Multimodal Imaging in Vitreo-retinal Surgery and Macular Dystrophies

Recruiting

• Conditions: Macular Holes; Macular Dystrophies; Epiretinal Membrane; Retinal Detachment

• Registration Number: NCT05747144
• Lead Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Brief 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

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.

Study Timeline

• Study Start: 2021-01-15
• Study First Submitted: 2023-02-17
• Study First Submitted QC: 2023-02-17
• Study First Posted: 2023-02-28
• Status Verified: 2024-02
• Last Update Submitted: 2024-02-10
• Last Update Posted: 2024-02-13
• Primary Completion: 2025-01-15
• Study Completion: 2025-01-16

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 100

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).

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Predictivity of morphological-functional radiomic data	3 years	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.

Secondary Outcome Measures

Name	Time	Method
Correlating with the age of patients	3 years	Identify predictive differences according to diagnosis and correlate them with the age of patients
Correlate with age of onset of disease	3 years	Identify predictive differences according to diagnosis and correlate them with the age of onset of disease
Identify predictive differences according to diagnosis	3 years	Subdivision into subgroups in order to identify predictive differences according to diagnosis

Trial Locations

Locations (1)

Prof. Stanislao Rizzo; 🇮🇹 Rome, Italy