic test

    Layer-by-layer assessment of the retina using focal ERG and pattern ERG according to standardised and published methods , For patients with visus < 3/10 and unstable fixation a protocol based on component analysis of the photopic ERG from diffuse flash will be used.

Study groups in NCT05747144
Study groupRoleWhat they receive
Macular holePatients affected by macular hole.
Receives: Diagnostic Test: Biometry; Diagnostic Test: Retinography (Color) + Autofluorescence (AF); Diagnostic Test: OCT B-scan and OCT angiography (OCTA); Diagnostic Test: Microperimetry; Diagnostic Test: Electrophysiological exams
Epiretinal membranesPatients affected by epiretinal membrane.
Receives: Diagnostic Test: Biometry; Diagnostic Test: Retinography (Color) + Autofluorescence (AF); Diagnostic Test: OCT B-scan and OCT angiography (OCTA); Diagnostic Test: Microperimetry; Diagnostic Test: Electrophysiological exams
Retinal detachmentPatients affected by retinal detachment.
Receives: Diagnostic Test: Biometry; Diagnostic Test: Retinography (Color) + Autofluorescence (AF); Diagnostic Test: OCT B-scan and OCT angiography (OCTA); Diagnostic Test: Microperimetry; Diagnostic Test: Electrophysiological exams
Macular dystrophiesPatients affected by macular dystrophies.
Receives: Diagnostic Test: Biometry; Diagnostic Test: Retinography (Color) + Autofluorescence (AF); Diagnostic Test: OCT B-scan and OCT angiography (OCTA); Diagnostic Test: Microperimetry; Diagnostic Test: Electrophysiological exams

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Predictivity of morphological-functional radiomic data

    Rate of predictivity of morphological-functional radiomic data to establish the grade of recovery in the post-operative period by means of an artificial intelligence (AI) machine learning model.

    Measured: 3 years

Secondary outcomes (3)

  1. Identify predictive differences according to diagnosis

    Subdivision into subgroups in order to identify predictive differences according to diagnosis

    Measured: 3 years

  2. Correlating with the age of patients

    Identify predictive differences according to diagnosis and correlate them with the age of patients

    Measured: 3 years

  3. Correlate with age of onset of disease

    Identify predictive differences according to diagnosis and correlate them with the age of onset of disease

    Measured: 3 years

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted28 Feb 2023
  2. Study start15 Jan 2021
  3. Primary completion15 Jan 2025 (estimated)
  4. Study completion16 Jan 2025 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Prospective
Enrolment
100 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT05747144
Sponsor's protocol ID
3680
Acronym
MICAI
Status last verified
Feb 2024
First submitted
17 Feb 2023
Last updated
13 Feb 2024
Results posted
No
Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No

Publications

  • Garrity ST, Sarraf D, Freund KB, Sadda SR. Multimodal Imaging of Nonneovascular Age-Related Macular Degeneration. Invest Ophthalmol Vis Sci. 2018 Mar 20;59(4):AMD48-AMD64. doi: 10.1167/iovs.18-24158.

    PubMed 30025107

  • Acon D, Wu L. Multimodal Imaging in Diabetic Macular Edema. Asia Pac J Ophthalmol (Phila). 2018 Jan-Feb;7(1):22-27. doi: 10.22608/APO.2017504. Epub 2017 Jan 29.

    PubMed 29376234

  • Duker JS, Kaiser PK, Binder S, de Smet MD, Gaudric A, Reichel E, Sadda SR, Sebag J, Spaide RF, Stalmans P. The International Vitreomacular Traction Study Group classification of vitreomacular adhesion, traction, and macular hole. Ophthalmology. 2013 Dec;120(12):2611-2619. doi: 10.1016/j.ophtha.2013.07.042. Epub 2013 Sep 17.

    PubMed 24053995

  • Spaide RF, Fujimoto JG, Waheed NK, Sadda SR, Staurenghi G. Optical coherence tomography angiography. Prog Retin Eye Res. 2018 May;64:1-55. doi: 10.1016/j.preteyeres.2017.11.003. Epub 2017 Dec 8.

    PubMed 29229445

  • Sun P, Tandias RM, Yu G, Arroyo JG. SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY FINDINGS AND VISUAL OUTCOME AFTER TREATMENT FOR VITREOMACULAR TRACTION. Retina. 2019 Jun;39(6):1054-1060. doi: 10.1097/IAE.0000000000002116.

    PubMed 29595569

  • Lee CS, Tyring AJ, Deruyter NP, Wu Y, Rokem A, Lee AY. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express. 2017 Jun 23;8(7):3440-3448. doi: 10.1364/BOE.8.003440. eCollection 2017 Jul 1.

    PubMed 28717579

  • Zur D, Iglicki M, Busch C, Invernizzi A, Mariussi M, Loewenstein A; International Retina Group. OCT Biomarkers as Functional Outcome Predictors in Diabetic Macular Edema Treated with Dexamethasone Implant. Ophthalmology. 2018 Feb;125(2):267-275. doi: 10.1016/j.ophtha.2017.08.031. Epub 2017 Sep 19.

    PubMed 28935399

  • Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA. 2018 Apr 3;319(13):1317-1318. doi: 10.1001/jama.2017.18391. No abstract available.

    PubMed 29532063

  • Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019 May;20(5):e262-e273. doi: 10.1016/S1470-2045(19)30149-4.

    PubMed 31044724

  • Abramoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M. Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning. Invest Ophthalmol Vis Sci. 2016 Oct 1;57(13):5200-5206. doi: 10.1167/iovs.16-19964.

    PubMed 27701631

  • Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.

    PubMed 27898976

  • Lakhani P, Sundaram B. Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. Radiology. 2017 Aug;284(2):574-582. doi: 10.1148/radiol.2017162326. Epub 2017 Apr 24.

    PubMed 28436741

  • Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115-118. doi: 10.1038/nature21056. Epub 2017 Jan 25.

    PubMed 28117445

  • Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, Hamzah H, Garcia-Franco R, San Yeo IY, Lee SY, Wong EYM, Sabanayagam C, Baskaran M, Ibrahim F, Tan NC, Finkelstein EA, Lamoureux EL, Wong IY, Bressler NM, Sivaprasad S, Varma R, Jonas JB, He MG, Cheng CY, Cheung GCM, Aung T, Hsu W, Lee ML, Wong TY. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017 Dec 12;318(22):2211-2223. doi: 10.1001/jama.2017.18152.

    PubMed 29234807

  • Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology. 2018 Aug;125(8):1199-1206. doi: 10.1016/j.ophtha.2018.01.023. Epub 2018 Mar 2.

    PubMed 29506863

Questions about this trial

What is this clinical trial studying?

An observational study of Macular Holes and Epiretinal Membrane, run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS at 1 site in Italy.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 5 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: status not confirmed, as of the sponsor's last update on 13 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

Results are not available in the source record.

How can a doctor refer a patient or contact the research team?

Contact Maria Cristina Savastano, MD,PhD (+39 3384443002, mariacristina.savastano@unicatt.it), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Predictivity of morphological-functional radiomic data (time frame: 3 years). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT05747144, which also lists 41 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Feb 2024.

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