Multimodal Imaging in Vitreo-retinal Surgery and Macular Dystrophies

Retinal Detachment Clinical Trial

— MICAI  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05747144
• Other study ID #: 3680
• Status: Recruiting
• Start date: January 15, 2021
• Est. completion date: January 16, 2025

Study information

• Verified date: February 2024
• Source: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
• Contact: Maria Cristina Savastano, MD,PhD
• Phone: +39 3384443002
• Email: mariacristina.savastano[@]unicatt.it
• Is FDA regulated: No
• Study type: Observational

Clinical 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

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100
• Est. completion date: January 16, 2025
• Est. primary completion date: January 15, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Months and older

Eligibility

Inclusion Criteria:

• All patients to undergo vitreo-retinal surgery for:

1. Macular hole

2. Epiretinal membranes

3. Retinal detachment

4. Macular dystrophies (retinal pre-prosthesis)

Exclusion Criteria:

• 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).

Related Conditions & MeSH terms

• Epiretinal Membrane
• Macular Degeneration
• Macular Holes
• Retinal Detachment
• Retinal Perforations

Intervention

Diagnostic Test:
• Biometry
Biometric measurements performed with IOL Master, if executable Contact or immersion echobiometry if IOL Master cannot be performed
• Retinography (Color) + Autofluorescence (AF)
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)
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
• Microperimetry
1) fixation pattern 2) retinal sensitivity map
• Electrophysiological exams
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.

Locations

Country	Name	City	State
Italy	Prof. Stanislao Rizzo	Rome

Sponsors (1)

Lead Sponsor	Collaborator
Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Country where clinical trial is conducted

Italy, 

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* Note: There are 40 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	3 years
Secondary	Identify predictive differences according to diagnosis	Subdivision into subgroups in order to identify predictive differences according to diagnosis	3 years
Secondary	Correlating with the age of patients	Identify predictive differences according to diagnosis and correlate them with the age of patients	3 years
Secondary	Correlate with age of onset of disease	Identify predictive differences according to diagnosis and correlate them with the age of onset of disease	3 years