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
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 Type
OBSERVATIONAL
Enrollment
100
Biometric measurements performed with IOL Master, if executable Contact or immersion echobiometry if IOL Master cannot be performed
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: 2 scans (6 mm) 1 cross line OCTA: 3x3 mm + 6x6 mm centred on the fovea 4.5 mm centred on the optic nerve
Prof. Stanislao Rizzo
Rome, Italy
RECRUITINGPredictivity 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.
Time frame: 3 years
Identify predictive differences according to diagnosis
Subdivision into subgroups in order to identify predictive differences according to diagnosis
Time frame: 3 years
Correlating with the age of patients
Identify predictive differences according to diagnosis and correlate them with the age of patients
Time frame: 3 years
Correlate with age of onset of disease
Identify predictive differences according to diagnosis and correlate them with the age of onset of disease
Time frame: 3 years
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1\) fixation pattern 2) retinal sensitivity map
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.