This study will further assess ERG components obtained with different ERG devices, to be considered in a prediction model for each diagnosis. The prediction models are diaMentis proprietary software used as an ERG-based diagnostic test (classified as a Software as Medical Device, SaMD) to support the diagnosis of schizophrenia and bipolar disorder type I. They involve the processing and analysis of specific retinal biosignatures (RSPA) with the support of statistical and mathematical modelling processes e.g. machine learning and statistical learning.
The technology under development by diaMentis is defined as a Software as a Medical Device (SaMD); it will be used in combination with an electroretinogram (ERG). This study will be performed using three different ERG devices, currently marketed and cleared by the health authorities (Espion, UTAS and RETeval) to support the analytical, scientific and performance validity of the SaMD. Anomalies detected by ERG provide an objective measure that may reflect specific underlying dysfunctions in patients and thus hold promise to confirm relevant biosignatures in psychiatric disorders. Significant differences between patients with SZ, BPI and control subjects have been found despite confounding factors; this trial is required to better define the impact of patient characteristics on ERG features with a potential to refine the interpretation of results. This is a multicenter study. Three hundred subjects will be enrolled into three groups: 100 SZ patients, 100 BPI patients and 100 control subjects (healthy volunteers). The primary objective is to further characterize the ERG components in SZ and BPI patients in order to develop prediction models that discriminate each pathology. The secondary objectives are the evaluation of the repeatability and reproducibility of the analysis of the ERG components in control subjects, the assessment of the reliability of ERG prediction score for patients following a repeat test, and the evaluation of the impact of different ERG devices on the data generated and the prediction models.
Study Type
OBSERVATIONAL
Enrollment
300
Processing and analysis of retinal signals
Collaborative Neuroscience Research LLC
Garden Grove, California, United States
Synergy San Diego
Differences in ERG components vs control ERG with full-field ERG stimulation conditions.
ERG components are retinal signal features (signal amplitude vs time) in the electrical signal recorded up to 100 msec post stimulation.
Time frame: Three ERG assessments within 6 weeks.
Differences in ERG components vs control ERG with Photopic Negative Response (PhNR) ERG stimulation conditions.
ERG components are retinal signal features (signal amplitude vs time) in the electrical signal recorded up to 250 msec post stimulation.
Time frame: Three ERG assessments within 6 weeks.
Differences in ERG components vs control ERG with On-Off ERG stimulation conditions.
ERG components are retinal signal features (signal amplitude vs time) in the electrical signal recorded up to 300 msec post stimulation.
Time frame: Three ERG assessments within 6 weeks.
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Lemon Grove, California, United States
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